diff --git a/.github/workflows/docker-push.yml b/.github/workflows/docker-push-main.yml
similarity index 98%
rename from .github/workflows/docker-push.yml
rename to .github/workflows/docker-push-main.yml
index ae1cbfe8..8e0090e7 100644
--- a/.github/workflows/docker-push.yml
+++ b/.github/workflows/docker-push-main.yml
@@ -1,4 +1,4 @@
-name: Publish Docker image
+name: Publish docker image (latest)
 
 env:
   DOCKER_IMAGE: katanemo/archgw
diff --git a/.github/workflows/docker-push-release.yml b/.github/workflows/docker-push-release.yml
new file mode 100644
index 00000000..6d9cf294
--- /dev/null
+++ b/.github/workflows/docker-push-release.yml
@@ -0,0 +1,87 @@
+name: Publish docker image (release)
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  # Build ARM64 image on native ARM64 runner
+  build-arm64:
+    runs-on: [linux-arm64]
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.DOCKER_IMAGE }}
+
+      - name: Build and Push ARM64 Image
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: ./arch/Dockerfile
+          platforms: linux/arm64
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}-arm64
+
+  # Build AMD64 image on GitHub's AMD64 runner
+  build-amd64:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.DOCKER_IMAGE }}
+
+      - name: Build and Push AMD64 Image
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: ./arch/Dockerfile
+          platforms: linux/amd64
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}-amd64
+
+
+  # Combine ARM64 and AMD64 images into a multi-arch manifest
+  create-manifest:
+    runs-on: ubuntu-latest
+    needs: [build-arm64, build-amd64]  # Wait for both builds
+    steps:
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.DOCKER_IMAGE }}
+
+      - name: Create Multi-Arch Manifest
+        run: |
+          # Combine the architecture-specific images into a single manifest
+          docker buildx imagetools create -t ${{ steps.meta.outputs.tags }} \
+            ${{ env.DOCKER_IMAGE }}:arm64 \
+            ${{ env.DOCKER_IMAGE }}:amd64
diff --git a/.github/workflows/e2e_archgw.yml b/.github/workflows/e2e_archgw.yml
index 84ccff68..fdc43726 100644
--- a/.github/workflows/e2e_archgw.yml
+++ b/.github/workflows/e2e_archgw.yml
@@ -30,6 +30,7 @@ jobs:
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
         run: |
           docker compose up | tee &> archgw.logs &
 
@@ -55,5 +56,6 @@ jobs:
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
         run: |
           docker compose down
diff --git a/.github/workflows/e2e_test_demos.yml b/.github/workflows/e2e_test_demos.yml
index 0dfe3dd7..d353fa46 100644
--- a/.github/workflows/e2e_test_demos.yml
+++ b/.github/workflows/e2e_test_demos.yml
@@ -32,6 +32,11 @@ jobs:
         run: |
           python -m venv venv
 
+      - name: install hurl
+        run: |
+          curl --location --remote-name https://github.com/Orange-OpenSource/hurl/releases/download/4.0.0/hurl_4.0.0_amd64.deb
+          sudo dpkg -i hurl_4.0.0_amd64.deb
+
       - name: install model server, arch gateway and test dependencies
         run: |
           source venv/bin/activate
@@ -43,6 +48,7 @@ jobs:
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
         run: |
           source venv/bin/activate
           cd demos/shared/test_runner && sh run_demo_tests.sh
diff --git a/.github/workflows/e2e_tests.yml b/.github/workflows/e2e_tests.yml
index f894b713..576a7fc3 100644
--- a/.github/workflows/e2e_tests.yml
+++ b/.github/workflows/e2e_tests.yml
@@ -29,6 +29,7 @@ jobs:
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
         run: |
           python -mvenv venv
           source venv/bin/activate && cd tests/e2e && bash run_e2e_tests.sh
diff --git a/.github/workflows/validate_arch_config.yml b/.github/workflows/validate_arch_config.yml
new file mode 100644
index 00000000..9503dad2
--- /dev/null
+++ b/.github/workflows/validate_arch_config.yml
@@ -0,0 +1,31 @@
+name: arch config tests
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+jobs:
+  validate_arch_config:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: .
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.12"
+
+      - name: build arch docker image
+        run: |
+          docker build  -f arch/Dockerfile . -t katanemo/archgw
+
+      - name: validate arch config
+        run: |
+          bash arch/validate_arch_config.sh
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 92f3fc17..8f2525a2 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,4 +1,5 @@
-#  Contribution
+# Contribution
+
 We would love feedback on our [Roadmap](https://github.com/orgs/katanemo/projects/1) and we welcome contributions to **Arch**!
 Whether you're fixing bugs, adding new features, improving documentation, or creating tutorials, your help is much appreciated.
 
@@ -22,7 +23,9 @@ $ cd arch
 ```
 
 ### 3. Create a branch
+
 Use a descriptive name for your branch (e.g., fix-bug-123, add-feature-x).
+
 ```bash
 $ git checkout -b <your-branch-name>
 ```
@@ -32,6 +35,7 @@ $ git checkout -b <your-branch-name>
 Make your changes in the relevant files. If you're adding new features or fixing bugs, please include tests where applicable.
 
 ### 5. Test your changes
+
 ```bash
 cd arch
 cargo test
@@ -51,4 +55,4 @@ Contribution Guidelines
     Follow the existing coding style.
     Update documentation as needed.
 
-To get in touch with us, please join our [discord server](https://discord.gg/rbjqVbpa). We will be monitoring that actively and offering support there.
+To get in touch with us, please join our [discord server](https://discord.gg/pGZf2gcwEc). We will be monitoring that actively and offering support there.
diff --git a/README.md b/README.md
index e3a6a8ba..889b8a5d 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,9 @@
 </div>
 <div align="center">
 
-_Arch is an intelligent (edge and LLM) proxy designed for agentic applications - to help you protect, observe, and build agentic tasks by simply connecting (existing) APIs._
+
+_The intelligent (edge and LLM) proxy server for agentic applications._<br><br>
+Move faster by letting Arch handle the **pesky** heavy lifting in building agents: fast input clarification, agent routing, seamless integration of prompts with tools for common tasks, and unified access and observability of LLMs.
 
 [Quickstart](#Quickstart) •
 [Demos](#Demos) •
@@ -16,26 +18,32 @@ _Arch is an intelligent (edge and LLM) proxy designed for agentic applications -
 [![rust tests (prompt and llm gateway)](https://github.com/katanemo/arch/actions/workflows/rust_tests.yml/badge.svg)](https://github.com/katanemo/arch/actions/workflows/rust_tests.yml)
 [![e2e tests](https://github.com/katanemo/arch/actions/workflows/e2e_tests.yml/badge.svg)](https://github.com/katanemo/arch/actions/workflows/e2e_tests.yml)
 [![Build and Deploy Documentation](https://github.com/katanemo/arch/actions/workflows/static.yml/badge.svg)](https://github.com/katanemo/arch/actions/workflows/static.yml)
+
+
 </div>
 
 # Overview
-<a href="https://www.producthunt.com/posts/arch-3?embed=true&utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-arch&#0045;3" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=565761&theme=light&period=daily" alt="Arch - Build&#0032;fast&#0044;&#0032;hyper&#0045;personalized&#0032;agents&#0032;with&#0032;intelligent&#0032;infra | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/posts/arch-3?embed=true&utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-arch&#0045;3" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=565761&theme=dark&period=daily&t=1742359429995" alt="Arch - Build&#0032;fast&#0044;&#0032;hyper&#0045;personalized&#0032;agents&#0032;with&#0032;intelligent&#0032;infra | Product Hunt" style="width: 188px; height: 41px;" width="188" height="41" /></a>
 
+Past the thrill of an AI demo, have you found yourself hitting these walls? You know, the all too familiar ones:
 
-Arch Gateway was built by the contributors of [Envoy Proxy](https://www.envoyproxy.io/) with the belief that:
+- You go from one BIG prompt to specialized prompts, but get stuck building **routing and handoff** code?
+- You want use new LLMs, but struggle to **quickly and safely add LLMs** without writing integration code?
+- You're bogged down with prompt engineering just to **clarify user intent and validate inputs** effectively?
+- You're wasting cycles choosing and integrating code for **observability** instead of it happening transparently?
 
->Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – outside core business logic.*
+And you think to yourself, can't I move faster by focusing on higher-level objectives in a language/framework agnostic way? Well, you can! **Arch Gateway** was built by the contributors of [Envoy Proxy](https://www.envoyproxy.io/) with the belief that:
 
-
-Arch is engineered with purpose-built LLMs to handle critical but pesky tasks related to the handling and processing of prompts. This includes detecting and rejecting [jailbreak](https://github.com/verazuo/jailbreak_llms) attempts, intent-based routing for improved task accuracy, mapping user request into "backend" functions, and managing the observability of prompts and LLM API calls in a centralized way.
+>Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems to improve speed and accuracy for common agentic scenarios  – all outside core application logic.*
 
 **Core Features**:
 
-  - **Intent-based prompt routing & fast ⚡ function-calling via APIs**. Engineered with purpose-built [LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling, and parameter extraction from prompts to build more task-accurate agentic applications.
-  - **Prompt [Guard](https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d)**: Arch centralizes guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code.
-  - **LLM Routing & Traffic Management**: Arch centralizes calls to LLMs used by your applications, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability.
-  - **Observability**: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance.
-  - **Built on [Envoy](https://envoyproxy.io)**: Arch runs alongside application servers as a separate containerized process, and builds on top of Envoy's proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs.
+  - `🚦 Routing`. Engineered with purpose-built [LLMs](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) for fast (<100ms) agent routing and hand-off scenarios
+  - `⚡ Tools Use`: For common agentic scenarios let Arch instantly clarify and convert prompts to tools/API calls
+  - `⛨ Guardrails`: Centrally configure and prevent harmful outcomes and ensure safe user interactions
+  - `🔗 Access to LLMs`: Centralize access and traffic to LLMs with smart retries for continuous availability
+  - `🕵 Observability`: W3C compatible request tracing and LLM metrics that instantly plugin with popular tools
+  - `🧱 Built on Envoy`: Arch runs alongside app servers as a containerized process, and builds on top of [Envoy's](https://envoyproxy.io) proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs.
 
 **High-Level Sequence Diagram**:
 ![alt text](docs/source/_static/img/arch_network_diagram_high_level.png)
@@ -73,7 +81,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
 ```console
 $ python -m venv venv
 $ source venv/bin/activate   # On Windows, use: venv\Scripts\activate
-$ pip install archgw==0.2.1
+$ pip install archgw==0.2.6
 ```
 
 ### Build AI Agent with Arch Gateway
@@ -143,7 +151,7 @@ $ archgw up arch_config.yaml
 2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.1.5
 ...
 2024-12-05 16:56:28,485 - cli.utils - INFO - Schema validation successful!
-2024-12-05 16:56:28,485 - cli.main - INFO - Starging arch model server and arch gateway
+2024-12-05 16:56:28,485 - cli.main - INFO - Starting arch model server and arch gateway
 ...
 2024-12-05 16:56:51,647 - cli.core - INFO - Container is healthy!
 
@@ -241,7 +249,7 @@ client = OpenAI(
 
 response = client.chat.completions.create(
     # we select model from arch_config file
-    model="--",
+    model="None",
     messages=[{"role": "user", "content": "What is the capital of France?"}],
 )
 
@@ -301,6 +309,33 @@ Arch is designed to support best-in class observability by supporting open stand
 
 ![alt text](docs/source/_static/img/tracing.png)
 
+## Debugging
+
+When debugging issues / errors application logs and access logs provide key information to give you more context on whats going on with the system. Arch gateway runs in info log level and following is a typical output you could see in a typical interaction between developer and arch gateway,
+
+```
+$ archgw up --service archgw --foreground
+...
+[2025-03-26 18:32:01.350][26][info] prompt_gateway: on_http_request_body: sending request to model server
+[2025-03-26 18:32:01.851][26][info] prompt_gateway: on_http_call_response: model server response received
+[2025-03-26 18:32:01.852][26][info] prompt_gateway: on_http_call_response: dispatching api call to developer endpoint: weather_forecast_service, path: /weather, method: POST
+[2025-03-26 18:32:01.882][26][info] prompt_gateway: on_http_call_response: developer api call response received: status code: 200
+[2025-03-26 18:32:01.882][26][info] prompt_gateway: on_http_call_response: sending request to upstream llm
+[2025-03-26 18:32:01.883][26][info] llm_gateway: on_http_request_body: provider: gpt-4o-mini, model requested: None, model selected: gpt-4o-mini
+[2025-03-26 18:32:02.818][26][info] llm_gateway: on_http_response_body: time to first token: 1468ms
+[2025-03-26 18:32:04.532][26][info] llm_gateway: on_http_response_body: request latency: 3183ms
+...
+```
+
+Log level can be changed to debug to get more details. To enable debug logs edit (Dockerfile)[arch/Dockerfile], change the log level `--component-log-level wasm:info` to `--component-log-level wasm:debug`. And after that you need to rebuild docker image and restart the arch gateway using following set of commands,
+
+```
+# make sure you are at the root of the repo
+$ archgw build
+# go to your service that has arch_config.yaml file and issue following command,
+$ archgw up --service archgw --foreground
+```
+
 ## Contribution
 We would love feedback on our [Roadmap](https://github.com/orgs/katanemo/projects/1) and we welcome contributions to **Arch**!
 Whether you're fixing bugs, adding new features, improving documentation, or creating tutorials, your help is much appreciated.
diff --git a/arch/Dockerfile b/arch/Dockerfile
index 2c3c8744..7f933da5 100644
--- a/arch/Dockerfile
+++ b/arch/Dockerfile
@@ -28,4 +28,5 @@ COPY arch/arch_config_schema.yaml .
 RUN pip install requests
 RUN touch /var/log/envoy.log
 
-ENTRYPOINT ["sh","-c", "python config_generator.py && envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && envoy -c /etc/envoy.env_sub.yaml --component-log-level wasm:debug 2>&1 | tee /var/log/envoy.log"]
+# ENTRYPOINT ["sh","-c", "python config_generator.py && envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && envoy -c /etc/envoy.env_sub.yaml --log-level trace 2>&1 | tee /var/log/envoy.log"]
+ENTRYPOINT ["sh","-c", "python config_generator.py && envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && envoy -c /etc/envoy.env_sub.yaml --component-log-level wasm:info 2>&1 | tee /var/log/envoy.log"]
diff --git a/arch/arch_config_schema.yaml b/arch/arch_config_schema.yaml
index d9c9349e..59276589 100644
--- a/arch/arch_config_schema.yaml
+++ b/arch/arch_config_schema.yaml
@@ -5,8 +5,9 @@ properties:
     type: string
   listeners:
     type: object
+    additionalProperties: false
     properties:
-      prompt_gateway:
+      ingress_traffic:
         type: object
         properties:
           address:
@@ -20,7 +21,7 @@ properties:
           timeout:
             type: string
         additionalProperties: false
-      llm_gateway:
+      egress_traffic:
         type: object
         properties:
           address:
@@ -31,7 +32,6 @@ properties:
             type: string
             enum:
               - openai
-              - huggingface
           timeout:
             type: string
         additionalProperties: false
@@ -62,7 +62,7 @@ properties:
       properties:
         name:
           type: string
-        # this field is deprecated, use provider_interface instead
+        # provider field is deprecated, use provider_interface instead
         provider:
           type: string
           enum:
@@ -78,8 +78,11 @@ properties:
           type: string
         default:
           type: boolean
+        # endpoint field is deprecated, use base_url instead
         endpoint:
           type: string
+        base_url:
+          type: string
         protocol:
           type: string
           enum:
@@ -90,7 +93,6 @@ properties:
       additionalProperties: false
       required:
         - name
-        - model
   overrides:
     type: object
     properties:
@@ -98,6 +100,8 @@ properties:
         type: number
       optimize_context_window:
         type: boolean
+      use_agent_orchestrator:
+        type: boolean
   system_prompt:
     type: string
   prompt_targets:
@@ -124,7 +128,10 @@ properties:
               required:
                 type: boolean
               default:
-                type: string
+                anyOf:
+                  - type: string
+                  - type: integer
+                  - type: boolean
               description:
                 type: string
               type:
@@ -132,7 +139,10 @@ properties:
               enum:
                 type: array
                 items:
-                  type: string
+                  anyOf:
+                    - type: string
+                    - type: integer
+                    - type: boolean
               in_path:
                 type: boolean
               format:
@@ -241,5 +251,4 @@ properties:
 additionalProperties: false
 required:
   - version
-  - listeners
   - llm_providers
diff --git a/arch/envoy.template.yaml b/arch/envoy.template.yaml
index 313ece97..cac17187 100644
--- a/arch/envoy.template.yaml
+++ b/arch/envoy.template.yaml
@@ -29,7 +29,7 @@ stats_config:
       - 180000
 static_resources:
   listeners:
-    - name: arch_listener_http
+    - name: ingress_traffic
       address:
         socket_address:
           address: {{ prompt_gateway_listener.address }}
@@ -55,7 +55,7 @@ static_resources:
                   random_sampling:
                     value: {{ arch_tracing.random_sampling }}
                 {% endif %}
-                stat_prefix: arch_listener_http
+                stat_prefix: ingress_traffic
                 codec_type: AUTO
                 scheme_header_transformation:
                   scheme_to_overwrite: https
@@ -82,7 +82,7 @@ static_resources:
                     typed_config:
                       "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
 
-    - name: arch_prompt_gateway_listener
+    - name: ingress_traffic_prompt
       address:
         socket_address:
           address: 0.0.0.0
@@ -104,11 +104,11 @@ static_resources:
                         envoy_grpc:
                           cluster_name: opentelemetry_collector
                         timeout: 0.250s
-                      service_name: prompt_processor
+                      service_name: ingress_traffic
                   random_sampling:
                     value: {{ arch_tracing.random_sampling }}
                 {% endif %}
-                stat_prefix: arch_prompt_gateway_listener
+                stat_prefix: ingress_traffic
                 codec_type: AUTO
                 scheme_header_transformation:
                   scheme_to_overwrite: https
@@ -142,6 +142,19 @@ static_resources:
                             cluster: {{ llm_cluster_name }}
                             timeout: 60s
                       {% endfor %}
+
+                      {% if agent_orchestrator %}
+                        - match:
+                            prefix: "/"
+                            headers:
+                              - name: "x-arch-llm-provider"
+                                string_match:
+                                  exact: {{ agent_orchestrator }}
+                          route:
+                            auto_host_rewrite: true
+                            cluster: {{ agent_orchestrator }}
+                            timeout: 60s
+                      {% endif %}
                 http_filters:
                   - name: envoy.filters.http.compressor
                     typed_config:
@@ -201,7 +214,7 @@ static_resources:
                     typed_config:
                       "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
 
-    - name: arch_internal
+    - name: egress_api_traffic
       address:
         socket_address:
           address: 0.0.0.0
@@ -223,11 +236,11 @@ static_resources:
                         envoy_grpc:
                           cluster_name: opentelemetry_collector
                         timeout: 0.250s
-                      service_name: prompt_processor
+                      service_name: egress_api_traffic
                   random_sampling:
                     value: {{ arch_tracing.random_sampling }}
                 {% endif %}
-                stat_prefix: arch_internal
+                stat_prefix: egress_api_traffic
                 codec_type: AUTO
                 scheme_header_transformation:
                   scheme_to_overwrite: https
@@ -273,12 +286,12 @@ static_resources:
                     typed_config:
                       "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
 
-    - name: arch_listener_http_llm
+    - name: egress_traffic
       address:
         socket_address:
           address: {{ llm_gateway_listener.address }}
           port_value: {{ llm_gateway_listener.port }}
-      traffic_direction: INBOUND
+      traffic_direction: OUTBOUND
       filter_chains:
         - filters:
             - name: envoy.filters.network.http_connection_manager
@@ -299,7 +312,7 @@ static_resources:
                   random_sampling:
                     value: {{ arch_tracing.random_sampling }}
                 {% endif %}
-                stat_prefix: arch_listener_http
+                stat_prefix: egress_traffic
                 codec_type: AUTO
                 scheme_header_transformation:
                   scheme_to_overwrite: https
@@ -326,7 +339,7 @@ static_resources:
                     typed_config:
                       "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
 
-    - name: arch_listener_llm
+    - name: egress_traffic_llm
       address:
         socket_address:
           address: 0.0.0.0
@@ -347,11 +360,11 @@ static_resources:
                         envoy_grpc:
                           cluster_name: opentelemetry_collector
                         timeout: 0.250s
-                      service_name: llm_gateway
+                      service_name: egress_traffic_llm
                   random_sampling:
                     value: {{ arch_tracing.random_sampling }}
                 {% endif %}
-                stat_prefix: arch_listener_http
+                stat_prefix: egress_traffic
                 codec_type: AUTO
                 scheme_header_transformation:
                   scheme_to_overwrite: https
diff --git a/arch/tools/README.md b/arch/tools/README.md
index a4722c4a..4ebd2da4 100644
--- a/arch/tools/README.md
+++ b/arch/tools/README.md
@@ -19,7 +19,7 @@ source venv/bin/activate
 
 ### Step 3: Run the build script
 ```bash
-pip install archgw==0.2.1
+pip install archgw==0.2.6
 ```
 
 ## Uninstall Instructions: archgw CLI
diff --git a/arch/tools/cli/config_generator.py b/arch/tools/cli/config_generator.py
index 33ea3c3b..44ceda27 100644
--- a/arch/tools/cli/config_generator.py
+++ b/arch/tools/cli/config_generator.py
@@ -3,6 +3,7 @@ import os
 from jinja2 import Environment, FileSystemLoader
 import yaml
 from jsonschema import validate
+from urllib.parse import urlparse
 
 ENVOY_CONFIG_TEMPLATE_FILE = os.getenv(
     "ENVOY_CONFIG_TEMPLATE_FILE", "envoy.template.yaml"
@@ -47,7 +48,7 @@ def validate_and_render_schema():
         arch_config_schema = file.read()
 
     config_yaml = yaml.safe_load(arch_config)
-    config_schema_yaml = yaml.safe_load(arch_config_schema)
+    _ = yaml.safe_load(arch_config_schema)
     inferred_clusters = {}
 
     endpoints = config_yaml.get("endpoints", {})
@@ -91,6 +92,9 @@ def validate_and_render_schema():
             del llm_provider["provider"]
         updated_llm_providers.append(llm_provider)
 
+        if llm_provider.get("endpoint") and llm_provider.get("base_url"):
+            raise Exception("Please provide either endpoint or base_url, not both")
+
         if llm_provider.get("endpoint", None):
             endpoint = llm_provider["endpoint"]
             protocol = llm_provider.get("protocol", "http")
@@ -98,13 +102,39 @@ def validate_and_render_schema():
                 endpoint, protocol
             )
             llms_with_endpoint.append(llm_provider)
+        elif llm_provider.get("base_url", None):
+            base_url = llm_provider["base_url"]
+            urlparse_result = urlparse(base_url)
+            if llm_provider.get("port"):
+                raise Exception("Please provider port in base_url")
+            if urlparse_result.scheme == "" or urlparse_result.scheme not in [
+                "http",
+                "https",
+            ]:
+                raise Exception(
+                    "Please provide a valid URL with scheme (http/https) in base_url"
+                )
+            protocol = urlparse_result.scheme
+            port = urlparse_result.port
+            if port is None:
+                if protocol == "http":
+                    port = 80
+                else:
+                    port = 443
+            endpoint = urlparse_result.hostname
+            llm_provider["endpoint"] = endpoint
+            llm_provider["port"] = port
+            llm_provider["protocol"] = protocol
+            llms_with_endpoint.append(llm_provider)
 
     config_yaml["llm_providers"] = updated_llm_providers
 
     arch_config_string = yaml.dump(config_yaml)
     arch_llm_config_string = yaml.dump(config_yaml)
 
-    prompt_gateway_listener = config_yaml.get("listeners", {}).get("prompt_gateway", {})
+    prompt_gateway_listener = config_yaml.get("listeners", {}).get(
+        "ingress_traffic", {}
+    )
     if prompt_gateway_listener.get("port") == None:
         prompt_gateway_listener["port"] = 10000  # default port for prompt gateway
     if prompt_gateway_listener.get("address") == None:
@@ -112,7 +142,7 @@ def validate_and_render_schema():
     if prompt_gateway_listener.get("timeout") == None:
         prompt_gateway_listener["timeout"] = "10s"
 
-    llm_gateway_listener = config_yaml.get("listeners", {}).get("llm_gateway", {})
+    llm_gateway_listener = config_yaml.get("listeners", {}).get("egress_traffic", {})
     if llm_gateway_listener.get("port") == None:
         llm_gateway_listener["port"] = 12000  # default port for llm gateway
     if llm_gateway_listener.get("address") == None:
@@ -120,6 +150,26 @@ def validate_and_render_schema():
     if llm_gateway_listener.get("timeout") == None:
         llm_gateway_listener["timeout"] = "10s"
 
+    use_agent_orchestrator = config_yaml.get("overrides", {}).get(
+        "use_agent_orchestrator", False
+    )
+
+    agent_orchestrator = None
+    if use_agent_orchestrator:
+        print("Using agent orchestrator")
+
+        if len(endpoints) == 0:
+            raise Exception(
+                "Please provide agent orchestrator in the endpoints section in your arch_config.yaml file"
+            )
+        elif len(endpoints) > 1:
+            raise Exception(
+                "Please provide single agent orchestrator in the endpoints section in your arch_config.yaml file"
+            )
+        else:
+            agent_orchestrator = list(endpoints.keys())[0]
+
+    print("agent_orchestrator: ", agent_orchestrator)
     data = {
         "prompt_gateway_listener": prompt_gateway_listener,
         "llm_gateway_listener": llm_gateway_listener,
@@ -129,6 +179,7 @@ def validate_and_render_schema():
         "arch_llm_providers": config_yaml["llm_providers"],
         "arch_tracing": arch_tracing,
         "local_llms": llms_with_endpoint,
+        "agent_orchestrator": agent_orchestrator,
     }
 
     rendered = template.render(data)
diff --git a/arch/tools/cli/core.py b/arch/tools/cli/core.py
index 4ffbd873..b0a6e58c 100644
--- a/arch/tools/cli/core.py
+++ b/arch/tools/cli/core.py
@@ -2,110 +2,49 @@ import subprocess
 import os
 import time
 import sys
-import glob
-import docker
-from docker.errors import DockerException
-from cli.utils import getLogger, update_docker_host_env
+
+import yaml
+from cli.utils import getLogger
 from cli.consts import (
-    ARCHGW_DOCKER_IMAGE,
     ARCHGW_DOCKER_NAME,
     KATANEMO_LOCAL_MODEL_LIST,
-    MODEL_SERVER_LOG_FILE,
-    ACCESS_LOG_FILES,
 )
 from huggingface_hub import snapshot_download
-from dotenv import dotenv_values
-import yaml
+import subprocess
+from cli.docker_cli import (
+    docker_container_status,
+    docker_remove_container,
+    docker_start_archgw_detached,
+    docker_stop_container,
+    health_check_endpoint,
+    stream_gateway_logs,
+)
 
 
 log = getLogger(__name__)
 
 
-def start_archgw_docker(
-    client, arch_config_file, env, prompt_gateway_port, llm_gateway_port
-):
-    logs_path = "~/archgw_logs"
-    logs_path_abs = os.path.expanduser(logs_path)
+def _get_gateway_ports(arch_config_file: str) -> tuple:
+    PROMPT_GATEWAY_DEFAULT_PORT = 10000
+    LLM_GATEWAY_DEFAULT_PORT = 12000
 
-    return client.containers.run(
-        name=ARCHGW_DOCKER_NAME,
-        image=ARCHGW_DOCKER_IMAGE,
-        detach=True,  # Run in detached mode
-        ports={
-            f"{prompt_gateway_port}/tcp": prompt_gateway_port,
-            "10001/tcp": 10001,
-            "11000/tcp": 11000,
-            f"{llm_gateway_port}/tcp": llm_gateway_port,
-            "9901/tcp": 19901,
-        },
-        volumes={
-            f"{arch_config_file}": {
-                "bind": "/app/arch_config.yaml",
-                "mode": "ro",
-            },
-            "/etc/ssl/cert.pem": {"bind": "/etc/ssl/cert.pem", "mode": "ro"},
-            logs_path_abs: {"bind": "/var/log"},
-        },
-        environment={
-            "OTEL_TRACING_HTTP_ENDPOINT": "http://host.docker.internal:4318/v1/traces",
-            "MODEL_SERVER_PORT": os.getenv("MODEL_SERVER_PORT", "51000"),
-            **env,
-        },
-        extra_hosts={"host.docker.internal": "host-gateway"},
-        healthcheck={
-            "test": [
-                "CMD",
-                "curl",
-                "-f",
-                f"http://localhost:{prompt_gateway_port}/healthz",
-            ],
-            "interval": 5000000000,  # 5 seconds
-            "timeout": 1000000000,  # 1 seconds
-            "retries": 3,
-        },
+    # parse arch_config_file yaml file and get prompt_gateway_port
+    arch_config_dict = {}
+    with open(arch_config_file) as f:
+        arch_config_dict = yaml.safe_load(f)
+
+    prompt_gateway_port = (
+        arch_config_dict.get("listeners", {})
+        .get("ingress_traffic", {})
+        .get("port", PROMPT_GATEWAY_DEFAULT_PORT)
+    )
+    llm_gateway_port = (
+        arch_config_dict.get("listeners", {})
+        .get("egress_traffic", {})
+        .get("port", LLM_GATEWAY_DEFAULT_PORT)
     )
 
-
-def stream_gateway_logs(follow):
-    """
-    Stream logs from the arch gateway service.
-    """
-    log.info("Logs from arch gateway service.")
-
-    options = ["docker", "logs", "archgw"]
-    if follow:
-        options.append("-f")
-    try:
-        # Run `docker-compose logs` to stream logs from the gateway service
-        subprocess.run(
-            options,
-            check=True,
-            stdout=sys.stdout,
-            stderr=sys.stderr,
-        )
-
-    except subprocess.CalledProcessError as e:
-        log.info(f"Failed to stream logs: {str(e)}")
-
-
-def stream_access_logs(follow):
-    """
-    Get the archgw access logs
-    """
-    log_file_pattern_expanded = os.path.expanduser(ACCESS_LOG_FILES)
-    log_files = glob.glob(log_file_pattern_expanded)
-
-    stream_command = ["tail"]
-    if follow:
-        stream_command.append("-f")
-
-    stream_command.extend(log_files)
-    subprocess.run(
-        stream_command,
-        check=True,
-        stdout=sys.stdout,
-        stderr=sys.stderr,
-    )
+    return prompt_gateway_port, llm_gateway_port
 
 
 def start_arch(arch_config_file, env, log_timeout=120, foreground=False):
@@ -119,73 +58,58 @@ def start_arch(arch_config_file, env, log_timeout=120, foreground=False):
     log.info("Starting arch gateway")
 
     try:
-        try:
-            client = docker.from_env()
-        except DockerException as e:
-            # try setting up the docker host environment variable and retry
-            update_docker_host_env()
-            client = docker.from_env()
+        archgw_container_status = docker_container_status(ARCHGW_DOCKER_NAME)
+        if archgw_container_status != "not found":
+            log.info("archgw found in docker, stopping and removing it")
+            docker_stop_container(ARCHGW_DOCKER_NAME)
+            docker_remove_container(ARCHGW_DOCKER_NAME)
 
-        try:
-            container = client.containers.get("archgw")
-            log.info("archgw container found in docker, stopping and removing it")
-            # ensure that previous docker container is stopped and removed
-            container.stop()
-            container.remove()
-            log.info("Stopped and removed archgw container")
-        except docker.errors.NotFound as e:
-            pass
+        prompt_gateway_port, llm_gateway_port = _get_gateway_ports(arch_config_file)
 
-        # parse arch_config_file yaml file and get prompt_gateway_port
-        arch_config_dict = {}
-        with open(arch_config_file) as f:
-            arch_config_dict = yaml.safe_load(f)
-
-        prompt_gateway_port = (
-            arch_config_dict.get("listeners", {})
-            .get("prompt_gateway", {})
-            .get("port", 10000)
-        )
-        llm_gateway_port = (
-            arch_config_dict.get("listeners", {})
-            .get("llm_gateway", {})
-            .get("port", 12000)
-        )
-
-        container = start_archgw_docker(
-            client, arch_config_file, env, prompt_gateway_port, llm_gateway_port
+        return_code, _, archgw_stderr = docker_start_archgw_detached(
+            arch_config_file,
+            os.path.expanduser("~/archgw_logs"),
+            env,
+            prompt_gateway_port,
+            llm_gateway_port,
         )
+        if return_code != 0:
+            log.info("Failed to start arch gateway: " + str(return_code))
+            log.info("stderr: " + archgw_stderr)
+            sys.exit(1)
 
         start_time = time.time()
-
         while True:
-            container = client.containers.get(container.id)
+            prompt_gateway_health_check_status = health_check_endpoint(
+                f"http://localhost:{prompt_gateway_port}/healthz"
+            )
+
+            llm_gateway_health_check_status = health_check_endpoint(
+                f"http://localhost:{llm_gateway_port}/healthz"
+            )
+
+            archgw_status = docker_container_status(ARCHGW_DOCKER_NAME)
             current_time = time.time()
             elapsed_time = current_time - start_time
 
             # Check if timeout is reached
             if elapsed_time > log_timeout:
-                log.info(f"Stopping log monitoring after {log_timeout} seconds.")
+                log.info(f"stopping log monitoring after {log_timeout} seconds.")
                 break
 
-            container_status = container.attrs["State"]["Health"]["Status"]
-
-            if container_status == "healthy":
-                log.info("Container is healthy!")
+            if prompt_gateway_health_check_status or llm_gateway_health_check_status:
+                log.info("archgw is running and is healthy!")
                 break
             else:
-                log.info(f"Container health status: {container_status}")
+                log.info(f"archgw status: {archgw_status}, health status: starting")
                 time.sleep(1)
 
         if foreground:
-            for line in container.logs(stream=True):
-                print(line.decode("utf-8").strip("\n"))
+            stream_gateway_logs(follow=True)
 
     except KeyboardInterrupt:
         log.info("Keyboard interrupt received, stopping arch gateway service.")
         stop_arch()
-    except docker.errors.APIError as e:
-        log.info(f"Failed to start Arch: {str(e)}")
 
 
 def stop_arch():
@@ -199,10 +123,10 @@ def stop_arch():
 
     try:
         subprocess.run(
-            ["docker", "stop", "archgw"],
+            ["docker", "stop", ARCHGW_DOCKER_NAME],
         )
         subprocess.run(
-            ["docker", "remove", "archgw"],
+            ["docker", "rm", ARCHGW_DOCKER_NAME],
         )
 
         log.info("Successfully shut down arch gateway service.")
diff --git a/arch/tools/cli/docker_cli.py b/arch/tools/cli/docker_cli.py
new file mode 100644
index 00000000..6edfb8dc
--- /dev/null
+++ b/arch/tools/cli/docker_cli.py
@@ -0,0 +1,133 @@
+import subprocess
+import json
+import sys
+import requests
+
+from cli.consts import ARCHGW_DOCKER_IMAGE, ARCHGW_DOCKER_NAME
+from cli.utils import getLogger
+
+log = getLogger(__name__)
+
+
+def docker_container_status(container: str) -> str:
+    result = subprocess.run(
+        ["docker", "inspect", "--type=container", container],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    if result.returncode != 0:
+        return "not found"
+
+    container_status = json.loads(result.stdout)[0]
+    return container_status.get("State", {}).get("Status", "")
+
+
+def docker_stop_container(container: str) -> str:
+    result = subprocess.run(
+        ["docker", "stop", container], capture_output=True, text=True, check=False
+    )
+    return result.returncode
+
+
+def docker_remove_container(container: str) -> str:
+    result = subprocess.run(
+        ["docker", "rm", container], capture_output=True, text=True, check=False
+    )
+    return result.returncode
+
+
+def docker_start_archgw_detached(
+    arch_config_file: str,
+    logs_path_abs: str,
+    env: dict,
+    prompt_gateway_port,
+    llm_gateway_port,
+) -> str:
+    env_args = [item for key, value in env.items() for item in ["-e", f"{key}={value}"]]
+
+    port_mappings = [
+        f"{prompt_gateway_port}:{prompt_gateway_port}",
+        f"{llm_gateway_port}:{llm_gateway_port}",
+        "9901:19901",
+    ]
+    port_mappings_args = [item for port in port_mappings for item in ("-p", port)]
+
+    volume_mappings = [
+        f"{logs_path_abs}:/var/log:rw",
+        f"{arch_config_file}:/app/arch_config.yaml:ro",
+        # "/Users/adilhafeez/src/intelligent-prompt-gateway/crates/target/wasm32-wasip1/release:/etc/envoy/proxy-wasm-plugins:ro",
+    ]
+    volume_mappings_args = [
+        item for volume in volume_mappings for item in ("-v", volume)
+    ]
+
+    options = [
+        "docker",
+        "run",
+        "-d",
+        "--name",
+        ARCHGW_DOCKER_NAME,
+        *port_mappings_args,
+        *volume_mappings_args,
+        *env_args,
+        "--add-host",
+        "host.docker.internal:host-gateway",
+        ARCHGW_DOCKER_IMAGE,
+    ]
+
+    result = subprocess.run(options, capture_output=True, text=True, check=False)
+    return result.returncode, result.stdout, result.stderr
+
+
+def health_check_endpoint(endpoint: str) -> bool:
+    try:
+        response = requests.get(endpoint)
+        if response.status_code == 200:
+            return True
+    except requests.RequestException as e:
+        pass
+    return False
+
+
+def stream_gateway_logs(follow):
+    """
+    Stream logs from the arch gateway service.
+    """
+    log.info("Logs from arch gateway service.")
+
+    options = ["docker", "logs"]
+    if follow:
+        options.append("-f")
+    options.append(ARCHGW_DOCKER_NAME)
+    try:
+        # Run `docker-compose logs` to stream logs from the gateway service
+        subprocess.run(
+            options,
+            check=True,
+            stdout=sys.stdout,
+            stderr=sys.stderr,
+        )
+
+    except subprocess.CalledProcessError as e:
+        log.info(f"Failed to stream logs: {str(e)}")
+
+
+def docker_validate_archgw_schema(arch_config_file):
+    result = subprocess.run(
+        [
+            "docker",
+            "run",
+            "--rm",
+            "-v",
+            f"{arch_config_file}:/app/arch_config.yaml:ro",
+            "--entrypoint",
+            "python",
+            ARCHGW_DOCKER_IMAGE,
+            "config_generator.py",
+        ],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    return result.returncode, result.stdout, result.stderr
diff --git a/arch/tools/cli/main.py b/arch/tools/cli/main.py
index 22ed7123..240a1b8c 100644
--- a/arch/tools/cli/main.py
+++ b/arch/tools/cli/main.py
@@ -5,11 +5,12 @@ import subprocess
 import multiprocessing
 import importlib.metadata
 from cli import targets
+from cli.docker_cli import docker_validate_archgw_schema, stream_gateway_logs
 from cli.utils import (
     getLogger,
     get_llm_provider_access_keys,
     load_env_file_to_dict,
-    validate_schema,
+    stream_access_logs,
 )
 from cli.core import (
     start_arch_modelserver,
@@ -17,12 +18,9 @@ from cli.core import (
     start_arch,
     stop_arch,
     download_models_from_hf,
-    stream_access_logs,
-    stream_gateway_logs,
 )
 from cli.consts import (
     KATANEMO_DOCKERHUB_REPO,
-    KATANEMO_LOCAL_MODEL_LIST,
     SERVICE_NAME_ARCHGW,
     SERVICE_NAME_MODEL_SERVER,
     SERVICE_ALL,
@@ -174,17 +172,24 @@ def up(file, path, service, foreground):
 
     log.info(f"Validating {arch_config_file}")
 
-    try:
-        validate_schema(arch_config_file)
-    except Exception as e:
-        log.info(f"Exiting archgw up: validation failed")
-        log.info(f"Error: {str(e)}")
+    (
+        validation_return_code,
+        validation_stdout,
+        validation_stderr,
+    ) = docker_validate_archgw_schema(arch_config_file)
+    if validation_return_code != 0:
+        log.info(f"Error: Validation failed. Exiting")
+        log.info(f"Validation stdout: {validation_stdout}")
+        log.info(f"Validation stderr: {validation_stderr}")
         sys.exit(1)
 
     log.info("Starting arch model server and arch gateway")
 
     # Set the ARCH_CONFIG_FILE environment variable
-    env_stage = {}
+    env_stage = {
+        "OTEL_TRACING_HTTP_ENDPOINT": "http://host.docker.internal:4318/v1/traces",
+        "MODEL_SERVER_PORT": os.getenv("MODEL_SERVER_PORT", "51000"),
+    }
     env = os.environ.copy()
     # check if access_keys are preesnt in the config file
     access_keys = get_llm_provider_access_keys(arch_config_file=arch_config_file)
diff --git a/arch/tools/cli/targets.py b/arch/tools/cli/targets.py
index a25c1aad..73b14b36 100644
--- a/arch/tools/cli/targets.py
+++ b/arch/tools/cli/targets.py
@@ -2,7 +2,6 @@ import ast
 import sys
 import yaml
 from typing import Any
-from pydantic import BaseModel
 
 FLASK_ROUTE_DECORATORS = ["route", "get", "post", "put", "delete", "patch"]
 FASTAPI_ROUTE_DECORATORS = ["get", "post", "put", "delete", "patch"]
diff --git a/arch/tools/cli/utils.py b/arch/tools/cli/utils.py
index cc6ac20b..eeb4516e 100644
--- a/arch/tools/cli/utils.py
+++ b/arch/tools/cli/utils.py
@@ -1,10 +1,11 @@
+import glob
 import os
+import subprocess
+import sys
 import yaml
 import logging
-import docker
-from docker.errors import DockerException
 
-from cli.consts import ARCHGW_DOCKER_IMAGE, ARCHGW_DOCKER_NAME
+from cli.consts import ACCESS_LOG_FILES
 
 logging.basicConfig(
     level=logging.INFO,
@@ -21,63 +22,6 @@ def getLogger(name="cli"):
 log = getLogger(__name__)
 
 
-def update_docker_host_env():
-    """
-    Update DOCKER_HOST environment variable to use the local Docker socket
-    """
-    if os.getenv("DOCKER_HOST"):
-        return
-
-    default_docker_socket = os.getenv("DEFAULT_DOCKER_SOCKET", "/var/run/docker.sock")
-    if not os.path.exists(default_docker_socket):
-        home_dir = os.getenv("HOME")
-        docker_host = f"unix://{home_dir}/.docker/run/docker.sock"
-        log.info(
-            f"Default docker socket {default_docker_socket} not found, using {docker_host}"
-        )
-        os.environ["DOCKER_HOST"] = docker_host
-
-
-def validate_schema(arch_config_file: str) -> None:
-    try:
-        try:
-            client = docker.from_env()
-        except DockerException as e:
-            # try setting up the docker host environment variable and retry
-            update_docker_host_env()
-            client = docker.from_env()
-
-        container = client.containers.run(
-            image=ARCHGW_DOCKER_IMAGE,
-            volumes={
-                f"{arch_config_file}": {
-                    "bind": "/app/arch_config.yaml",
-                    "mode": "ro",
-                },
-            },
-            entrypoint=["python", "config_generator.py"],
-            detach=True,
-        )
-
-        # Wait for the container to finish and get the exit code
-        exit_code = container.wait()
-
-        # Check exit code for validation success
-        if exit_code["StatusCode"] != 0:
-            # Validation failed (non-zero exit code)
-            logs = container.logs().decode()  # Get container logs for debugging
-            raise ValueError(
-                f"Validation failed. Container exited with code {exit_code}.\nLogs:\n{logs}"
-            )
-
-        # Successful validation (exit code 0)
-        log.info("Schema validation successful!")
-
-    except docker.errors.APIError as e:
-        # Handle container creation error
-        raise ValueError(f"Failed to create container: {e}")
-
-
 def get_llm_provider_access_keys(arch_config_file):
     with open(arch_config_file, "r") as file:
         arch_config = file.read()
@@ -127,3 +71,23 @@ def load_env_file_to_dict(file_path):
                 env_dict[key] = value
 
     return env_dict
+
+
+def stream_access_logs(follow):
+    """
+    Get the archgw access logs
+    """
+    log_file_pattern_expanded = os.path.expanduser(ACCESS_LOG_FILES)
+    log_files = glob.glob(log_file_pattern_expanded)
+
+    stream_command = ["tail"]
+    if follow:
+        stream_command.append("-f")
+
+    stream_command.extend(log_files)
+    subprocess.run(
+        stream_command,
+        check=True,
+        stdout=sys.stdout,
+        stderr=sys.stderr,
+    )
diff --git a/arch/tools/poetry.lock b/arch/tools/poetry.lock
index bcb89dae..728f175e 100644
--- a/arch/tools/poetry.lock
+++ b/arch/tools/poetry.lock
@@ -1,34 +1,134 @@
 # This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
 
 [[package]]
-name = "archgw_modelserver"
-version = "0.2.1"
+name = "accelerate"
+version = "1.6.0"
+description = "Accelerate"
+optional = false
+python-versions = ">=3.9.0"
+files = [
+    {file = "accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa"},
+    {file = "accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.21.0"
+numpy = ">=1.17,<3.0.0"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = ">=0.4.3"
+torch = ">=2.0.0"
+
+[package.extras]
+deepspeed = ["deepspeed"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.11.2,<0.12.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.11.2,<0.12.0)"]
+rich = ["rich"]
+sagemaker = ["sagemaker"]
+test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"]
+test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "tensorboard", "wandb"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.9.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+trio = ["trio (>=0.26.1)"]
+
+[[package]]
+name = "archgw-modelserver"
+version = "0.2.6"
 description = "A model server for serving models"
 optional = false
-python-versions = "*"
-files = []
-develop = true
+python-versions = "<4.0,>=3.10"
+files = [
+    {file = "archgw_modelserver-0.2.6-py3-none-any.whl", hash = "sha256:7b472404734a799aa8847a8a142275bc532c964e76f3db1a6781f84abe92c290"},
+    {file = "archgw_modelserver-0.2.6.tar.gz", hash = "sha256:09186c2e2a4f61bb01366490ac1638ed67f0aa3488cdb1ec57a17c97411b9500"},
+]
 
-[package.source]
-type = "directory"
-url = "../../model_server"
+[package.dependencies]
+accelerate = ">=1.0.0,<2.0.0"
+dateparser = "*"
+fastapi = "0.115.0"
+httpx = "0.27.2"
+openai = ">=1.50.2,<2.0.0"
+opentelemetry-api = ">=1.28.0,<2.0.0"
+opentelemetry-exporter-otlp = ">=1.28.0,<2.0.0"
+opentelemetry-instrumentation-fastapi = ">=0.49b0,<0.50"
+opentelemetry-sdk = ">=1.28.0,<2.0.0"
+overrides = ">=7.7.0,<8.0.0"
+pydantic = ">=2.10.1,<3.0.0"
+pytest = "*"
+pytest-asyncio = "*"
+pytest-httpserver = ">=1.1.0,<2.0.0"
+pytest-retry = ">=1.6.3,<2.0.0"
+setuptools = "75.5.0"
+torch = "2.4.1"
+transformers = ">=4.37.0,<5.0.0"
+uvicorn = "0.31.0"
+
+[[package]]
+name = "asgiref"
+version = "3.8.1"
+description = "ASGI specs, helper code, and adapters"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"},
+    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
+
+[package.extras]
+tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
 
 [[package]]
 name = "attrs"
-version = "25.1.0"
+version = "25.3.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"},
-    {file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"},
+    {file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"},
+    {file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"},
 ]
 
 [package.extras]
 benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"]
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
@@ -170,26 +270,315 @@ files = [
 ]
 
 [[package]]
-name = "docker"
-version = "7.1.0"
-description = "A Python library for the Docker Engine API."
+name = "dateparser"
+version = "1.2.1"
+description = "Date parsing library designed to parse dates from HTML pages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "docker-7.1.0-py3-none-any.whl", hash = "sha256:c96b93b7f0a746f9e77d325bcfb87422a3d8bd4f03136ae8a85b37f1898d5fc0"},
-    {file = "docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c"},
+    {file = "dateparser-1.2.1-py3-none-any.whl", hash = "sha256:bdcac262a467e6260030040748ad7c10d6bacd4f3b9cdb4cfd2251939174508c"},
+    {file = "dateparser-1.2.1.tar.gz", hash = "sha256:7e4919aeb48481dbfc01ac9683c8e20bfe95bb715a38c1e9f6af889f4f30ccc3"},
 ]
 
 [package.dependencies]
-pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
-requests = ">=2.26.0"
-urllib3 = ">=1.26.0"
+python-dateutil = ">=2.7.0"
+pytz = ">=2024.2"
+regex = ">=2015.06.24,<2019.02.19 || >2019.02.19,<2021.8.27 || >2021.8.27"
+tzlocal = ">=0.2"
 
 [package.extras]
-dev = ["coverage (==7.2.7)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.1.0)", "ruff (==0.1.8)"]
-docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"]
-ssh = ["paramiko (>=2.4.3)"]
-websockets = ["websocket-client (>=1.3.0)"]
+calendars = ["convertdate (>=2.2.1)", "hijridate"]
+fasttext = ["fasttext (>=0.9.1)", "numpy (>=1.19.3,<2)"]
+langdetect = ["langdetect (>=1.0.0)"]
+
+[[package]]
+name = "deprecated"
+version = "1.2.18"
+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+files = [
+    {file = "Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec"},
+    {file = "deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d"},
+]
+
+[package.dependencies]
+wrapt = ">=1.10,<2"
+
+[package.extras]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
+
+[[package]]
+name = "distro"
+version = "1.9.0"
+description = "Distro - an OS platform information API"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
+    {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
+]
+
+[[package]]
+name = "exceptiongroup"
+version = "1.2.2"
+description = "Backport of PEP 654 (exception groups)"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
+    {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
+]
+
+[package.extras]
+test = ["pytest (>=6)"]
+
+[[package]]
+name = "fastapi"
+version = "0.115.0"
+description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fastapi-0.115.0-py3-none-any.whl", hash = "sha256:17ea427674467486e997206a5ab25760f6b09e069f099b96f5b55a32fb6f1631"},
+    {file = "fastapi-0.115.0.tar.gz", hash = "sha256:f93b4ca3529a8ebc6fc3fcf710e5efa8de3df9b41570958abf1d97d843138004"},
+]
+
+[package.dependencies]
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
+starlette = ">=0.37.2,<0.39.0"
+typing-extensions = ">=4.8.0"
+
+[package.extras]
+all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
+
+[[package]]
+name = "filelock"
+version = "3.18.0"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de"},
+    {file = "filelock-3.18.0.tar.gz", hash = "sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2"},
+]
+
+[package.extras]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
+typing = ["typing-extensions (>=4.12.2)"]
+
+[[package]]
+name = "fsspec"
+version = "2025.3.2"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "fsspec-2025.3.2-py3-none-any.whl", hash = "sha256:2daf8dc3d1dfa65b6aa37748d112773a7a08416f6c70d96b264c96476ecaf711"},
+    {file = "fsspec-2025.3.2.tar.gz", hash = "sha256:e52c77ef398680bbd6a98c0e628fbc469491282981209907bbc8aea76a04fdc6"},
+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+tqdm = ["tqdm"]
+
+[[package]]
+name = "googleapis-common-protos"
+version = "1.70.0"
+description = "Common protobufs used in Google APIs"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"},
+    {file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"},
+]
+
+[package.dependencies]
+protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0"
+
+[package.extras]
+grpc = ["grpcio (>=1.44.0,<2.0.0)"]
+
+[[package]]
+name = "grpcio"
+version = "1.71.0"
+description = "HTTP/2-based RPC framework"
+optional = false
+python-versions = ">=3.9"
+files = [
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 name = "requests"
 version = "2.32.3"
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+python-versions = ">=3.8"
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+]
+
+[[package]]
+name = "zipp"
+version = "3.21.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "d02e43f0884294d48736e1b8df248f47af480baffcbb7a0194da4e16cc1ea502"
+content-hash = "5cc1d766ed43530c15dfbde7710346e1b157be450bf4736eb4eed2b091efef47"
diff --git a/arch/tools/pyproject.toml b/arch/tools/pyproject.toml
index 8ce28cbc..f730684e 100644
--- a/arch/tools/pyproject.toml
+++ b/arch/tools/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "archgw"
-version = "0.2.1"
+version = "0.2.6"
 description = "Python-based CLI tool to manage Arch Gateway."
 authors = ["Katanemo Labs, Inc."]
 packages = [
@@ -10,13 +10,11 @@ readme = "README.md"
 
 [tool.poetry.dependencies]
 python = "^3.10"
-archgw_modelserver = "^0.2.1"
+archgw_modelserver = "^0.2.6"
 click = "^8.1.7"
 jinja2 = "^3.1.4"
 jsonschema = "^4.23.0"
 setuptools = "75.5.0"
-docker = "^7.1.0"
-python-dotenv = "^1.0.1"
 pyyaml = "^6.0.2"
 
 [tool.poetry.scripts]
diff --git a/arch/validate_arch_config.sh b/arch/validate_arch_config.sh
new file mode 100644
index 00000000..a3822e90
--- /dev/null
+++ b/arch/validate_arch_config.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+failed_files=()
+
+for file in $(find . -name arch_config.yaml -o -name arch_config_full_reference.yaml); do
+  echo "Validating $file..."
+  if ! docker run --rm -v "$(pwd)/$file:/app/arch_config.yaml:ro" --entrypoint /bin/sh katanemo/archgw:latest -c "python config_generator.py" 2>&1 > /dev/null ; then
+    echo "Validation failed for $file"
+    failed_files+=("$file")
+  fi
+done
+
+# Print summary of failed files
+if [ ${#failed_files[@]} -ne 0 ]; then
+  echo -e "\nValidation failed for the following files:"
+  printf '%s\n' "${failed_files[@]}"
+  exit 1
+else
+  echo -e "\nAll files validated successfully!"
+fi
diff --git a/archgw.code-workspace b/archgw.code-workspace
index 78c124e2..840bd2dd 100644
--- a/archgw.code-workspace
+++ b/archgw.code-workspace
@@ -31,6 +31,10 @@
     {
       "name": "chatbot_ui",
       "path": "demos/shared/chatbot_ui"
+    },
+    {
+      "name": "java_demo",
+      "path": "demos/samples_java/weather_forcecast_service"
     }
   ],
   "settings": {
diff --git a/crates/common/src/api/open_ai.rs b/crates/common/src/api/open_ai.rs
index 8c72a7cd..d71b0d58 100644
--- a/crates/common/src/api/open_ai.rs
+++ b/crates/common/src/api/open_ai.rs
@@ -135,7 +135,10 @@ impl From<String> for ParameterType {
             "array" => ParameterType::List,
             "dict" => ParameterType::Dict,
             "dictionary" => ParameterType::Dict,
-            _ => ParameterType::String,
+            _ => {
+                log::warn!("Unknown parameter type: {}, assuming type str", s);
+                ParameterType::String
+            }
         }
     }
 }
@@ -186,7 +189,7 @@ pub struct ToolCall {
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct FunctionCallDetail {
     pub name: String,
-    pub arguments: HashMap<String, Value>,
+    pub arguments: Option<HashMap<String, Value>>,
 }
 
 #[derive(Debug, Deserialize, Serialize)]
@@ -202,13 +205,6 @@ pub struct ToolCallState {
 pub enum ArchState {
     ToolCall(Vec<ToolCallState>),
 }
-#[derive(Deserialize, Serialize)]
-#[serde(untagged)]
-pub enum ModelServerResponse {
-    ChatCompletionsResponse(ChatCompletionsResponse),
-    ModelServerErrorResponse(ModelServerErrorResponse),
-}
-
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ModelServerErrorResponse {
     pub result: String,
diff --git a/crates/common/src/configuration.rs b/crates/common/src/configuration.rs
index acd97fc3..2065b1aa 100644
--- a/crates/common/src/configuration.rs
+++ b/crates/common/src/configuration.rs
@@ -25,6 +25,7 @@ pub struct Configuration {
 pub struct Overrides {
     pub prompt_target_intent_matching_threshold: Option<f64>,
     pub optimize_context_window: Option<bool>,
+    pub use_agent_orchestrator: Option<bool>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize, Default)]
@@ -159,7 +160,7 @@ pub struct LlmProvider {
     pub name: String,
     pub provider_interface: LlmProviderType,
     pub access_key: Option<String>,
-    pub model: String,
+    pub model: Option<String>,
     pub default: Option<bool>,
     pub stream: Option<bool>,
     pub endpoint: Option<String>,
@@ -326,16 +327,6 @@ mod test {
             Some("/agent/summary".to_string())
         );
 
-        let error_target = config.error_target.as_ref().unwrap();
-        assert_eq!(
-            error_target.endpoint.as_ref().unwrap().name,
-            "error_target_1".to_string()
-        );
-        assert_eq!(
-            error_target.endpoint.as_ref().unwrap().path,
-            Some("/error".to_string())
-        );
-
         let tracing = config.tracing.as_ref().unwrap();
         assert_eq!(tracing.sampling_rate.unwrap(), 0.1);
 
diff --git a/crates/common/src/consts.rs b/crates/common/src/consts.rs
index cd52220e..e58bebde 100644
--- a/crates/common/src/consts.rs
+++ b/crates/common/src/consts.rs
@@ -11,10 +11,13 @@ pub const MODEL_SERVER_NAME: &str = "model_server";
 pub const ARCH_ROUTING_HEADER: &str = "x-arch-llm-provider";
 pub const MESSAGES_KEY: &str = "messages";
 pub const ARCH_PROVIDER_HINT_HEADER: &str = "x-arch-llm-provider-hint";
-pub const CHAT_COMPLETIONS_PATH: &str = "/v1/chat/completions";
+pub const CHAT_COMPLETIONS_PATH: [&str; 2] = ["/v1/chat/completions", "/openai/v1/chat/completions"];
 pub const HEALTHZ_PATH: &str = "/healthz";
-pub const ARCH_STATE_HEADER: &str = "x-arch-state";
-pub const ARCH_FC_MODEL_NAME: &str = "Arch-Function-1.5B";
+pub const X_ARCH_STATE_HEADER: &str = "x-arch-state";
+pub const X_ARCH_API_RESPONSE: &str = "x-arch-api-response-message";
+pub const X_ARCH_TOOL_CALL: &str = "x-arch-tool-call-message";
+pub const X_ARCH_FC_MODEL_RESPONSE: &str = "x-arch-fc-model-response";
+pub const ARCH_FC_MODEL_NAME: &str = "Arch-Function";
 pub const REQUEST_ID_HEADER: &str = "x-request-id";
 pub const TRACE_PARENT_HEADER: &str = "traceparent";
 pub const ARCH_INTERNAL_CLUSTER_NAME: &str = "arch_internal";
diff --git a/crates/common/src/http.rs b/crates/common/src/http.rs
index 85626f9b..e3120fc4 100644
--- a/crates/common/src/http.rs
+++ b/crates/common/src/http.rs
@@ -3,7 +3,7 @@ use crate::{
     stats::{Gauge, IncrementingMetric},
 };
 use derivative::Derivative;
-use log::trace;
+use log::debug;
 use proxy_wasm::traits::Context;
 use serde::Serialize;
 use std::{cell::RefCell, collections::HashMap, fmt::Debug, time::Duration};
@@ -48,10 +48,9 @@ pub trait Client: Context {
         call_args: CallArgs,
         call_context: Self::CallContext,
     ) -> Result<u32, ClientError> {
-        trace!(
+        debug!(
             "dispatching http call with args={:?} context={:?}",
-            call_args,
-            call_context
+            call_args, call_context
         );
 
         match self.dispatch_http_call(
diff --git a/crates/common/src/tokenizer.rs b/crates/common/src/tokenizer.rs
index c424e344..11ce7295 100644
--- a/crates/common/src/tokenizer.rs
+++ b/crates/common/src/tokenizer.rs
@@ -1,19 +1,25 @@
-use log::trace;
-
-#[derive(thiserror::Error, Debug, PartialEq, Eq)]
-#[allow(dead_code)]
-pub enum Error {
-    #[error("Unknown model: {model_name}")]
-    UnknownModel { model_name: String },
-}
+use log::debug;
 
 #[allow(dead_code)]
-pub fn token_count(model_name: &str, text: &str) -> Result<usize, Error> {
-    trace!("getting token count model={}", model_name);
+pub fn token_count(model_name: &str, text: &str) -> Result<usize, String> {
+    debug!("getting token count model={}", model_name);
+    //HACK: add support for tokenizing mistral and other models
+    //filed issue https://github.com/katanemo/arch/issues/222
+
+    let updated_model = match model_name.starts_with("gpt") {
+        false => {
+            debug!(
+                "tiktoken_rs: unsupported model: {}, using gpt-4 to compute token count",
+                model_name
+            );
+
+            "gpt-4"
+        }
+        true => model_name,
+    };
+
     // Consideration: is it more expensive to instantiate the BPE object every time, or to contend the singleton?
-    let bpe = tiktoken_rs::get_bpe_from_model(model_name).map_err(|_| Error::UnknownModel {
-        model_name: model_name.to_string(),
-    })?;
+    let bpe = tiktoken_rs::get_bpe_from_model(updated_model).map_err(|e| e.to_string())?;
     Ok(bpe.encode_ordinary(text).len())
 }
 
@@ -30,14 +36,4 @@ mod test {
             token_count(model_name, text).expect("correct tokenization")
         );
     }
-
-    #[test]
-    fn unrecognized_model() {
-        assert_eq!(
-            Error::UnknownModel {
-                model_name: "unknown".to_string()
-            },
-            token_count("unknown", "").expect_err("unknown model")
-        )
-    }
 }
diff --git a/crates/common/src/tracing.rs b/crates/common/src/tracing.rs
index 85a95b0f..363a0870 100644
--- a/crates/common/src/tracing.rs
+++ b/crates/common/src/tracing.rs
@@ -166,7 +166,7 @@ impl TraceData {
                 attributes: vec![Attribute {
                     key: "service.name".to_string(),
                     value: AttributeValue {
-                        string_value: Some("upstream-llm".to_string()),
+                        string_value: Some("egress_llm_traffic".to_string()),
                     },
                 }],
             };
diff --git a/crates/llm_gateway/src/filter_context.rs b/crates/llm_gateway/src/filter_context.rs
index 0edba456..fc31355a 100644
--- a/crates/llm_gateway/src/filter_context.rs
+++ b/crates/llm_gateway/src/filter_context.rs
@@ -1,6 +1,7 @@
 use crate::metrics::Metrics;
 use crate::stream_context::StreamContext;
 use common::configuration::Configuration;
+use common::configuration::Overrides;
 use common::consts::OTEL_COLLECTOR_HTTP;
 use common::consts::OTEL_POST_PATH;
 use common::http::CallArgs;
@@ -31,6 +32,7 @@ pub struct FilterContext {
     callouts: RefCell<HashMap<u32, CallContext>>,
     llm_providers: Option<Rc<LlmProviders>>,
     traces_queue: Arc<Mutex<VecDeque<TraceData>>>,
+    overrides: Rc<Option<Overrides>>,
 }
 
 impl FilterContext {
@@ -40,6 +42,7 @@ impl FilterContext {
             metrics: Rc::new(Metrics::new()),
             llm_providers: None,
             traces_queue: Arc::new(Mutex::new(VecDeque::new())),
+            overrides: Rc::new(None),
         }
     }
 }
@@ -69,6 +72,7 @@ impl RootContext for FilterContext {
         };
 
         ratelimit::ratelimits(Some(config.ratelimits.unwrap_or_default()));
+        self.overrides = Rc::new(config.overrides);
 
         match config.llm_providers.try_into() {
             Ok(llm_providers) => self.llm_providers = Some(Rc::new(llm_providers)),
@@ -93,6 +97,7 @@ impl RootContext for FilterContext {
                     .expect("LLM Providers must exist when Streams are being created"),
             ),
             Arc::clone(&self.traces_queue),
+            Rc::clone(&self.overrides),
         )))
     }
 
diff --git a/crates/llm_gateway/src/stream_context.rs b/crates/llm_gateway/src/stream_context.rs
index 528358a3..5b741a43 100644
--- a/crates/llm_gateway/src/stream_context.rs
+++ b/crates/llm_gateway/src/stream_context.rs
@@ -3,9 +3,9 @@ use common::api::open_ai::{
     ChatCompletionStreamResponseServerEvents, ChatCompletionsRequest, ChatCompletionsResponse,
     Message, StreamOptions,
 };
-use common::configuration::LlmProvider;
+use common::configuration::{LlmProvider, LlmProviderType, Overrides};
 use common::consts::{
-    ARCH_PROVIDER_HINT_HEADER, ARCH_ROUTING_HEADER, CHAT_COMPLETIONS_PATH,
+    ARCH_PROVIDER_HINT_HEADER, ARCH_ROUTING_HEADER, CHAT_COMPLETIONS_PATH, HEALTHZ_PATH,
     RATELIMIT_SELECTOR_HEADER_KEY, REQUEST_ID_HEADER, TRACE_PARENT_HEADER,
 };
 use common::errors::ServerError;
@@ -15,7 +15,7 @@ use common::stats::{IncrementingMetric, RecordingMetric};
 use common::tracing::{Event, Span, TraceData, Traceparent};
 use common::{ratelimit, routing, tokenizer};
 use http::StatusCode;
-use log::{debug, trace, warn};
+use log::{debug, info, warn};
 use proxy_wasm::hostcalls::get_current_time;
 use proxy_wasm::traits::*;
 use proxy_wasm::types::*;
@@ -42,6 +42,7 @@ pub struct StreamContext {
     request_body_sent_time: Option<u128>,
     user_message: Option<Message>,
     traces_queue: Arc<Mutex<VecDeque<TraceData>>>,
+    overrides: Rc<Option<Overrides>>,
 }
 
 impl StreamContext {
@@ -50,10 +51,12 @@ impl StreamContext {
         metrics: Rc<Metrics>,
         llm_providers: Rc<LlmProviders>,
         traces_queue: Arc<Mutex<VecDeque<TraceData>>>,
+        overrides: Rc<Option<Overrides>>,
     ) -> Self {
         StreamContext {
             context_id,
             metrics,
+            overrides,
             ratelimit_selector: None,
             streaming_response: false,
             response_tokens: 0,
@@ -86,10 +89,34 @@ impl StreamContext {
             provider_hint,
         ));
 
+        // Check if we need to modify the path based on the provider's base_url
+        let needs_openai_prefix = self
+            .llm_provider
+            .as_ref()
+            .and_then(|provider| provider.endpoint.as_ref())
+            .map(|url| url.contains("api.groq.com"))
+            .unwrap_or(false);
+
+        if needs_openai_prefix {
+            if let Some(path) = self.get_http_request_header(":path") {
+                if path.starts_with("/v1/") {
+                    let new_path = format!("/openai{}", path);
+                    self.set_http_request_header(":path", Some(new_path.as_str()));
+                }
+            }
+        }
+
         debug!(
-            "request received: llm provider hint: {:?}, selected llm: {}",
-            self.get_http_request_header(ARCH_PROVIDER_HINT_HEADER),
-            self.llm_provider.as_ref().unwrap().name
+            "request received: llm provider hint: {}, selected llm: {}, model: {}",
+            self.get_http_request_header(ARCH_PROVIDER_HINT_HEADER)
+                .unwrap_or_default(),
+            self.llm_provider.as_ref().unwrap().name,
+            self.llm_provider
+                .as_ref()
+                .unwrap()
+                .model
+                .as_ref()
+                .unwrap_or(&String::new())
         );
     }
 
@@ -130,7 +157,7 @@ impl StreamContext {
     }
 
     fn send_server_error(&self, error: ServerError, override_status_code: Option<StatusCode>) {
-        debug!("server error occurred: {}", error);
+        warn!("server error occurred: {}", error);
         self.send_http_response(
             override_status_code
                 .unwrap_or(StatusCode::INTERNAL_SERVER_ERROR)
@@ -149,11 +176,11 @@ impl StreamContext {
         // Tokenize and record token count.
         let token_count = tokenizer::token_count(model, json_string).unwrap_or(0);
 
+        debug!("Recorded input token count: {}", token_count);
         // Record the token count to metrics.
         self.metrics
             .input_sequence_length
             .record(token_count as u64);
-        trace!("Recorded input token count: {}", token_count);
 
         // Check if rate limiting needs to be applied.
         if let Some(selector) = self.ratelimit_selector.take() {
@@ -164,7 +191,7 @@ impl StreamContext {
                 NonZero::new(token_count as u32).unwrap(),
             )?;
         } else {
-            trace!("No rate limit applied for model: {}", model);
+            debug!("No rate limit applied for model: {}", model);
         }
 
         Ok(())
@@ -176,29 +203,59 @@ impl HttpContext for StreamContext {
     // Envoy's HTTP model is event driven. The WASM ABI has given implementors events to hook onto
     // the lifecycle of the http request and response.
     fn on_http_request_headers(&mut self, _num_headers: usize, _end_of_stream: bool) -> Action {
-        self.select_llm_provider();
-
-        // if endpoint is not set then use provider name as routing header so envoy can resolve the cluster name
-        if self.llm_provider().endpoint.is_none() {
-            self.add_http_request_header(
-                ARCH_ROUTING_HEADER,
-                &self.llm_provider().provider_interface.to_string(),
-            );
-        } else {
-            self.add_http_request_header(ARCH_ROUTING_HEADER, &self.llm_provider().name);
+        let request_path = self.get_http_request_header(":path").unwrap_or_default();
+        if request_path == HEALTHZ_PATH {
+            self.send_http_response(200, vec![], None);
+            return Action::Continue;
         }
 
-        if let Err(error) = self.modify_auth_headers() {
-            // ensure that the provider has an endpoint if the access key is missing else return a bad request
-            if self.llm_provider.as_ref().unwrap().endpoint.is_none() {
-                self.send_server_error(error, Some(StatusCode::BAD_REQUEST));
+        let routing_header_value = self.get_http_request_header(ARCH_ROUTING_HEADER);
+
+        let use_agent_orchestrator = match self.overrides.as_ref() {
+            Some(overrides) => overrides.use_agent_orchestrator.unwrap_or_default(),
+            None => false,
+        };
+
+        if let Some(routing_header_value) = routing_header_value.as_ref() {
+            info!("routing header already set: {}", routing_header_value);
+            self.llm_provider = Some(Rc::new(LlmProvider {
+                name: routing_header_value.to_string(),
+                provider_interface: LlmProviderType::OpenAI,
+                access_key: None,
+                endpoint: None,
+                model: None,
+                default: None,
+                stream: None,
+                port: None,
+                rate_limits: None,
+            }));
+        } else {
+            self.select_llm_provider();
+            if self.llm_provider().endpoint.is_some() {
+                self.add_http_request_header(
+                    ARCH_ROUTING_HEADER,
+                    &self.llm_provider().name.to_string(),
+                );
+            } else {
+                self.add_http_request_header(
+                    ARCH_ROUTING_HEADER,
+                    &self.llm_provider().provider_interface.to_string(),
+                );
+            }
+            if let Err(error) = self.modify_auth_headers() {
+                // ensure that the provider has an endpoint if the access key is missing else return a bad request
+                if self.llm_provider.as_ref().unwrap().endpoint.is_none() && !use_agent_orchestrator
+                {
+                    self.send_server_error(error, Some(StatusCode::BAD_REQUEST));
+                }
             }
         }
+
         self.delete_content_length_header();
         self.save_ratelimit_header();
 
-        self.is_chat_completions_request =
-            self.get_http_request_header(":path").unwrap_or_default() == CHAT_COMPLETIONS_PATH;
+        let request_path = self.get_http_request_header(":path").unwrap_or_default();
+        self.is_chat_completions_request = CHAT_COMPLETIONS_PATH.contains(&request_path.as_str());
 
         self.request_id = self.get_http_request_header(REQUEST_ID_HEADER);
         self.traceparent = self.get_http_request_header(TRACE_PARENT_HEADER);
@@ -207,6 +264,11 @@ impl HttpContext for StreamContext {
     }
 
     fn on_http_request_body(&mut self, body_size: usize, end_of_stream: bool) -> Action {
+        debug!(
+            "on_http_request_body [S={}] bytes={} end_stream={}",
+            self.context_id, body_size, end_of_stream
+        );
+
         // Let the client send the gateway all the data before sending to the LLM_provider.
         // TODO: consider a streaming API.
 
@@ -222,34 +284,41 @@ impl HttpContext for StreamContext {
             return Action::Continue;
         }
 
+        let body_bytes = match self.get_http_request_body(0, body_size) {
+            Some(body_bytes) => body_bytes,
+            None => {
+                self.send_server_error(
+                    ServerError::LogicError(format!(
+                        "Failed to obtain body bytes even though body_size is {}",
+                        body_size
+                    )),
+                    None,
+                );
+                return Action::Pause;
+            }
+        };
+
         // Deserialize body into spec.
         // Currently OpenAI API.
         let mut deserialized_body: ChatCompletionsRequest =
-            match self.get_http_request_body(0, body_size) {
-                Some(body_bytes) => match serde_json::from_slice(&body_bytes) {
-                    Ok(deserialized) => deserialized,
-                    Err(e) => {
-                        self.send_server_error(
-                            ServerError::Deserialization(e),
-                            Some(StatusCode::BAD_REQUEST),
-                        );
-                        return Action::Pause;
-                    }
-                },
-                None => {
+            match serde_json::from_slice(&body_bytes) {
+                Ok(deserialized) => deserialized,
+                Err(e) => {
+                    debug!(
+                        "on_http_request_body: request body: {}",
+                        String::from_utf8_lossy(&body_bytes)
+                    );
                     self.send_server_error(
-                        ServerError::LogicError(format!(
-                            "Failed to obtain body bytes even though body_size is {}",
-                            body_size
-                        )),
-                        None,
+                        ServerError::Deserialization(e),
+                        Some(StatusCode::BAD_REQUEST),
                     );
                     return Action::Pause;
                 }
             };
 
         // remove metadata from the request body
-        deserialized_body.metadata = None;
+        //TODO: move this to prompt gateway
+        // deserialized_body.metadata = None;
         // delete model key from message array
         for message in deserialized_body.messages.iter_mut() {
             message.model = None;
@@ -262,15 +331,47 @@ impl HttpContext for StreamContext {
             .last()
             .cloned();
 
-        // override model name from the llm provider
-        deserialized_body
-            .model
-            .clone_from(&self.llm_provider.as_ref().unwrap().model);
+        let model_name = match self.llm_provider.as_ref() {
+            Some(llm_provider) => llm_provider.model.as_ref(),
+            None => None,
+        };
+
+        let use_agent_orchestrator = match self.overrides.as_ref() {
+            Some(overrides) => overrides.use_agent_orchestrator.unwrap_or_default(),
+            None => false,
+        };
+
+        let model_requested = deserialized_body.model.clone();
+        if deserialized_body.model.is_empty() || deserialized_body.model.to_lowercase() == "none" {
+            deserialized_body.model = match model_name {
+                Some(model_name) => model_name.clone(),
+                None => {
+                    if use_agent_orchestrator {
+                        "agent_orchestrator".to_string()
+                    } else {
+                        self.send_server_error(
+                          ServerError::BadRequest {
+                              why: format!("No model specified in request and couldn't determine model name from arch_config. Model name in req: {}, arch_config, provider: {}, model: {:?}", deserialized_body.model, self.llm_provider().name, self.llm_provider().model).to_string(),
+                          },
+                          Some(StatusCode::BAD_REQUEST),
+                      );
+                        return Action::Continue;
+                    }
+                }
+            }
+        }
+
+        info!(
+            "on_http_request_body: provider: {}, model requested: {}, model selected: {}",
+            self.llm_provider().name,
+            model_requested,
+            model_name.unwrap_or(&"None".to_string()),
+        );
+
         let chat_completion_request_str = serde_json::to_string(&deserialized_body).unwrap();
 
-        trace!(
-            "arch => {:?}, body: {}",
-            deserialized_body.model,
+        debug!(
+            "on_http_request_body: request body: {}",
             chat_completion_request_str
         );
 
@@ -307,10 +408,9 @@ impl HttpContext for StreamContext {
     }
 
     fn on_http_response_headers(&mut self, _num_headers: usize, _end_of_stream: bool) -> Action {
-        trace!(
+        debug!(
             "on_http_response_headers [S={}] end_stream={}",
-            self.context_id,
-            _end_of_stream
+            self.context_id, _end_of_stream
         );
 
         self.set_property(
@@ -322,15 +422,18 @@ impl HttpContext for StreamContext {
     }
 
     fn on_http_response_body(&mut self, body_size: usize, end_of_stream: bool) -> Action {
-        trace!(
+        debug!(
             "on_http_response_body [S={}] bytes={} end_stream={}",
-            self.context_id,
-            body_size,
-            end_of_stream
+            self.context_id, body_size, end_of_stream
         );
 
+        if self.request_body_sent_time.is_none() {
+            debug!("on_http_response_body: request body not sent, not doing any processing in llm filter");
+            return Action::Continue;
+        }
+
         if !self.is_chat_completions_request {
-            debug!("non-chatcompletion request");
+            info!("on_http_response_body: non-chatcompletion request");
             return Action::Continue;
         }
 
@@ -342,7 +445,7 @@ impl HttpContext for StreamContext {
                 Ok(duration) => {
                     // Convert the duration to milliseconds
                     let duration_ms = duration.as_millis();
-                    debug!("request latency: {}ms", duration_ms);
+                    info!("on_http_response_body: request latency: {}ms", duration_ms);
                     // Record the latency to the latency histogram
                     self.metrics.request_latency.record(duration_ms as u64);
 
@@ -353,7 +456,7 @@ impl HttpContext for StreamContext {
                         // Record the time per output token
                         self.metrics.time_per_output_token.record(tpot);
 
-                        trace!(
+                        debug!(
                             "time per token: {}ms, tokens per second: {}",
                             tpot,
                             1000 / tpot
@@ -381,7 +484,7 @@ impl HttpContext for StreamContext {
                     Ok(traceparent) => {
                         let mut trace_data = common::tracing::TraceData::new();
                         let mut llm_span = Span::new(
-                            "upstream_llm_time".to_string(),
+                            "egress_traffic".to_string(),
                             Some(traceparent.trace_id),
                             Some(traceparent.parent_id),
                             self.request_body_sent_time.unwrap(),
@@ -417,10 +520,9 @@ impl HttpContext for StreamContext {
         let body = if self.streaming_response {
             let chunk_start = 0;
             let chunk_size = body_size;
-            trace!(
-                "streaming response reading, {}..{}",
-                chunk_start,
-                chunk_size
+            debug!(
+                "on_http_response_body: streaming response reading, {}..{}",
+                chunk_start, chunk_size
             );
             let streaming_chunk = match self.get_http_response_body(0, chunk_size) {
                 Some(chunk) => chunk,
@@ -442,7 +544,7 @@ impl HttpContext for StreamContext {
             }
             streaming_chunk
         } else {
-            trace!("non streaming response bytes read: 0:{}", body_size);
+            debug!("non streaming response bytes read: 0:{}", body_size);
             match self.get_http_response_body(0, body_size) {
                 Some(body) => body,
                 None => {
@@ -455,17 +557,21 @@ impl HttpContext for StreamContext {
         let body_utf8 = match String::from_utf8(body) {
             Ok(body_utf8) => body_utf8,
             Err(e) => {
-                debug!("could not convert to utf8: {}", e);
+                warn!("could not convert to utf8: {}", e);
                 return Action::Continue;
             }
         };
 
         if self.streaming_response {
+            if body_utf8 == "data: [DONE]\n" {
+                return Action::Continue;
+            }
+
             let chat_completions_chunk_response_events =
                 match ChatCompletionStreamResponseServerEvents::try_from(body_utf8.as_str()) {
                     Ok(response) => response,
                     Err(e) => {
-                        debug!(
+                        warn!(
                             "invalid streaming response: body str: {}, {:?}",
                             body_utf8, e
                         );
@@ -474,33 +580,27 @@ impl HttpContext for StreamContext {
                 };
 
             if chat_completions_chunk_response_events.events.is_empty() {
-                debug!("empty streaming response");
+                warn!(
+                    "couldn't parse any streaming events: body str: {}",
+                    body_utf8
+                );
                 return Action::Continue;
             }
 
-            let mut model = chat_completions_chunk_response_events
+            let model = chat_completions_chunk_response_events
                 .events
                 .first()
                 .unwrap()
                 .model
                 .clone();
             let tokens_str = chat_completions_chunk_response_events.to_string();
-            //HACK: add support for tokenizing mistral and other models
-            //filed issue https://github.com/katanemo/arch/issues/222
-            if !model.as_ref().unwrap().starts_with("gpt") {
-                warn!(
-                    "tiktoken_rs: unsupported model: {}, using gpt-4 to compute token count",
-                    model.as_ref().unwrap()
-                );
-            }
-            model = Some("gpt-4".to_string());
 
             let token_count =
                 match tokenizer::token_count(model.as_ref().unwrap().as_str(), tokens_str.as_str())
                 {
                     Ok(token_count) => token_count,
                     Err(e) => {
-                        debug!("could not get token count: {:?}", e);
+                        warn!("could not get token count: {:?}", e);
                         return Action::Continue;
                     }
                 };
@@ -514,7 +614,10 @@ impl HttpContext for StreamContext {
                 match current_time.duration_since(self.start_time) {
                     Ok(duration) => {
                         let duration_ms = duration.as_millis();
-                        debug!("time to first token: {}ms", duration_ms);
+                        info!(
+                            "on_http_response_body: time to first token: {}ms",
+                            duration_ms
+                        );
                         self.ttft_duration = Some(duration);
                         self.metrics.time_to_first_token.record(duration_ms as u64);
                     }
@@ -524,12 +627,12 @@ impl HttpContext for StreamContext {
                 }
             }
         } else {
-            trace!("non streaming response");
+            debug!("non streaming response");
             let chat_completions_response: ChatCompletionsResponse =
                 match serde_json::from_str(body_utf8.as_str()) {
                     Ok(de) => de,
                     Err(err) => {
-                        debug!(
+                        info!(
                             "non chat-completion compliant response received err: {}, body: {}",
                             err, body_utf8
                         );
@@ -546,11 +649,9 @@ impl HttpContext for StreamContext {
             }
         }
 
-        trace!(
+        debug!(
             "recv [S={}] total_tokens={} end_stream={}",
-            self.context_id,
-            self.response_tokens,
-            end_of_stream
+            self.context_id, self.response_tokens, end_of_stream
         );
 
         Action::Continue
diff --git a/crates/llm_gateway/tests/integration.rs b/crates/llm_gateway/tests/integration.rs
index 0b28a175..0f94d2d8 100644
--- a/crates/llm_gateway/tests/integration.rs
+++ b/crates/llm_gateway/tests/integration.rs
@@ -18,12 +18,19 @@ fn wasm_module() -> String {
 fn request_headers_expectations(module: &mut Tester, http_context: i32) {
     module
         .call_proxy_on_request_headers(http_context, 0, false)
+        .expect_get_header_map_value(Some(MapType::HttpRequestHeaders), Some(":path"))
+        .returning(Some("/v1/chat/completions"))
+        .expect_get_header_map_value(
+            Some(MapType::HttpRequestHeaders),
+            Some("x-arch-llm-provider"),
+        )
+        .returning(None)
         .expect_get_header_map_value(
             Some(MapType::HttpRequestHeaders),
             Some("x-arch-llm-provider-hint"),
         )
         .returning(None)
-        .expect_log(Some(LogLevel::Debug), Some("request received: llm provider hint: Some(\"default\"), selected llm: open-ai-gpt-4"))
+        .expect_log(Some(LogLevel::Debug), Some("request received: llm provider hint: default, selected llm: open-ai-gpt-4, model: gpt-4"))
         .expect_add_header_map_value(
             Some(MapType::HttpRequestHeaders),
             Some("x-arch-llm-provider"),
@@ -34,6 +41,7 @@ fn request_headers_expectations(module: &mut Tester, http_context: i32) {
             Some("Authorization"),
             Some("Bearer secret_key"),
         )
+        .expect_remove_header_map_value(Some(MapType::HttpRequestHeaders), Some("content-length"))
         .expect_get_header_map_value(
             Some(MapType::HttpRequestHeaders),
             Some("x-arch-llm-provider-hint"),
@@ -46,8 +54,6 @@ fn request_headers_expectations(module: &mut Tester, http_context: i32) {
         .returning(Some("selector-key"))
         .expect_get_header_map_value(Some(MapType::HttpRequestHeaders), Some("selector-key"))
         .returning(Some("selector-value"))
-        .expect_get_header_map_pairs(Some(MapType::HttpRequestHeaders))
-        .returning(None)
         .expect_get_header_map_value(Some(MapType::HttpRequestHeaders), Some(":path"))
         .returning(Some("/v1/chat/completions"))
         .expect_get_header_map_value(Some(MapType::HttpRequestHeaders), Some("x-request-id"))
@@ -217,12 +223,14 @@ fn llm_gateway_successful_request_to_open_ai_chat_completions() {
             chat_completions_request_body.len() as i32,
             true,
         )
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
         .returning(Some(chat_completions_request_body))
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_metric_record("input_sequence_length", 21)
-        .expect_log(Some(LogLevel::Trace), None)
         .expect_log(Some(LogLevel::Debug), None)
         .expect_log(Some(LogLevel::Debug), None)
         .expect_set_buffer_bytes(Some(BufferType::HttpRequestBody), None)
@@ -264,7 +272,7 @@ fn llm_gateway_bad_request_to_open_ai_chat_completions() {
     {\
         \"messages\": [\
         {\
-            \"role\": \"system\",\
+            \"role\": \"system\"\
         },\
         {\
             \"role\": \"user\",\
@@ -282,13 +290,20 @@ fn llm_gateway_bad_request_to_open_ai_chat_completions() {
         .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
         .returning(Some(incomplete_chat_completions_request_body))
         .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), Some("on_http_request_body: provider: open-ai-gpt-4, model requested: , model selected: gpt-4"))
         .expect_send_local_response(
             Some(StatusCode::BAD_REQUEST.as_u16().into()),
             None,
             None,
             None,
         )
-        .execute_and_expect(ReturnType::Action(Action::Pause))
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_metric_record("input_sequence_length", 14)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .execute_and_expect(ReturnType::Action(Action::Continue))
         .unwrap();
 }
 
@@ -337,16 +352,18 @@ fn llm_gateway_request_ratelimited() {
             chat_completions_request_body.len() as i32,
             true,
         )
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
         .returning(Some(chat_completions_request_body))
         // The actual call is not important in this test, we just need to grab the token_id
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_metric_record("input_sequence_length", 107)
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Debug), None)
         .expect_log(Some(LogLevel::Debug), None)
         .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Warn), Some("server error occurred: exceeded limit provider=gpt-4, selector=Header { key: \"selector-key\", value: \"selector-value\" }, tokens_used=107"))
         .expect_send_local_response(
             Some(StatusCode::TOO_MANY_REQUESTS.as_u16().into()),
             None,
@@ -403,13 +420,201 @@ fn llm_gateway_request_not_ratelimited() {
             chat_completions_request_body.len() as i32,
             true,
         )
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
         .returning(Some(chat_completions_request_body))
         // The actual call is not important in this test, we just need to grab the token_id
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_metric_record("input_sequence_length", 29)
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_set_buffer_bytes(Some(BufferType::HttpRequestBody), None)
+        .execute_and_expect(ReturnType::Action(Action::Continue))
+        .unwrap();
+}
+
+#[test]
+#[serial]
+fn llm_gateway_override_model_name() {
+    let args = tester::MockSettings {
+        wasm_path: wasm_module(),
+        quiet: false,
+        allow_unexpected: false,
+    };
+    let mut module = tester::mock(args).unwrap();
+
+    module
+        .call_start()
+        .execute_and_expect(ReturnType::None)
+        .unwrap();
+
+    // Setup Filter
+    let filter_context = setup_filter(&mut module, default_config());
+
+    // Setup HTTP Stream
+    let http_context = 2;
+
+    normal_flow(&mut module, filter_context, http_context);
+
+    // give shorter body to avoid rate limiting
+    let chat_completions_request_body = "\
+{\
+    \"model\": \"o1-mini\",\
+    \"messages\": [\
+    {\
+        \"role\": \"system\",\
+        \"content\": \"You are a poetic assistant, skilled in explaining complex programming concepts with creative flair.\"\
+    },\
+    {\
+        \"role\": \"user\",\
+        \"content\": \"Compose a poem that explains the concept of recursion in programming.\"\
+    }\
+    ]
+}";
+
+    module
+        .call_proxy_on_request_body(
+            http_context,
+            chat_completions_request_body.len() as i32,
+            true,
+        )
+        .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
+        .returning(Some(chat_completions_request_body))
+        // The actual call is not important in this test, we just need to grab the token_id
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), Some("on_http_request_body: provider: open-ai-gpt-4, model requested: o1-mini, model selected: gpt-4"))
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_metric_record("input_sequence_length", 29)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_set_buffer_bytes(Some(BufferType::HttpRequestBody), None)
+        .execute_and_expect(ReturnType::Action(Action::Continue))
+        .unwrap();
+}
+
+#[test]
+#[serial]
+fn llm_gateway_override_use_default_model() {
+    let args = tester::MockSettings {
+        wasm_path: wasm_module(),
+        quiet: false,
+        allow_unexpected: false,
+    };
+    let mut module = tester::mock(args).unwrap();
+
+    module
+        .call_start()
+        .execute_and_expect(ReturnType::None)
+        .unwrap();
+
+    // Setup Filter
+    let filter_context = setup_filter(&mut module, default_config());
+
+    // Setup HTTP Stream
+    let http_context = 2;
+
+    normal_flow(&mut module, filter_context, http_context);
+
+    // give shorter body to avoid rate limiting
+    let chat_completions_request_body = "\
+{\
+    \"messages\": [\
+    {\
+        \"role\": \"system\",\
+        \"content\": \"You are a poetic assistant, skilled in explaining complex programming concepts with creative flair.\"\
+    },\
+    {\
+        \"role\": \"user\",\
+        \"content\": \"Compose a poem that explains the concept of recursion in programming.\"\
+    }\
+    ]
+}";
+
+    module
+        .call_proxy_on_request_body(
+            http_context,
+            chat_completions_request_body.len() as i32,
+            true,
+        )
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
+        .returning(Some(chat_completions_request_body))
+        // The actual call is not important in this test, we just need to grab the token_id
+        .expect_log(
+            Some(LogLevel::Info),
+            Some("on_http_request_body: provider: open-ai-gpt-4, model requested: , model selected: gpt-4"),
+        )
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_metric_record("input_sequence_length", 29)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_set_buffer_bytes(Some(BufferType::HttpRequestBody), None)
+        .execute_and_expect(ReturnType::Action(Action::Continue))
+        .unwrap();
+}
+
+#[test]
+#[serial]
+fn llm_gateway_override_use_model_name_none() {
+    let args = tester::MockSettings {
+        wasm_path: wasm_module(),
+        quiet: false,
+        allow_unexpected: false,
+    };
+    let mut module = tester::mock(args).unwrap();
+
+    module
+        .call_start()
+        .execute_and_expect(ReturnType::None)
+        .unwrap();
+
+    // Setup Filter
+    let filter_context = setup_filter(&mut module, default_config());
+
+    // Setup HTTP Stream
+    let http_context = 2;
+
+    normal_flow(&mut module, filter_context, http_context);
+
+    // give shorter body to avoid rate limiting
+    let chat_completions_request_body = "\
+{\
+    \"model\": \"none\",\
+    \"messages\": [\
+    {\
+        \"role\": \"system\",\
+        \"content\": \"You are a poetic assistant, skilled in explaining complex programming concepts with creative flair.\"\
+    },\
+    {\
+        \"role\": \"user\",\
+        \"content\": \"Compose a poem that explains the concept of recursion in programming.\"\
+    }\
+    ]
+}";
+
+    module
+        .call_proxy_on_request_body(
+            http_context,
+            chat_completions_request_body.len() as i32,
+            true,
+        )
+        .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
+        .returning(Some(chat_completions_request_body))
+        // The actual call is not important in this test, we just need to grab the token_id
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), Some("on_http_request_body: provider: open-ai-gpt-4, model requested: none, model selected: gpt-4"))
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_metric_record("input_sequence_length", 29)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_log(Some(LogLevel::Debug), None)
         .expect_log(Some(LogLevel::Debug), None)
         .expect_set_buffer_bytes(Some(BufferType::HttpRequestBody), None)
diff --git a/crates/prompt_gateway/src/filter_context.rs b/crates/prompt_gateway/src/filter_context.rs
index f782dea2..d5368686 100644
--- a/crates/prompt_gateway/src/filter_context.rs
+++ b/crates/prompt_gateway/src/filter_context.rs
@@ -1,6 +1,8 @@
 use crate::metrics::Metrics;
 use crate::stream_context::StreamContext;
-use common::configuration::{Configuration, Overrides, PromptGuards, PromptTarget, Tracing};
+use common::configuration::{
+    Configuration, Endpoint, Overrides, PromptGuards, PromptTarget, Tracing,
+};
 use common::http::Client;
 use common::stats::Gauge;
 use log::trace;
@@ -21,6 +23,7 @@ pub struct FilterContext {
     overrides: Rc<Option<Overrides>>,
     system_prompt: Rc<Option<String>>,
     prompt_targets: Rc<HashMap<String, PromptTarget>>,
+    endpoints: Rc<Option<HashMap<String, Endpoint>>>,
     prompt_guards: Rc<PromptGuards>,
     tracing: Rc<Option<Tracing>>,
 }
@@ -34,6 +37,7 @@ impl FilterContext {
             prompt_targets: Rc::new(HashMap::new()),
             overrides: Rc::new(None),
             prompt_guards: Rc::new(PromptGuards::default()),
+            endpoints: Rc::new(None),
             tracing: Rc::new(None),
         }
     }
@@ -73,6 +77,7 @@ impl RootContext for FilterContext {
         }
         self.system_prompt = Rc::new(config.system_prompt);
         self.prompt_targets = Rc::new(prompt_targets);
+        self.endpoints = Rc::new(config.endpoints);
 
         if let Some(prompt_guards) = config.prompt_guards {
             self.prompt_guards = Rc::new(prompt_guards)
@@ -94,6 +99,7 @@ impl RootContext for FilterContext {
             Rc::clone(&self.metrics),
             Rc::clone(&self.system_prompt),
             Rc::clone(&self.prompt_targets),
+            Rc::clone(&self.endpoints),
             Rc::clone(&self.overrides),
             Rc::clone(&self.tracing),
         )))
diff --git a/crates/prompt_gateway/src/http_context.rs b/crates/prompt_gateway/src/http_context.rs
index 53a2d25b..505ce942 100644
--- a/crates/prompt_gateway/src/http_context.rs
+++ b/crates/prompt_gateway/src/http_context.rs
@@ -4,17 +4,18 @@ use common::{
         self, ArchState, ChatCompletionStreamResponse, ChatCompletionTool, ChatCompletionsRequest,
     },
     consts::{
-        ARCH_FC_MODEL_NAME, ARCH_INTERNAL_CLUSTER_NAME, ARCH_STATE_HEADER,
+        ARCH_FC_MODEL_NAME, ARCH_INTERNAL_CLUSTER_NAME, ARCH_ROUTING_HEADER,
         ARCH_UPSTREAM_HOST_HEADER, ASSISTANT_ROLE, CHAT_COMPLETIONS_PATH, HEALTHZ_PATH,
         MODEL_SERVER_NAME, MODEL_SERVER_REQUEST_TIMEOUT_MS, REQUEST_ID_HEADER, TOOL_ROLE,
-        TRACE_PARENT_HEADER, USER_ROLE,
+        TRACE_PARENT_HEADER, USER_ROLE, X_ARCH_API_RESPONSE, X_ARCH_FC_MODEL_RESPONSE,
+        X_ARCH_STATE_HEADER, X_ARCH_TOOL_CALL,
     },
     errors::ServerError,
     http::{CallArgs, Client},
     pii::obfuscate_auth_header,
 };
 use http::StatusCode;
-use log::{debug, trace, warn};
+use log::{debug, info, warn};
 use proxy_wasm::{traits::HttpContext, types::Action};
 use serde_json::Value;
 use std::{
@@ -33,15 +34,37 @@ impl HttpContext for StreamContext {
         // manipulate the body in benign ways e.g., compression.
         self.set_http_request_header("content-length", None);
 
+        if let Some(overrides) = self.overrides.as_ref() {
+            if overrides.use_agent_orchestrator.unwrap_or_default() {
+                // get endpoint that has agent_orchestrator set to true
+                if let Some(endpoints) = self.endpoints.as_ref() {
+                    if endpoints.len() == 1 {
+                        let (name, _) = endpoints.iter().next().unwrap();
+                        info!("Setting ARCH_PROVIDER_HINT_HEADER to {}", name);
+                        self.set_http_request_header(ARCH_ROUTING_HEADER, Some(name));
+                    } else {
+                        warn!("Need single endpoint when use_agent_orchestrator is set");
+                        self.send_server_error(
+                            ServerError::LogicError(
+                                "Need single endpoint when use_agent_orchestrator is set"
+                                    .to_string(),
+                            ),
+                            None,
+                        );
+                    }
+                }
+            }
+        }
+
         let request_path = self.get_http_request_header(":path").unwrap_or_default();
         if request_path == HEALTHZ_PATH {
             self.send_http_response(200, vec![], None);
             return Action::Continue;
         }
 
-        self.is_chat_completions_request = request_path == CHAT_COMPLETIONS_PATH;
+        self.is_chat_completions_request = CHAT_COMPLETIONS_PATH.contains(&request_path.as_str());
 
-        trace!(
+        debug!(
             "on_http_request_headers S[{}] req_headers={:?}",
             self.context_id,
             obfuscate_auth_header(&mut self.get_http_request_headers())
@@ -49,6 +72,7 @@ impl HttpContext for StreamContext {
 
         self.request_id = self.get_http_request_header(REQUEST_ID_HEADER);
         self.traceparent = self.get_http_request_header(TRACE_PARENT_HEADER);
+
         Action::Continue
     }
 
@@ -66,10 +90,9 @@ impl HttpContext for StreamContext {
 
         self.request_body_size = body_size;
 
-        trace!(
+        debug!(
             "on_http_request_body S[{}] body_size={}",
-            self.context_id,
-            body_size
+            self.context_id, body_size
         );
 
         let body_bytes = match self.get_http_request_body(0, body_size) {
@@ -86,7 +109,7 @@ impl HttpContext for StreamContext {
             }
         };
 
-        trace!("request body: {}", String::from_utf8_lossy(&body_bytes));
+        debug!("request body: {}", String::from_utf8_lossy(&body_bytes));
 
         // Deserialize body into spec.
         // Currently OpenAI API.
@@ -103,8 +126,8 @@ impl HttpContext for StreamContext {
 
         self.arch_state = match deserialized_body.metadata {
             Some(ref metadata) => {
-                if metadata.contains_key(ARCH_STATE_HEADER) {
-                    let arch_state_str = metadata[ARCH_STATE_HEADER].clone();
+                if metadata.contains_key(X_ARCH_STATE_HEADER) {
+                    let arch_state_str = metadata[X_ARCH_STATE_HEADER].clone();
                     let arch_state: Vec<ArchState> = serde_json::from_str(&arch_state_str).unwrap();
                     Some(arch_state)
                 } else {
@@ -152,11 +175,23 @@ impl HttpContext for StreamContext {
             }
         }
 
+        if let Some(overrides) = self.overrides.as_ref() {
+            if overrides.use_agent_orchestrator.unwrap_or_default() {
+                if metadata.is_none() {
+                    metadata = Some(HashMap::new());
+                }
+                metadata
+                    .as_mut()
+                    .unwrap()
+                    .insert("use_agent_orchestrator".to_string(), "true".to_string());
+            }
+        }
+
         let arch_fc_chat_completion_request = ChatCompletionsRequest {
             messages: deserialized_body.messages.clone(),
             metadata,
             stream: deserialized_body.stream,
-            model: "--".to_string(),
+            model: deserialized_body.model.clone(),
             stream_options: deserialized_body.stream_options.clone(),
             tools: Some(tool_calls),
         };
@@ -171,8 +206,10 @@ impl HttpContext for StreamContext {
             }
         };
 
-        debug!("sending request to model server");
-        trace!("request body: {}", json_data);
+        info!("on_http_request_body: sending request to model server");
+        debug!("request body: {}", json_data);
+
+        let timeout_str = MODEL_SERVER_REQUEST_TIMEOUT_MS.to_string();
 
         let timeout_str = MODEL_SERVER_REQUEST_TIMEOUT_MS.to_string();
 
@@ -213,7 +250,7 @@ impl HttpContext for StreamContext {
         };
 
         if let Err(e) = self.http_call(call_args, call_context) {
-            debug!("http_call failed: {:?}", e);
+            warn!("http_call failed: {:?}", e);
             self.send_server_error(ServerError::HttpDispatch(e), None);
         }
 
@@ -221,7 +258,7 @@ impl HttpContext for StreamContext {
     }
 
     fn on_http_response_headers(&mut self, _num_headers: usize, _end_of_stream: bool) -> Action {
-        trace!(
+        debug!(
             "on_http_response_headers recv [S={}] headers={:?}",
             self.context_id,
             self.get_http_response_headers()
@@ -233,15 +270,13 @@ impl HttpContext for StreamContext {
     }
 
     fn on_http_response_body(&mut self, body_size: usize, end_of_stream: bool) -> Action {
-        trace!(
+        debug!(
             "on_http_response_body: recv [S={}] bytes={} end_stream={}",
-            self.context_id,
-            body_size,
-            end_of_stream
+            self.context_id, body_size, end_of_stream
         );
 
         if !self.is_chat_completions_request {
-            debug!("non-gpt request");
+            info!("non-gpt request");
             return Action::Continue;
         }
 
@@ -280,7 +315,7 @@ impl HttpContext for StreamContext {
 
             streaming_chunk
         } else {
-            debug!("non streaming response bytes read: 0:{}", body_size);
+            info!("non streaming response bytes read: 0:{}", body_size);
             match self.get_http_response_body(0, body_size) {
                 Some(body) => body,
                 None => {
@@ -293,21 +328,21 @@ impl HttpContext for StreamContext {
         let body_utf8 = match String::from_utf8(body) {
             Ok(body_utf8) => body_utf8,
             Err(e) => {
-                debug!("could not convert to utf8: {}", e);
+                info!("could not convert to utf8: {}", e);
                 return Action::Continue;
             }
         };
 
         if self.streaming_response {
-            trace!("streaming response");
+            debug!("streaming response");
 
             if self.tool_calls.is_some() && !self.tool_calls.as_ref().unwrap().is_empty() {
                 let chunks = vec![
                     ChatCompletionStreamResponse::new(
-                        None,
+                        self.arch_fc_response.clone(),
                         Some(ASSISTANT_ROLE.to_string()),
                         Some(ARCH_FC_MODEL_NAME.to_string()),
-                        self.tool_calls.to_owned(),
+                        None,
                     ),
                     ChatCompletionStreamResponse::new(
                         self.tool_call_response.clone(),
@@ -349,25 +384,47 @@ impl HttpContext for StreamContext {
                         *metadata = Value::Object(serde_json::Map::new());
                     }
 
-                    let fc_messages = vec![
-                        self.generate_toll_call_message(),
-                        self.generate_api_response_message(),
-                    ];
+                    let tool_call_message = self.generate_tool_call_message();
+                    let tool_call_message_str = serde_json::to_string(&tool_call_message).unwrap();
+                    metadata.as_object_mut().unwrap().insert(
+                        X_ARCH_TOOL_CALL.to_string(),
+                        serde_json::Value::String(tool_call_message_str),
+                    );
+
+                    let api_response_message = self.generate_api_response_message();
+                    let api_response_message_str =
+                        serde_json::to_string(&api_response_message).unwrap();
+                    metadata.as_object_mut().unwrap().insert(
+                        X_ARCH_API_RESPONSE.to_string(),
+                        serde_json::Value::String(api_response_message_str),
+                    );
+
+                    let fc_messages = vec![tool_call_message, api_response_message];
+
                     let fc_messages_str = serde_json::to_string(&fc_messages).unwrap();
                     let arch_state = HashMap::from([("messages".to_string(), fc_messages_str)]);
                     let arch_state_str = serde_json::to_string(&arch_state).unwrap();
                     metadata.as_object_mut().unwrap().insert(
-                        ARCH_STATE_HEADER.to_string(),
+                        X_ARCH_STATE_HEADER.to_string(),
                         serde_json::Value::String(arch_state_str),
                     );
+
+                    if let Some(arch_fc_response) = self.arch_fc_response.as_ref() {
+                        metadata.as_object_mut().unwrap().insert(
+                            X_ARCH_FC_MODEL_RESPONSE.to_string(),
+                            serde_json::Value::String(
+                                serde_json::to_string(arch_fc_response).unwrap(),
+                            ),
+                        );
+                    }
                     let data_serialized = serde_json::to_string(&data).unwrap();
-                    debug!("archgw <= developer: {}", data_serialized);
+                    info!("archgw <= developer: {}", data_serialized);
                     self.set_http_response_body(0, body_size, data_serialized.as_bytes());
                 };
             }
         }
 
-        trace!("recv [S={}] end_stream={}", self.context_id, end_of_stream);
+        debug!("recv [S={}] end_stream={}", self.context_id, end_of_stream);
 
         Action::Continue
     }
diff --git a/crates/prompt_gateway/src/stream_context.rs b/crates/prompt_gateway/src/stream_context.rs
index d197b3e0..1cd2fa86 100644
--- a/crates/prompt_gateway/src/stream_context.rs
+++ b/crates/prompt_gateway/src/stream_context.rs
@@ -2,20 +2,21 @@ use crate::metrics::Metrics;
 use crate::tools::compute_request_path_body;
 use common::api::open_ai::{
     to_server_events, ArchState, ChatCompletionStreamResponse, ChatCompletionsRequest,
-    ChatCompletionsResponse, Message, ModelServerResponse, ToolCall,
+    ChatCompletionsResponse, Message, ToolCall,
 };
-use common::configuration::{Overrides, PromptTarget, Tracing};
+use common::configuration::{Endpoint, Overrides, PromptTarget, Tracing};
 use common::consts::{
     API_REQUEST_TIMEOUT_MS, ARCH_FC_MODEL_NAME, ARCH_INTERNAL_CLUSTER_NAME,
     ARCH_UPSTREAM_HOST_HEADER, ASSISTANT_ROLE, DEFAULT_TARGET_REQUEST_TIMEOUT_MS, MESSAGES_KEY,
     REQUEST_ID_HEADER, SYSTEM_ROLE, TOOL_ROLE, TRACE_PARENT_HEADER, USER_ROLE,
+    X_ARCH_FC_MODEL_RESPONSE,
 };
 use common::errors::ServerError;
 use common::http::{CallArgs, Client};
 use common::stats::Gauge;
 use derivative::Derivative;
 use http::StatusCode;
-use log::{debug, trace, warn};
+use log::{debug, info, warn};
 use proxy_wasm::traits::*;
 use std::cell::RefCell;
 use std::collections::HashMap;
@@ -46,6 +47,7 @@ pub struct StreamCallContext {
 pub struct StreamContext {
     system_prompt: Rc<Option<String>>,
     pub prompt_targets: Rc<HashMap<String, PromptTarget>>,
+    pub endpoints: Rc<Option<HashMap<String, Endpoint>>>,
     pub overrides: Rc<Option<Overrides>>,
     pub metrics: Rc<Metrics>,
     pub callouts: RefCell<HashMap<u32, StreamCallContext>>,
@@ -63,15 +65,16 @@ pub struct StreamContext {
     pub time_to_first_token: Option<u128>,
     pub traceparent: Option<String>,
     pub _tracing: Rc<Option<Tracing>>,
+    pub arch_fc_response: Option<String>,
 }
 
 impl StreamContext {
-    #[allow(clippy::too_many_arguments)]
     pub fn new(
         context_id: u32,
         metrics: Rc<Metrics>,
         system_prompt: Rc<Option<String>>,
         prompt_targets: Rc<HashMap<String, PromptTarget>>,
+        endpoints: Rc<Option<HashMap<String, Endpoint>>>,
         overrides: Rc<Option<Overrides>>,
         tracing: Rc<Option<Tracing>>,
     ) -> Self {
@@ -80,6 +83,7 @@ impl StreamContext {
             metrics,
             system_prompt,
             prompt_targets,
+            endpoints,
             callouts: RefCell::new(HashMap::new()),
             chat_completions_request: None,
             tool_calls: None,
@@ -95,6 +99,7 @@ impl StreamContext {
             _tracing: tracing,
             start_upstream_llm_request_time: 0,
             time_to_first_token: None,
+            arch_fc_response: None,
         }
     }
 
@@ -125,10 +130,10 @@ impl StreamContext {
         mut callout_context: StreamCallContext,
     ) {
         let body_str = String::from_utf8(body).unwrap();
-        debug!("model server response received");
-        trace!("response body: {}", body_str);
+        info!("on_http_call_response: model server response received");
+        debug!("response body: {}", body_str);
 
-        let model_server_response: ModelServerResponse = match serde_json::from_str(&body_str) {
+        let model_server_response: ChatCompletionsResponse = match serde_json::from_str(&body_str) {
             Ok(arch_fc_response) => arch_fc_response,
             Err(e) => {
                 warn!(
@@ -139,77 +144,122 @@ impl StreamContext {
             }
         };
 
-        let arch_fc_response = match model_server_response {
-            ModelServerResponse::ChatCompletionsResponse(response) => response,
-            ModelServerResponse::ModelServerErrorResponse(response) => {
-                debug!("archgw <= modelserver error response: {}", response.result);
-                if response.result == "No intent matched" {
-                    if let Some(default_prompt_target) = self
-                        .prompt_targets
-                        .values()
-                        .find(|pt| pt.default.unwrap_or(false))
-                    {
-                        debug!("default prompt target found, forwarding request to default prompt target");
-                        let endpoint = default_prompt_target.endpoint.clone().unwrap();
-                        let upstream_path: String = endpoint.path.unwrap_or(String::from("/"));
+        let intent_matched = check_intent_matched(&model_server_response);
+        info!("intent matched: {}", intent_matched);
 
-                        let upstream_endpoint = endpoint.name;
-                        let mut params = HashMap::new();
-                        params.insert(
-                            MESSAGES_KEY.to_string(),
-                            callout_context.request_body.messages.clone(),
-                        );
-                        let arch_messages_json = serde_json::to_string(&params).unwrap();
-                        let timeout_str = DEFAULT_TARGET_REQUEST_TIMEOUT_MS.to_string();
+        self.arch_fc_response = model_server_response
+            .metadata
+            .as_ref()
+            .and_then(|metadata| metadata.get(X_ARCH_FC_MODEL_RESPONSE))
+            .cloned();
 
-                        let mut headers = vec![
-                            (":method", "POST"),
-                            (ARCH_UPSTREAM_HOST_HEADER, &upstream_endpoint),
-                            (":path", &upstream_path),
-                            (":authority", &upstream_endpoint),
-                            ("content-type", "application/json"),
-                            ("x-envoy-max-retries", "3"),
-                            ("x-envoy-upstream-rq-timeout-ms", timeout_str.as_str()),
-                        ];
+        if !intent_matched {
+            // check if we have a default prompt target
+            if let Some(default_prompt_target) = self
+                .prompt_targets
+                .values()
+                .find(|pt| pt.default.unwrap_or(false))
+            {
+                info!("default prompt target found, forwarding request to default prompt target");
+                let endpoint = default_prompt_target.endpoint.clone().unwrap();
+                let upstream_path: String = endpoint.path.unwrap_or(String::from("/"));
 
-                        if self.request_id.is_some() {
-                            headers.push((REQUEST_ID_HEADER, self.request_id.as_ref().unwrap()));
-                        }
+                let upstream_endpoint = endpoint.name;
+                let mut params = HashMap::new();
+                params.insert(
+                    MESSAGES_KEY.to_string(),
+                    callout_context.request_body.messages.clone(),
+                );
+                let arch_messages_json = serde_json::to_string(&params).unwrap();
+                let timeout_str = DEFAULT_TARGET_REQUEST_TIMEOUT_MS.to_string();
 
-                        // if self.trace_arch_internal() && self.traceparent.is_some() {
-                        //     headers.push((TRACE_PARENT_HEADER, self.traceparent.as_ref().unwrap()));
-                        // }
+                let mut headers = vec![
+                    (":method", "POST"),
+                    (ARCH_UPSTREAM_HOST_HEADER, &upstream_endpoint),
+                    (":path", &upstream_path),
+                    (":authority", &upstream_endpoint),
+                    ("content-type", "application/json"),
+                    ("x-envoy-max-retries", "3"),
+                    ("x-envoy-upstream-rq-timeout-ms", timeout_str.as_str()),
+                ];
 
-                        let call_args = CallArgs::new(
-                            ARCH_INTERNAL_CLUSTER_NAME,
-                            &upstream_path,
-                            headers,
-                            Some(arch_messages_json.as_bytes()),
-                            vec![],
-                            Duration::from_secs(5),
-                        );
-                        callout_context.response_handler_type = ResponseHandlerType::DefaultTarget;
-                        callout_context.prompt_target_name =
-                            Some(default_prompt_target.name.clone());
+                if self.request_id.is_some() {
+                    headers.push((REQUEST_ID_HEADER, self.request_id.as_ref().unwrap()));
+                }
 
-                        if let Err(e) = self.http_call(call_args, callout_context) {
-                            warn!("error dispatching default prompt target request: {}", e);
-                            return self.send_server_error(
-                                ServerError::HttpDispatch(e),
-                                Some(StatusCode::BAD_REQUEST),
-                            );
-                        }
-                        return;
+                let call_args = CallArgs::new(
+                    ARCH_INTERNAL_CLUSTER_NAME,
+                    &upstream_path,
+                    headers,
+                    Some(arch_messages_json.as_bytes()),
+                    vec![],
+                    Duration::from_secs(5),
+                );
+                callout_context.response_handler_type = ResponseHandlerType::DefaultTarget;
+                callout_context.prompt_target_name = Some(default_prompt_target.name.clone());
+
+                if let Err(e) = self.http_call(call_args, callout_context) {
+                    warn!("error dispatching default prompt target request: {}", e);
+                    return self.send_server_error(
+                        ServerError::HttpDispatch(e),
+                        Some(StatusCode::BAD_REQUEST),
+                    );
+                }
+                return;
+            } else {
+                info!("no default prompt target found, forwarding request to upstream llm");
+                let mut messages = Vec::new();
+                // add system prompt
+                match self.system_prompt.as_ref() {
+                    None => {}
+                    Some(system_prompt) => {
+                        let system_prompt_message = Message {
+                            role: SYSTEM_ROLE.to_string(),
+                            content: Some(system_prompt.clone()),
+                            model: None,
+                            tool_calls: None,
+                            tool_call_id: None,
+                        };
+                        messages.push(system_prompt_message);
                     }
                 }
-                return self.send_server_error(
-                    ServerError::LogicError(response.result),
-                    Some(StatusCode::BAD_REQUEST),
-                );
-            }
-        };
 
-        arch_fc_response.choices[0]
+                messages.append(
+                    &mut self
+                        .filter_out_arch_messages(callout_context.request_body.messages.as_ref()),
+                );
+
+                let chat_completion_request = ChatCompletionsRequest {
+                    model: self
+                        .chat_completions_request
+                        .as_ref()
+                        .unwrap()
+                        .model
+                        .clone(),
+                    messages,
+                    tools: None,
+                    stream: callout_context.request_body.stream,
+                    stream_options: callout_context.request_body.stream_options,
+                    metadata: None,
+                };
+
+                let chat_completion_request_json =
+                    serde_json::to_string(&chat_completion_request).unwrap();
+                info!(
+                    "archgw => upstream llm request: {}",
+                    chat_completion_request_json
+                );
+                self.set_http_request_body(
+                    0,
+                    self.request_body_size,
+                    chat_completion_request_json.as_bytes(),
+                );
+                self.resume_http_request();
+                return;
+            }
+        }
+
+        model_server_response.choices[0]
             .message
             .tool_calls
             .clone_into(&mut self.tool_calls);
@@ -231,14 +281,14 @@ impl StreamContext {
             let direct_response_str = if self.streaming_response {
                 let chunks = vec![
                     ChatCompletionStreamResponse::new(
-                        None,
+                        self.arch_fc_response.clone(),
                         Some(ASSISTANT_ROLE.to_string()),
-                        Some(ARCH_FC_MODEL_NAME.to_owned()),
+                        Some(ARCH_FC_MODEL_NAME.to_string()),
                         None,
                     ),
                     ChatCompletionStreamResponse::new(
                         Some(
-                            arch_fc_response.choices[0]
+                            model_server_response.choices[0]
                                 .message
                                 .content
                                 .as_ref()
@@ -246,7 +296,7 @@ impl StreamContext {
                                 .clone(),
                         ),
                         None,
-                        Some(ARCH_FC_MODEL_NAME.to_owned()),
+                        Some(format!("{}-Chat", ARCH_FC_MODEL_NAME.to_owned())),
                         None,
                     ),
                 ];
@@ -268,12 +318,59 @@ impl StreamContext {
         callout_context.prompt_target_name =
             Some(self.tool_calls.as_ref().unwrap()[0].function.name.clone());
 
+        if let Some(overrides) = self.overrides.as_ref() {
+            if overrides.use_agent_orchestrator.unwrap_or_default() {
+                let mut metadata = HashMap::new();
+                metadata.insert("use_agent_orchestrator".to_string(), "true".to_string());
+
+                metadata.insert(
+                    "agent-name".to_string(),
+                    callout_context
+                        .prompt_target_name
+                        .as_ref()
+                        .unwrap()
+                        .to_string(),
+                );
+
+                if let Some(overrides) = self.overrides.as_ref() {
+                    if overrides.optimize_context_window.unwrap_or_default() {
+                        metadata.insert("optimize_context_window".to_string(), "true".to_string());
+                    }
+                }
+
+                if let Some(overrides) = self.overrides.as_ref() {
+                    if overrides.use_agent_orchestrator.unwrap_or_default() {
+                        metadata.insert("use_agent_orchestrator".to_string(), "true".to_string());
+                    }
+                }
+
+                let messages = self.construct_llm_messages(&callout_context);
+
+                let chat_completion_request = ChatCompletionsRequest {
+                    model: callout_context.request_body.model.clone(),
+                    messages,
+                    tools: None,
+                    stream: callout_context.request_body.stream,
+                    stream_options: callout_context.request_body.stream_options.clone(),
+                    metadata: Some(metadata),
+                };
+
+                let body_str = serde_json::to_string(&chat_completion_request).unwrap();
+                info!("sending request to llm agent: {}", body_str);
+                self.set_http_request_body(0, self.request_body_size, body_str.as_bytes());
+                self.resume_http_request();
+                return;
+            }
+        }
+
         self.schedule_api_call_request(callout_context);
     }
 
     fn schedule_api_call_request(&mut self, mut callout_context: StreamCallContext) {
+        // Construct messages early to avoid mutable borrow conflicts
+
         let tools_call_name = self.tool_calls.as_ref().unwrap()[0].function.name.clone();
-        let prompt_target = self.prompt_targets.get(&tools_call_name).unwrap();
+        let prompt_target = self.prompt_targets.get(&tools_call_name).unwrap().clone();
         let tool_params = &self.tool_calls.as_ref().unwrap()[0].function.arguments;
         let endpoint_details = prompt_target.endpoint.as_ref().unwrap();
         let endpoint_path: String = endpoint_details
@@ -285,7 +382,7 @@ impl StreamContext {
         let http_method = endpoint_details.method.clone().unwrap_or_default();
         let prompt_target_params = prompt_target.parameters.clone().unwrap_or_default();
 
-        let (path, body) = match compute_request_path_body(
+        let (path, api_call_body) = match compute_request_path_body(
             &endpoint_path,
             tool_params,
             &prompt_target_params,
@@ -302,6 +399,8 @@ impl StreamContext {
             }
         };
 
+        debug!("on_http_call_response: api call body {:?}", api_call_body);
+
         let timeout_str = API_REQUEST_TIMEOUT_MS.to_string();
 
         let http_method_str = http_method.to_string();
@@ -335,13 +434,13 @@ impl StreamContext {
             ARCH_INTERNAL_CLUSTER_NAME,
             &path,
             headers.into_iter().collect(),
-            body.as_deref().map(|s| s.as_bytes()),
+            api_call_body.as_deref().map(|s| s.as_bytes()),
             vec![],
             Duration::from_secs(5),
         );
 
-        debug!(
-            "dispatching api call to developer endpoint: {}, path: {}, method: {}",
+        info!(
+            "on_http_call_response: dispatching api call to developer endpoint: {}, path: {}, method: {}",
             endpoint_details.name, path, http_method_str
         );
 
@@ -358,10 +457,15 @@ impl StreamContext {
         let http_status = self
             .get_http_call_response_header(":status")
             .unwrap_or(StatusCode::OK.as_str().to_string());
-        debug!(
-            "developer api call response received: status code: {}",
+        info!(
+            "on_http_call_response: developer api call response received: status code: {}",
             http_status
         );
+        let prompt_target = self
+            .prompt_targets
+            .get(callout_context.prompt_target_name.as_ref().unwrap())
+            .unwrap()
+            .clone();
         if http_status != StatusCode::OK.as_str() {
             warn!(
                 "api server responded with non 2xx status code: {}",
@@ -378,7 +482,7 @@ impl StreamContext {
             );
         }
         self.tool_call_response = Some(String::from_utf8(body).unwrap());
-        trace!(
+        debug!(
             "response body: {}",
             self.tool_call_response.as_ref().unwrap()
         );
@@ -397,6 +501,37 @@ impl StreamContext {
             }
         };
 
+        if !prompt_target.auto_llm_dispatch_on_response.unwrap_or(true) {
+            let tool_call_response = self.tool_call_response.as_ref().unwrap().clone();
+
+            let direct_response_str = if self.streaming_response {
+                let chunks = vec![
+                    ChatCompletionStreamResponse::new(
+                        None,
+                        Some(ASSISTANT_ROLE.to_string()),
+                        Some(ARCH_FC_MODEL_NAME.to_owned()),
+                        None,
+                    ),
+                    ChatCompletionStreamResponse::new(
+                        Some(tool_call_response.clone()),
+                        None,
+                        Some(ARCH_FC_MODEL_NAME.to_owned()),
+                        None,
+                    ),
+                ];
+
+                to_server_events(chunks)
+            } else {
+                tool_call_response
+            };
+
+            return self.send_http_response(
+                StatusCode::OK.as_u16().into(),
+                vec![],
+                Some(direct_response_str.as_bytes()),
+            );
+        }
+
         let final_prompt = format!(
             "{}\ncontext: {}",
             user_message.content.unwrap(),
@@ -429,8 +564,8 @@ impl StreamContext {
                 return self.send_server_error(ServerError::Serialization(e), None);
             }
         };
-        debug!("sending request to upstream llm");
-        trace!("request body: {}", llm_request_str);
+        info!("on_http_call_response: sending request to upstream llm");
+        debug!("request body: {}", llm_request_str);
 
         self.start_upstream_llm_request_time = SystemTime::now()
             .duration_since(UNIX_EPOCH)
@@ -491,13 +626,24 @@ impl StreamContext {
         messages
     }
 
-    pub fn generate_toll_call_message(&mut self) -> Message {
-        Message {
-            role: ASSISTANT_ROLE.to_string(),
-            content: None,
-            model: Some(ARCH_FC_MODEL_NAME.to_string()),
-            tool_calls: self.tool_calls.clone(),
-            tool_call_id: None,
+    pub fn generate_tool_call_message(&mut self) -> Message {
+        if self.arch_fc_response.is_none() {
+            info!("arch_fc_response is none, generating tool call message");
+            Message {
+                role: ASSISTANT_ROLE.to_string(),
+                content: None,
+                model: Some(ARCH_FC_MODEL_NAME.to_string()),
+                tool_calls: self.tool_calls.clone(),
+                tool_call_id: None,
+            }
+        } else {
+            Message {
+                role: ASSISTANT_ROLE.to_string(),
+                content: self.arch_fc_response.as_ref().cloned(),
+                model: Some(ARCH_FC_MODEL_NAME.to_string()),
+                tool_calls: None,
+                tool_call_id: None,
+            }
         }
     }
 
@@ -519,10 +665,7 @@ impl StreamContext {
             .clone();
 
         // check if the default target should be dispatched to the LLM provider
-        if !prompt_target
-            .auto_llm_dispatch_on_response
-            .unwrap_or_default()
-        {
+        if !prompt_target.auto_llm_dispatch_on_response.unwrap_or(true) {
             let default_target_response_str = if self.streaming_response {
                 let chat_completion_response =
                     match serde_json::from_slice::<ChatCompletionsResponse>(&body) {
@@ -626,12 +769,29 @@ impl StreamContext {
         };
 
         let json_resp = serde_json::to_string(&chat_completion_request).unwrap();
-        debug!("archgw => (default target) llm request: {}", json_resp);
+        info!("archgw => (default target) llm request: {}", json_resp);
         self.set_http_request_body(0, self.request_body_size, json_resp.as_bytes());
         self.resume_http_request();
     }
 }
 
+fn check_intent_matched(model_server_response: &ChatCompletionsResponse) -> bool {
+    let content = model_server_response
+        .choices.first()
+        .and_then(|choice| choice.message.content.as_ref());
+
+    let content_has_value = content.is_some() && !content.unwrap().is_empty();
+
+    let tool_calls = model_server_response
+        .choices.first()
+        .and_then(|choice| choice.message.tool_calls.as_ref());
+
+    // intent was matched if content has some value or tool_calls is empty
+
+
+    content_has_value || (tool_calls.is_some() && !tool_calls.unwrap().is_empty())
+}
+
 impl Client for StreamContext {
     type CallContext = StreamCallContext;
 
@@ -643,3 +803,77 @@ impl Client for StreamContext {
         &self.metrics.active_http_calls
     }
 }
+
+#[cfg(test)]
+mod test {
+    use common::api::open_ai::{ChatCompletionsResponse, Choice, Message, ToolCall};
+
+    use crate::stream_context::check_intent_matched;
+
+    #[test]
+    fn test_intent_matched() {
+        let model_server_response = ChatCompletionsResponse {
+            choices: vec![Choice {
+                message: Message {
+                    content: Some("".to_string()),
+                    tool_calls: Some(vec![]),
+                    role: "assistant".to_string(),
+                    model: None,
+                    tool_call_id: None,
+                },
+                finish_reason: None,
+                index: None,
+            }],
+            usage: None,
+            model: "arch-fc".to_string(),
+            metadata: None,
+        };
+
+        assert!(!check_intent_matched(&model_server_response));
+
+        let model_server_response = ChatCompletionsResponse {
+            choices: vec![Choice {
+                message: Message {
+                    content: Some("hello".to_string()),
+                    tool_calls: Some(vec![]),
+                    role: "assistant".to_string(),
+                    model: None,
+                    tool_call_id: None,
+                },
+                finish_reason: None,
+                index: None,
+            }],
+            usage: None,
+            model: "arch-fc".to_string(),
+            metadata: None,
+        };
+
+        assert!(check_intent_matched(&model_server_response));
+
+        let model_server_response = ChatCompletionsResponse {
+            choices: vec![Choice {
+                message: Message {
+                    content: Some("".to_string()),
+                    tool_calls: Some(vec![ToolCall {
+                        id: "1".to_string(),
+                        function: common::api::open_ai::FunctionCallDetail {
+                            name: "test".to_string(),
+                            arguments: None,
+                        },
+                        tool_type: common::api::open_ai::ToolType::Function,
+                    }]),
+                    role: "assistant".to_string(),
+                    model: None,
+                    tool_call_id: None,
+                },
+                finish_reason: None,
+                index: None,
+            }],
+            usage: None,
+            model: "arch-fc".to_string(),
+            metadata: None,
+        };
+
+        assert!(check_intent_matched(&model_server_response));
+    }
+}
diff --git a/crates/prompt_gateway/src/tools.rs b/crates/prompt_gateway/src/tools.rs
index b3b3d07e..c909a2dd 100644
--- a/crates/prompt_gateway/src/tools.rs
+++ b/crates/prompt_gateway/src/tools.rs
@@ -4,8 +4,13 @@ use std::collections::HashMap;
 use serde_yaml::Value;
 
 // only add params that are of string, number and bool type
-pub fn filter_tool_params(tool_params: &HashMap<String, Value>) -> HashMap<String, String> {
+pub fn filter_tool_params(tool_params: &Option<HashMap<String, Value>>) -> HashMap<String, String> {
+    if tool_params.is_none() {
+        return HashMap::new();
+    }
     tool_params
+        .as_ref()
+        .unwrap()
         .iter()
         .filter(|(_, value)| value.is_number() || value.is_string() || value.is_bool())
         .map(|(key, value)| match value {
@@ -22,7 +27,7 @@ pub fn filter_tool_params(tool_params: &HashMap<String, Value>) -> HashMap<Strin
 
 pub fn compute_request_path_body(
     endpoint_path: &str,
-    tool_params: &HashMap<String, Value>,
+    tool_params: &Option<HashMap<String, Value>>,
     prompt_target_params: &[Parameter],
     http_method: &HttpMethod,
 ) -> Result<(String, Option<String>), String> {
diff --git a/crates/prompt_gateway/tests/integration.rs b/crates/prompt_gateway/tests/integration.rs
index 4af98166..91b36c01 100644
--- a/crates/prompt_gateway/tests/integration.rs
+++ b/crates/prompt_gateway/tests/integration.rs
@@ -24,12 +24,12 @@ fn wasm_module() -> String {
 fn request_headers_expectations(module: &mut Tester, http_context: i32) {
     module
         .call_proxy_on_request_headers(http_context, 0, false)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_remove_header_map_value(Some(MapType::HttpRequestHeaders), Some("content-length"))
         .expect_get_header_map_value(Some(MapType::HttpRequestHeaders), Some(":path"))
         .returning(Some("/v1/chat/completions"))
         .expect_get_header_map_pairs(Some(MapType::HttpRequestHeaders))
         .returning(None)
-        .expect_log(Some(LogLevel::Trace), None)
         .expect_get_header_map_value(Some(MapType::HttpRequestHeaders), Some("x-request-id"))
         .returning(None)
         .expect_get_header_map_value(Some(MapType::HttpRequestHeaders), Some("traceparent"))
@@ -69,10 +69,14 @@ fn normal_flow(module: &mut Tester, filter_context: i32, http_context: i32) {
             chat_completions_request_body.len() as i32,
             true,
         )
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
         .returning(Some(chat_completions_request_body))
         // The actual call is not important in this test, we just need to grab the token_id
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_http_call(
             Some("arch_internal"),
             Some(vec![
@@ -81,16 +85,13 @@ fn normal_flow(module: &mut Tester, filter_context: i32, http_context: i32) {
                 (":path", "/function_calling"),
                 ("content-type", "application/json"),
                 (":authority", "model_server"),
+                ("x-envoy-upstream-rq-timeout-ms", "30000"),
             ]),
             None,
             None,
-            None,
+            Some(5000),
         )
         .returning(Some(1))
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Debug), None)
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
         .expect_metric_increment("active_http_calls", 1)
         .execute_and_expect(ReturnType::Action(Action::Pause))
         .unwrap();
@@ -232,13 +233,13 @@ fn prompt_gateway_successful_request_to_open_ai_chat_completions() {
             chat_completions_request_body.len() as i32,
             true,
         )
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
         .returning(Some(chat_completions_request_body))
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
         .expect_log(Some(LogLevel::Debug), None)
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_http_call(Some("arch_internal"), None, None, None, None)
         .returning(Some(4))
         .expect_metric_increment("active_http_calls", 1)
@@ -295,16 +296,16 @@ fn prompt_gateway_bad_request_to_open_ai_chat_completions() {
             incomplete_chat_completions_request_body.len() as i32,
             true,
         )
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_get_buffer_bytes(Some(BufferType::HttpRequestBody))
         .returning(Some(incomplete_chat_completions_request_body))
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Debug), None)
         .expect_send_local_response(
             Some(StatusCode::BAD_REQUEST.as_u16().into()),
             None,
             None,
             None,
         )
-        .expect_log(Some(LogLevel::Trace), None)
         .execute_and_expect(ReturnType::Action(Action::Pause))
         .unwrap();
 }
@@ -351,10 +352,10 @@ fn prompt_gateway_request_to_llm_gateway() {
                     tool_type: ToolType::Function,
                     function: FunctionCallDetail {
                         name: String::from("weather_forecast"),
-                        arguments: HashMap::from([(
+                        arguments: Some(HashMap::from([(
                             String::from("city"),
                             Value::String(String::from("seattle")),
-                        )]),
+                        )])),
                     },
                 }]),
                 model: None,
@@ -362,7 +363,11 @@ fn prompt_gateway_request_to_llm_gateway() {
             },
         }],
         model: String::from("test"),
-        metadata: None,
+        metadata: {
+            let mut map: HashMap<String, String> = HashMap::new();
+            map.insert("function_latency".to_string(), "0.0".to_string());
+            Some(map)
+        },
     };
 
     let expected_body = "{\"city\":\"seattle\"}";
@@ -373,27 +378,30 @@ fn prompt_gateway_request_to_llm_gateway() {
         .expect_get_buffer_bytes(Some(BufferType::HttpCallResponseBody))
         .returning(Some(&arch_fc_resp_str))
         .expect_log(Some(LogLevel::Warn), None)
+        .expect_log(Some(LogLevel::Info), None)
         .expect_log(Some(LogLevel::Debug), None)
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), None)
         .expect_log(Some(LogLevel::Debug), None)
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
         .expect_http_call(
             Some("arch_internal"),
             Some(vec![
-                (":method", "POST"),
-                ("content-type", "application/json"),
-                ("x-arch-upstream", "api_server"),
-                (":authority", "api_server"),
                 ("x-envoy-max-retries", "3"),
+                ("x-arch-upstream", "api_server"),
+                ("content-type", "application/json"),
+                ("x-envoy-upstream-rq-timeout-ms", "30000"),
                 (":path", "/weather"),
+                (":method", "POST"),
+                (":authority", "api_server"),
             ]),
             Some(expected_body),
             None,
-            None,
+            Some(5000),
         )
         .returning(Some(2))
         .expect_metric_increment("active_http_calls", 1)
+        .expect_log(Some(LogLevel::Trace), None)
         .execute_and_expect(ReturnType::None)
         .unwrap();
 
@@ -403,14 +411,14 @@ fn prompt_gateway_request_to_llm_gateway() {
         .expect_metric_increment("active_http_calls", -1)
         .expect_get_buffer_bytes(Some(BufferType::HttpCallResponseBody))
         .returning(Some(&body_text))
+        .expect_log(Some(LogLevel::Info), None)
         .expect_log(Some(LogLevel::Debug), None)
-        .expect_log(Some(LogLevel::Trace), None)
+        .expect_log(Some(LogLevel::Info), None)
         .expect_log(Some(LogLevel::Debug), None)
-        .expect_log(Some(LogLevel::Trace), None)
-        .expect_log(Some(LogLevel::Trace), None)
         .expect_get_header_map_value(Some(MapType::HttpCallResponseHeaders), Some(":status"))
         .returning(Some("200"))
         .expect_set_buffer_bytes(Some(BufferType::HttpRequestBody), None)
+        .expect_log(Some(LogLevel::Debug), None)
         .execute_and_expect(ReturnType::None)
         .unwrap();
 
@@ -442,11 +450,241 @@ fn prompt_gateway_request_to_llm_gateway() {
         )
         .expect_get_buffer_bytes(Some(BufferType::HttpResponseBody))
         .returning(Some(chat_completion_response_str.as_str()))
-        .expect_log(Some(LogLevel::Trace), None)
         .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), None)
         .expect_set_buffer_bytes(Some(BufferType::HttpResponseBody), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Info), None)
         .expect_log(Some(LogLevel::Debug), None)
-        .expect_log(Some(LogLevel::Trace), None)
         .execute_and_expect(ReturnType::Action(Action::Continue))
         .unwrap();
 }
+
+#[test]
+#[serial]
+fn prompt_gateway_request_no_intent_match() {
+    let args = tester::MockSettings {
+        wasm_path: wasm_module(),
+        quiet: false,
+        allow_unexpected: false,
+    };
+    let mut module = tester::mock(args).unwrap();
+
+    module
+        .call_start()
+        .execute_and_expect(ReturnType::None)
+        .unwrap();
+
+    // Setup Filter
+    let mut config: Configuration = serde_yaml::from_str(default_config()).unwrap();
+    config.ratelimits.as_mut().unwrap()[0].limit.tokens += 1000;
+    let config_str = serde_json::to_string(&config).unwrap();
+
+    let filter_context = setup_filter(&mut module, &config_str);
+
+    // Setup HTTP Stream
+    let http_context = 2;
+
+    normal_flow(&mut module, filter_context, http_context);
+
+    let arch_fc_resp = ChatCompletionsResponse {
+        usage: Some(Usage {
+            completion_tokens: 0,
+        }),
+        choices: vec![Choice {
+            finish_reason: Some("test".to_string()),
+            index: Some(0),
+            message: Message {
+                role: "assistant".to_string(),
+                content: None,
+                tool_calls: None,
+                model: None,
+                tool_call_id: None,
+            },
+        }],
+        model: String::from("test"),
+        metadata: None,
+    };
+
+    let arch_fc_resp_str = serde_json::to_string(&arch_fc_resp).unwrap();
+    module
+        .call_proxy_on_http_call_response(http_context, 1, 0, arch_fc_resp_str.len() as i32, 0)
+        .expect_metric_increment("active_http_calls", -1)
+        .expect_get_buffer_bytes(Some(BufferType::HttpCallResponseBody))
+        .returning(Some(&arch_fc_resp_str))
+        .expect_log(Some(LogLevel::Warn), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), Some("intent matched: false"))
+        .expect_log(
+            Some(LogLevel::Info),
+            Some("no default prompt target found, forwarding request to upstream llm"),
+        )
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_set_buffer_bytes(Some(BufferType::HttpRequestBody), None)
+        .execute_and_expect(ReturnType::None)
+        .unwrap();
+}
+
+fn arch_config_default_target() -> &'static str {
+    r#"
+version: "0.1-beta"
+
+listener:
+  address: 0.0.0.0
+  port: 10000
+  message_format: huggingface
+  connect_timeout: 0.005s
+
+endpoints:
+  api_server:
+    endpoint: api_server:80
+    connect_timeout: 0.005s
+
+llm_providers:
+  - name: open-ai-gpt-4
+    provider_interface: openai
+    access_key: secret_key
+    model: gpt-4
+    default: true
+
+overrides:
+  # confidence threshold for prompt target intent matching
+  prompt_target_intent_matching_threshold: 0.0
+
+system_prompt: |
+  You are a helpful assistant.
+
+prompt_guards:
+  input_guards:
+    jailbreak:
+      on_exception:
+        message: "Looks like you're curious about my abilities, but I can only provide assistance within my programmed parameters."
+
+prompt_targets:
+  - name: weather_forecast
+    description: This function provides realtime weather forecast information for a given city.
+    parameters:
+      - name: city
+        required: true
+        description: The city for which the weather forecast is requested.
+      - name: days
+        description: The number of days for which the weather forecast is requested.
+      - name: units
+        description: The units in which the weather forecast is requested.
+    endpoint:
+      name: api_server
+      path: /weather
+      http_method: POST
+    system_prompt: |
+      You are a helpful weather forecaster. Use weater data that is provided to you. Please following following guidelines when responding to user queries:
+      - Use farenheight for temperature
+      - Use miles per hour for wind speed
+
+  - name: default_target
+    default: true
+    description: This is the default target for all unmatched prompts.
+    endpoint:
+      name: weather_forecast_service
+      path: /default_target
+      http_method: POST
+    system_prompt: |
+      You are a helpful assistant! Summarize the user's request and provide a helpful response.
+    # if it is set to false arch will send response that it received from this prompt target to the user
+    # if true arch will forward the response to the default LLM
+    auto_llm_dispatch_on_response: false
+
+ratelimits:
+  - model: gpt-4
+    selector:
+      key: selector-key
+      value: selector-value
+    limit:
+      tokens: 1
+      unit: minute
+"#
+}
+
+#[test]
+#[serial]
+fn prompt_gateway_request_no_intent_match_default_target() {
+    let args = tester::MockSettings {
+        wasm_path: wasm_module(),
+        quiet: false,
+        allow_unexpected: false,
+    };
+    let mut module = tester::mock(args).unwrap();
+
+    module
+        .call_start()
+        .execute_and_expect(ReturnType::None)
+        .unwrap();
+
+    // Setup Filter
+    let mut config: Configuration = serde_yaml::from_str(arch_config_default_target()).unwrap();
+    config.ratelimits.as_mut().unwrap()[0].limit.tokens += 1000;
+    let config_str = serde_json::to_string(&config).unwrap();
+
+    let filter_context = setup_filter(&mut module, &config_str);
+
+    // Setup HTTP Stream
+    let http_context = 2;
+
+    normal_flow(&mut module, filter_context, http_context);
+
+    let arch_fc_resp = ChatCompletionsResponse {
+        usage: Some(Usage {
+            completion_tokens: 0,
+        }),
+        choices: vec![Choice {
+            finish_reason: Some("test".to_string()),
+            index: Some(0),
+            message: Message {
+                role: "system".to_string(),
+                content: None,
+                tool_calls: None,
+                model: None,
+                tool_call_id: None,
+            },
+        }],
+        model: String::from("test"),
+        metadata: None,
+    };
+
+    let arch_fc_resp_str = serde_json::to_string(&arch_fc_resp).unwrap();
+    module
+        .call_proxy_on_http_call_response(http_context, 1, 0, arch_fc_resp_str.len() as i32, 0)
+        .expect_metric_increment("active_http_calls", -1)
+        .expect_get_buffer_bytes(Some(BufferType::HttpCallResponseBody))
+        .returning(Some(&arch_fc_resp_str))
+        .expect_log(Some(LogLevel::Warn), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), Some("intent matched: false"))
+        .expect_log(
+            Some(LogLevel::Info),
+            Some("default prompt target found, forwarding request to default prompt target"),
+        )
+        .expect_log(Some(LogLevel::Debug), None)
+        .expect_log(Some(LogLevel::Info), None)
+        .expect_http_call(
+            Some("arch_internal"),
+            Some(vec![
+                (":method", "POST"),
+                ("x-arch-upstream", "weather_forecast_service"),
+                (":path", "/default_target"),
+                (":authority", "weather_forecast_service"),
+                ("content-type", "application/json"),
+                ("x-envoy-max-retries", "3"),
+                ("x-envoy-upstream-rq-timeout-ms", "30000"),
+            ]),
+            None,
+            None,
+            Some(5000),
+        )
+        .returning(Some(2))
+        .expect_metric_increment("active_http_calls", 1)
+        .execute_and_expect(ReturnType::None)
+        .unwrap();
+}
diff --git a/demos/samples_java/weather_forcecast_service/.classpath b/demos/samples_java/weather_forcecast_service/.classpath
new file mode 100644
index 00000000..39abf1c5
--- /dev/null
+++ b/demos/samples_java/weather_forcecast_service/.classpath
@@ -0,0 +1,49 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<classpath>
+	<classpathentry kind="src" output="target/classes" path="src/main/java">
+		<attributes>
+			<attribute name="optional" value="true"/>
+			<attribute name="maven.pomderived" value="true"/>
+		</attributes>
+	</classpathentry>
+	<classpathentry excluding="**" kind="src" output="target/classes" path="src/main/resources">
+		<attributes>
+			<attribute name="maven.pomderived" value="true"/>
+		</attributes>
+	</classpathentry>
+	<classpathentry kind="src" output="target/test-classes" path="src/test/java">
+		<attributes>
+			<attribute name="optional" value="true"/>
+			<attribute name="maven.pomderived" value="true"/>
+			<attribute name="test" value="true"/>
+		</attributes>
+	</classpathentry>
+	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-1.8">
+		<attributes>
+			<attribute name="maven.pomderived" value="true"/>
+		</attributes>
+	</classpathentry>
+	<classpathentry kind="con" path="org.eclipse.m2e.MAVEN2_CLASSPATH_CONTAINER">
+		<attributes>
+			<attribute name="maven.pomderived" value="true"/>
+		</attributes>
+	</classpathentry>
+	<classpathentry kind="src" path="target/generated-sources/annotations">
+		<attributes>
+			<attribute name="optional" value="true"/>
+			<attribute name="maven.pomderived" value="true"/>
+			<attribute name="ignore_optional_problems" value="true"/>
+			<attribute name="m2e-apt" value="true"/>
+		</attributes>
+	</classpathentry>
+	<classpathentry kind="src" output="target/test-classes" path="target/generated-test-sources/test-annotations">
+		<attributes>
+			<attribute name="optional" value="true"/>
+			<attribute name="maven.pomderived" value="true"/>
+			<attribute name="ignore_optional_problems" value="true"/>
+			<attribute name="m2e-apt" value="true"/>
+			<attribute name="test" value="true"/>
+		</attributes>
+	</classpathentry>
+	<classpathentry kind="output" path="target/classes"/>
+</classpath>
diff --git a/demos/samples_java/weather_forcecast_service/.project b/demos/samples_java/weather_forcecast_service/.project
new file mode 100644
index 00000000..efcdddf7
--- /dev/null
+++ b/demos/samples_java/weather_forcecast_service/.project
@@ -0,0 +1,34 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>weather-forecast-service</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>org.eclipse.jdt.core.javabuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+		<buildCommand>
+			<name>org.eclipse.m2e.core.maven2Builder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>org.eclipse.jdt.core.javanature</nature>
+		<nature>org.eclipse.m2e.core.maven2Nature</nature>
+	</natures>
+	<filteredResources>
+		<filter>
+			<id>1742579142020</id>
+			<name></name>
+			<type>30</type>
+			<matcher>
+				<id>org.eclipse.core.resources.regexFilterMatcher</id>
+				<arguments>node_modules|\.git|__CREATED_BY_JAVA_LANGUAGE_SERVER__</arguments>
+			</matcher>
+		</filter>
+	</filteredResources>
+</projectDescription>
diff --git a/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.core.resources.prefs b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.core.resources.prefs
new file mode 100644
index 00000000..abdea9ac
--- /dev/null
+++ b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.core.resources.prefs
@@ -0,0 +1,4 @@
+eclipse.preferences.version=1
+encoding//src/main/java=UTF-8
+encoding//src/main/resources=UTF-8
+encoding/<project>=UTF-8
diff --git a/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.jdt.apt.core.prefs b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.jdt.apt.core.prefs
new file mode 100644
index 00000000..d4313d4b
--- /dev/null
+++ b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.jdt.apt.core.prefs
@@ -0,0 +1,2 @@
+eclipse.preferences.version=1
+org.eclipse.jdt.apt.aptEnabled=false
diff --git a/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.jdt.core.prefs b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.jdt.core.prefs
new file mode 100644
index 00000000..951833c6
--- /dev/null
+++ b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.jdt.core.prefs
@@ -0,0 +1,10 @@
+eclipse.preferences.version=1
+org.eclipse.jdt.core.compiler.codegen.methodParameters=generate
+org.eclipse.jdt.core.compiler.codegen.targetPlatform=1.8
+org.eclipse.jdt.core.compiler.compliance=1.8
+org.eclipse.jdt.core.compiler.problem.enablePreviewFeatures=disabled
+org.eclipse.jdt.core.compiler.problem.forbiddenReference=warning
+org.eclipse.jdt.core.compiler.problem.reportPreviewFeatures=ignore
+org.eclipse.jdt.core.compiler.processAnnotations=disabled
+org.eclipse.jdt.core.compiler.release=disabled
+org.eclipse.jdt.core.compiler.source=1.8
diff --git a/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.m2e.core.prefs b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.m2e.core.prefs
new file mode 100644
index 00000000..f897a7f1
--- /dev/null
+++ b/demos/samples_java/weather_forcecast_service/.settings/org.eclipse.m2e.core.prefs
@@ -0,0 +1,4 @@
+activeProfiles=
+eclipse.preferences.version=1
+resolveWorkspaceProjects=true
+version=1
diff --git a/demos/samples_java/weather_forcecast_service/Dockerfile b/demos/samples_java/weather_forcecast_service/Dockerfile
index 1f536c5a..909d9796 100644
--- a/demos/samples_java/weather_forcecast_service/Dockerfile
+++ b/demos/samples_java/weather_forcecast_service/Dockerfile
@@ -14,5 +14,10 @@ WORKDIR /app
 # Copy the built jar from the previous stage
 COPY --from=build /app/target/weather-forecast-service-0.0.1-SNAPSHOT.jar app.jar
 # Expose the port on which the app runs (default Spring Boot is 8080)
+
+# Expose the application port and the debug port
 EXPOSE 8081
-ENTRYPOINT ["java", "-jar", "app.jar"]
+EXPOSE 5005
+
+# Start the application with remote debugging enabled
+ENTRYPOINT ["java", "-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=*:5005", "-jar", "app.jar"]
diff --git a/demos/samples_java/weather_forcecast_service/arch_config.yaml b/demos/samples_java/weather_forcecast_service/arch_config.yaml
index 10c22819..8228d0f4 100644
--- a/demos/samples_java/weather_forcecast_service/arch_config.yaml
+++ b/demos/samples_java/weather_forcecast_service/arch_config.yaml
@@ -1,8 +1,10 @@
 version: v0.1
-listener:
-  address: 127.0.0.1
-  port: 10000 #If you configure port 443, you'll need to update the listener with tls_certificates
-  message_format: huggingface
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 # Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
 llm_providers:
@@ -43,3 +45,7 @@ prompt_targets:
       name: weather_forecast_service
       path: /weather
       http_method: POST
+
+tracing:
+  random_sampling: 100
+  trace_arch_internal: true
diff --git a/demos/samples_java/weather_forcecast_service/docker-compose.yaml b/demos/samples_java/weather_forcecast_service/docker-compose.yaml
index 998800b7..61bebd4c 100644
--- a/demos/samples_java/weather_forcecast_service/docker-compose.yaml
+++ b/demos/samples_java/weather_forcecast_service/docker-compose.yaml
@@ -5,6 +5,7 @@ services:
       dockerfile: Dockerfile
     ports:
       - "18081:8081"
+      - "5005:5005"
 
   chatbot_ui:
     build:
@@ -18,3 +19,11 @@ services:
       - "host.docker.internal:host-gateway"
     volumes:
       - ./arch_config.yaml:/app/arch_config.yaml
+
+  jaeger:
+    build:
+      context: ../../shared/jaeger
+    ports:
+      - "16686:16686"
+      - "4317:4317"
+      - "4318:4318"
diff --git a/demos/samples_java/weather_forcecast_service/pom.xml b/demos/samples_java/weather_forcecast_service/pom.xml
index c2fed928..db65d95a 100644
--- a/demos/samples_java/weather_forcecast_service/pom.xml
+++ b/demos/samples_java/weather_forcecast_service/pom.xml
@@ -35,6 +35,15 @@
                 <groupId>org.springframework.boot</groupId>
                 <artifactId>spring-boot-maven-plugin</artifactId>
             </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>3.8.1</version>
+                <configuration>
+                    <debug>true</debug>
+                    <debuglevel>lines,vars,source</debuglevel>
+                </configuration>
+            </plugin>
         </plugins>
     </build>
 </project>
diff --git a/demos/samples_python/currency_exchange/arch_config.yaml b/demos/samples_python/currency_exchange/arch_config.yaml
index 89a9e65b..03e5a01d 100644
--- a/demos/samples_python/currency_exchange/arch_config.yaml
+++ b/demos/samples_python/currency_exchange/arch_config.yaml
@@ -1,10 +1,11 @@
 version: v0.1
 
-listener:
-  address: 0.0.0.0
-  port: 10000
-  message_format: huggingface
-  connect_timeout: 0.005s
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 llm_providers:
   - name: gpt-4o
@@ -18,7 +19,7 @@ endpoints:
     protocol: https
 
 system_prompt: |
-  You are a helpful assistant.
+  You are a helpful assistant. Only respond to queries related to currency exchange. If there are any other questions, I can't help you.
 
 prompt_guards:
   input_guards:
diff --git a/demos/samples_python/currency_exchange/hurl_tests/simple.hurl b/demos/samples_python/currency_exchange/hurl_tests/simple.hurl
new file mode 100644
index 00000000..579de34a
--- /dev/null
+++ b/demos/samples_python/currency_exchange/hurl_tests/simple.hurl
@@ -0,0 +1,19 @@
+POST http://localhost:10000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "convert 100 eur"
+    }
+  ]
+}
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^gpt-4o/
+jsonpath "$.metadata.x-arch-state" != null
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" != null
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/demos/samples_python/currency_exchange/hurl_tests/simple_stream.hurl b/demos/samples_python/currency_exchange/hurl_tests/simple_stream.hurl
new file mode 100644
index 00000000..8315236c
--- /dev/null
+++ b/demos/samples_python/currency_exchange/hurl_tests/simple_stream.hurl
@@ -0,0 +1,17 @@
+POST http://localhost:10000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "convert 100 eur"
+    }
+  ],
+  "stream": true
+}
+HTTP 200
+[Asserts]
+header "content-type" matches /text\/event-stream/
+body matches /^data: .*?currency_exchange.*?\n/
+body matches /^data: .*?EUR.*?\n/
diff --git a/demos/samples_python/human_resources_agent/arch_config.yaml b/demos/samples_python/human_resources_agent/arch_config.yaml
index 09264821..5b1a9aa2 100644
--- a/demos/samples_python/human_resources_agent/arch_config.yaml
+++ b/demos/samples_python/human_resources_agent/arch_config.yaml
@@ -1,8 +1,10 @@
 version: v0.1
-listener:
-  address: 127.0.0.1
-  port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
-  message_format: huggingface
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 # Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
 llm_providers:
diff --git a/demos/samples_python/multi_turn_rag_agent/arch_config.yaml b/demos/samples_python/multi_turn_rag_agent/arch_config.yaml
index 1399965f..8e8feb4f 100644
--- a/demos/samples_python/multi_turn_rag_agent/arch_config.yaml
+++ b/demos/samples_python/multi_turn_rag_agent/arch_config.yaml
@@ -1,10 +1,11 @@
 version: v0.1
 
-listener:
-  address: 127.0.0.1
-  port: 10000
-  message_format: huggingface
-  connect_timeout: 0.005s
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 endpoints:
   rag_energy_source_agent:
diff --git a/demos/samples_python/network_switch_operator_agent/README.md b/demos/samples_python/network_switch_operator_agent/README.md
index e7038732..848f2410 100644
--- a/demos/samples_python/network_switch_operator_agent/README.md
+++ b/demos/samples_python/network_switch_operator_agent/README.md
@@ -28,7 +28,7 @@ The assistant can perform several key operations, including rebooting devices, a
 4. Tell me what can you do for me?"
 
 # Observability
-Arch gateway publishes stats endpoint at http://localhost:19901/stats. In this demo we are using prometheus to pull stats from arch and we are using grafana to visalize the stats in dashboard. To see grafana dashboard follow instructions below,
+Arch gateway publishes stats endpoint at http://localhost:19901/stats. In this demo we are using prometheus to pull stats from arch and we are using grafana to visualize the stats in dashboard. To see grafana dashboard follow instructions below,
 
 1. Start grafana and prometheus using following command
    ```yaml
diff --git a/demos/samples_python/network_switch_operator_agent/arch_config.yaml b/demos/samples_python/network_switch_operator_agent/arch_config.yaml
index ad3bfae5..fa830d0b 100644
--- a/demos/samples_python/network_switch_operator_agent/arch_config.yaml
+++ b/demos/samples_python/network_switch_operator_agent/arch_config.yaml
@@ -1,15 +1,17 @@
 version: v0.1
-listener:
-  address: 127.0.0.1
-  port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
-  message_format: huggingface
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 # Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
 llm_providers:
   - name: OpenAI
     provider_interface: openai
     access_key: $OPENAI_API_KEY
-    model: gpt-3.5-turbo
+    model: gpt-4o
     default: true
 
 # default system prompt used by all prompt targets
@@ -24,25 +26,26 @@ prompt_targets:
       path: /agent/device_summary
       http_method: POST
     parameters:
-      - name: device_ids
-        type: list
-        description: A list of device identifiers (IDs) to retrieve statistics for.
+      - name: device_id
+        type: str
+        description: A device identifier to retrieve statistics for.
         required: true # device_ids are required to get device statistics
       - name: days
         type: int
         description: The number of days for which to gather device statistics.
-        default: "7"
-  - name: reboot_devices
-    description: Reboot a list of devices
+        default: 7
+  - name: reboot_device
+    description: Reboot a device
     endpoint:
       name: app_server
       path: /agent/device_reboot
       http_method: POST
     parameters:
-      - name: device_ids
-        type: list
-        description: A list of device identifiers (IDs).
+      - name: device_id
+        type: str
+        description: the device identifier
         required: true
+    system_prompt: You will get a status JSON object. Simply summarize it
 
 # Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
 endpoints:
@@ -53,3 +56,8 @@ endpoints:
     endpoint: host.docker.internal:18083
     # max time to wait for a connection to be established
     connect_timeout: 0.005s
+
+
+tracing:
+  random_sampling: 100
+  trace_arch_internal: true
diff --git a/demos/samples_python/network_switch_operator_agent/docker-compose.yaml b/demos/samples_python/network_switch_operator_agent/docker-compose.yaml
index 90b5c084..237afa4f 100644
--- a/demos/samples_python/network_switch_operator_agent/docker-compose.yaml
+++ b/demos/samples_python/network_switch_operator_agent/docker-compose.yaml
@@ -18,3 +18,11 @@ services:
       - "host.docker.internal:host-gateway"
     volumes:
       - ./arch_config.yaml:/app/arch_config.yaml
+
+  jaeger:
+    build:
+      context: ../../shared/jaeger
+    ports:
+      - "16686:16686"
+      - "4317:4317"
+      - "4318:4318"
diff --git a/demos/samples_python/network_switch_operator_agent/main.py b/demos/samples_python/network_switch_operator_agent/main.py
index b9676c45..a7a07ab4 100644
--- a/demos/samples_python/network_switch_operator_agent/main.py
+++ b/demos/samples_python/network_switch_operator_agent/main.py
@@ -13,7 +13,7 @@ DEMO_DESCRIPTION = """This demo illustrates how **Arch** can be used to perform
 
 # Define the request model
 class DeviceSummaryRequest(BaseModel):
-    device_ids: List[int]
+    device_id: str
     time_range: Optional[int] = Field(
         default=7, description="Time range in days, defaults to 7"
     )
@@ -21,7 +21,7 @@ class DeviceSummaryRequest(BaseModel):
 
 # Define the response model
 class DeviceStatistics(BaseModel):
-    device_id: int
+    device_id: str
     time_range: str
     data: str
 
@@ -33,7 +33,7 @@ class DeviceSummaryResponse(BaseModel):
 
 
 class DeviceRebootRequest(BaseModel):
-    device_ids: List[int]
+    device_id: str
 
 
 # Response model for the device reboot
@@ -49,24 +49,21 @@ def reboot_network_device(request_data: DeviceRebootRequest):
     """
 
     # Access data from the Pydantic model
-    device_ids = request_data.device_ids
+    device_id = request_data.device_id
 
-    # Validate 'device_ids'
+    # Validate 'device_id'
     # (This is already validated by Pydantic, but additional logic can be added if needed)
-    if not device_ids:
-        raise HTTPException(
-            status_code=400, detail="'device_ids' parameter is required"
-        )
+    if not device_id:
+        raise HTTPException(status_code=400, detail="'device_id' parameter is required")
 
     # Simulate reboot operation and return the response
     statistics = []
-    for device_id in device_ids:
-        # Placeholder for actual data retrieval or device reboot logic
-        stats = {"data": f"Device {device_id} has been successfully rebooted."}
-        statistics.append(stats)
+    # Placeholder for actual data retrieval or device reboot logic
+    stats = {"data": f"Device {device_id} has been successfully rebooted."}
+    statistics.append(stats)
 
     # Return the response with a summary
-    return CoverageResponse(status="success", summary={"device_ids": device_ids})
+    return CoverageResponse(status="success", summary={"device_id": device_id})
 
 
 # Post method for device summary
@@ -76,28 +73,20 @@ def get_device_summary(request: DeviceSummaryRequest):
     Endpoint to retrieve device statistics based on device IDs and an optional time range.
     """
 
-    # Extract 'device_ids' and 'time_range' from the request
-    device_ids = request.device_ids
+    # Extract 'device_id' and 'time_range' from the request
+    device_id = request.device_id
     time_range = request.time_range
 
     # Simulate retrieving statistics for the given device IDs and time range
     statistics = []
-    minutes = 1
-    for device_id in device_ids:
-        stats = {
-            "device_id": device_id,
-            "time_range": f"Last {time_range} days",
-            "data": f"""Device {device_id} over the last {time_range} days experienced {minutes}
-             minutes of downtime.""",
-        }
-        minutes += 1
-        statistics.append(DeviceStatistics(**stats))
+    minutes = 4
+    stats = {
+        "device_id": device_id,
+        "time_range": f"Last {time_range} days",
+        "data": f"""Device {device_id} over the last {time_range} days experienced {minutes}
+        minutes of downtime.""",
+    }
+
+    statistics.append(DeviceStatistics(**stats))
 
     return DeviceSummaryResponse(statistics=statistics)
-
-
-CHAT_COMPLETION_ENDPOINT = os.getenv("CHAT_COMPLETION_ENDPOINT")
-client = OpenAI(
-    api_key="--",
-    base_url=CHAT_COMPLETION_ENDPOINT,
-)
diff --git a/demos/samples_python/network_switch_operator_agent/run_demo.sh b/demos/samples_python/network_switch_operator_agent/run_demo.sh
index 6995e7d0..eb47dce6 100644
--- a/demos/samples_python/network_switch_operator_agent/run_demo.sh
+++ b/demos/samples_python/network_switch_operator_agent/run_demo.sh
@@ -22,9 +22,8 @@ start_demo() {
   echo "Starting Arch with arch_config.yaml..."
   archgw up arch_config.yaml
 
-  # Step 4: Start Network Agent
+  # Step 4: Start developer services
   echo "Starting Network Agent using Docker Compose..."
-  cd build
   docker compose up -d  # Run in detached mode
 }
 
diff --git a/demos/samples_python/stock_quote/arch_config.yaml b/demos/samples_python/stock_quote/arch_config.yaml
index c763d4ca..96901620 100644
--- a/demos/samples_python/stock_quote/arch_config.yaml
+++ b/demos/samples_python/stock_quote/arch_config.yaml
@@ -1,10 +1,11 @@
 version: v0.1
 
-listener:
-  address: 0.0.0.0
-  port: 10000
-  message_format: huggingface
-  connect_timeout: 0.005s
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 llm_providers:
   - name: gpt-4o
diff --git a/demos/samples_python/weather_forecast/README.md b/demos/samples_python/weather_forecast/README.md
index 2dfd6a8f..26eea157 100644
--- a/demos/samples_python/weather_forecast/README.md
+++ b/demos/samples_python/weather_forecast/README.md
@@ -1,6 +1,6 @@
 # Function calling
 
-This demo shows how you can use Arch's core function calling capabilites.
+This demo shows how you can use Arch's core function calling capabilities.
 
 # Starting the demo
 
diff --git a/demos/samples_python/weather_forecast/arch_config.yaml b/demos/samples_python/weather_forecast/arch_config.yaml
index 94a6bdfb..b6463594 100644
--- a/demos/samples_python/weather_forecast/arch_config.yaml
+++ b/demos/samples_python/weather_forecast/arch_config.yaml
@@ -1,10 +1,11 @@
 version: "0.1-beta"
 
-listener:
-  address: 0.0.0.0
-  port: 10000
-  message_format: huggingface
-  connect_timeout: 0.005s
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 endpoints:
   weather_forecast_service:
@@ -16,21 +17,17 @@ overrides:
   prompt_target_intent_matching_threshold: 0.6
 
 llm_providers:
-  - name: gpt-4o-mini
-    access_key: $OPENAI_API_KEY
+  - name: groq
+    access_key: $GROQ_API_KEY
     provider_interface: openai
-    model: gpt-4o-mini
-    default: true
-
-  - name: gpt-3.5-turbo-0125
-    access_key: $OPENAI_API_KEY
-    provider_interface: openai
-    model: gpt-3.5-turbo-0125
+    model: llama-3.2-3b-preview
+    base_url: https://api.groq.com
 
   - name: gpt-4o
     access_key: $OPENAI_API_KEY
     provider_interface: openai
     model: gpt-4o
+    default: true
 
 system_prompt: |
   You are a helpful assistant.
diff --git a/demos/samples_python/weather_forecast/docker-compose.yaml b/demos/samples_python/weather_forecast/docker-compose.yaml
index 566dfa8d..abb69654 100644
--- a/demos/samples_python/weather_forecast/docker-compose.yaml
+++ b/demos/samples_python/weather_forecast/docker-compose.yaml
@@ -19,3 +19,5 @@ services:
       - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
     extra_hosts:
       - "host.docker.internal:host-gateway"
+    volumes:
+      - ./arch_config.yaml:/app/arch_config.yaml
diff --git a/demos/samples_python/weather_forecast/hurl_tests/simple.hurl b/demos/samples_python/weather_forecast/hurl_tests/simple.hurl
new file mode 100644
index 00000000..d1243d20
--- /dev/null
+++ b/demos/samples_python/weather_forecast/hurl_tests/simple.hurl
@@ -0,0 +1,19 @@
+POST http://localhost:10000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "how is the weather in seattle for next 5 days"
+    }
+  ]
+}
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^gpt-4o/
+jsonpath "$.metadata.x-arch-state" != null
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" matches /Seattle/
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/demos/samples_python/weather_forecast/hurl_tests/simple_stream.hurl b/demos/samples_python/weather_forecast/hurl_tests/simple_stream.hurl
new file mode 100644
index 00000000..51844c2d
--- /dev/null
+++ b/demos/samples_python/weather_forecast/hurl_tests/simple_stream.hurl
@@ -0,0 +1,17 @@
+POST http://localhost:10000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "how is the weather in seattle for next 5 days"
+    }
+  ],
+  "stream": true
+}
+HTTP 200
+[Asserts]
+header "content-type" matches /text\/event-stream/
+body matches "(?s).*\"name\":\"get_current_weather\".*"
+body matches "(?s).*\"model\":\"gpt-4o-mini.*"
diff --git a/demos/samples_python/weather_forecast/main.py b/demos/samples_python/weather_forecast/main.py
index 3be2f4da..84ee75e6 100644
--- a/demos/samples_python/weather_forecast/main.py
+++ b/demos/samples_python/weather_forecast/main.py
@@ -73,7 +73,7 @@ async def weather(req: WeatherRequest, res: Response):
 
 
 class DefaultTargetRequest(BaseModel):
-    messages: list
+    messages: list = []
 
 
 @app.post("/default_target")
@@ -86,12 +86,9 @@ async def default_target(req: DefaultTargetRequest, res: Response):
                     "role": "assistant",
                     "content": "I can help you with weather forecast",
                 },
-                "finish_reason": "completed",
-                "index": 0,
             }
         ],
         "model": "api_server",
-        "usage": {"completion_tokens": 0},
     }
     logger.info(f"sending response: {json.dumps(resp)}")
     return resp
diff --git a/demos/shared/chatbot_ui/.vscode/launch.json b/demos/shared/chatbot_ui/.vscode/launch.json
index e7f91d36..6a470b7b 100644
--- a/demos/shared/chatbot_ui/.vscode/launch.json
+++ b/demos/shared/chatbot_ui/.vscode/launch.json
@@ -15,7 +15,7 @@
         "LLM": "1",
         "CHAT_COMPLETION_ENDPOINT": "http://localhost:10000/v1",
         "STREAMING": "True",
-        "ARCH_CONFIG": "../../weather_forecast/arch_config.yaml"
+        "ARCH_CONFIG": "../../samples_python/weather_forecast/arch_config.yaml"
       }
     },
     {
@@ -29,7 +29,7 @@
         "LLM": "1",
         "CHAT_COMPLETION_ENDPOINT": "http://localhost:12000/v1",
         "STREAMING": "True",
-        "ARCH_CONFIG": "../../llm_routing/arch_config.yaml"
+        "ARCH_CONFIG": "../../samples_python/weather_forecast/arch_config.yaml"
       }
     },
   ]
diff --git a/demos/shared/chatbot_ui/common.py b/demos/shared/chatbot_ui/common.py
index cfcc6556..1de8f94c 100644
--- a/demos/shared/chatbot_ui/common.py
+++ b/demos/shared/chatbot_ui/common.py
@@ -38,7 +38,7 @@ def chat(
     try:
         response = client.chat.completions.create(
             # we select model from arch_config file
-            model="--",
+            model="None",
             messages=history,
             temperature=1.0,
             stream=True,
@@ -120,8 +120,11 @@ def process_stream_chunk(chunk, history):
 
     if delta.content:
         # append content to the last history item
-        history[-1]["content"] = history[-1].get("content", "") + delta.content
+        if history[-1]["model"] != "Arch-Function-Chat":
+            history[-1]["content"] = history[-1].get("content", "") + delta.content
         # yield content if it is from assistant
+        if history[-1]["model"] == "Arch-Function":
+            return None
         if history[-1]["role"] == "assistant":
             return delta.content
 
diff --git a/demos/shared/chatbot_ui/run_stream.py b/demos/shared/chatbot_ui/run_stream.py
index 407d4c05..89448355 100644
--- a/demos/shared/chatbot_ui/run_stream.py
+++ b/demos/shared/chatbot_ui/run_stream.py
@@ -54,13 +54,13 @@ def chat(
         if model_selector and model_selector != "":
             headers["x-arch-llm-provider-hint"] = model_selector
         client = OpenAI(
-            api_key="--",
+            api_key="None",
             base_url=CHAT_COMPLETION_ENDPOINT,
             default_headers=headers,
         )
         response = client.chat.completions.create(
             # we select model from arch_config file
-            model="--",
+            model="None",
             messages=history,
             temperature=1.0,
             stream=True,
@@ -88,6 +88,22 @@ def chat(
 
             yield "", conversation, history, debug_output, model_selector
 
+    # update assistant response to have correct format
+    # arch-fc 1.1 expects following format:
+    # {
+    #     "response": "<assistant response>",
+    # }
+    # and this entire block needs to be encoded in ```json\n{json_encoded_content}\n```
+
+    if not history[-1]["model"].startswith("Arch"):
+        assistant_response = {
+            "response": history[-1]["content"],
+        }
+        history[-1]["content"] = "```json\n{}\n```".format(
+            json.dumps(assistant_response)
+        )
+    log.info("history: {}".format(json.dumps(history)))
+
 
 def main():
     with gr.Blocks(
diff --git a/demos/shared/test_runner/run_demo_tests.sh b/demos/shared/test_runner/run_demo_tests.sh
index a029345f..5b930565 100644
--- a/demos/shared/test_runner/run_demo_tests.sh
+++ b/demos/shared/test_runner/run_demo_tests.sh
@@ -8,11 +8,13 @@ do
   echo "Running tests for $demo ..."
   echo "****************************************"
   cd ../../samples_python/$demo
+  echo "starting archgw"
   archgw up arch_config.yaml
-  docker compose up -d
-  cd ../../shared/test_runner
-  TEST_DATA=../../samples_python/$demo/test_data.yaml poetry run pytest
-  cd ../../samples_python/$demo
+  echo "starting docker containers"
+  docker compose up -d 2>&1 > /dev/null
+  echo "starting hurl tests"
+  hurl --test hurl_tests/*.hurl
+  echo "stopping docker containers and archgw"
   archgw down
   docker compose down -v
   cd ../../shared/test_runner
diff --git a/demos/use_cases/llm_routing/arch_config.yaml b/demos/use_cases/llm_routing/arch_config.yaml
index f7ce78cd..11087d3a 100644
--- a/demos/use_cases/llm_routing/arch_config.yaml
+++ b/demos/use_cases/llm_routing/arch_config.yaml
@@ -1,10 +1,11 @@
 version: "0.1-beta"
 
-listener:
-  address: 0.0.0.0
-  port: 10000
-  message_format: huggingface
-  connect_timeout: 0.005s
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
 
 llm_providers:
   - name: gpt-4o-mini
@@ -13,11 +14,6 @@ llm_providers:
     model: gpt-4o-mini
     default: true
 
-  - name: gpt-3.5-turbo-0125
-    access_key: $OPENAI_API_KEY
-    provider_interface: openai
-    model: gpt-3.5-turbo-0125
-
   - name: gpt-4o
     access_key: $OPENAI_API_KEY
     provider_interface: openai
@@ -28,5 +24,17 @@ llm_providers:
     provider_interface: mistral
     model: ministral-3b-latest
 
+  - name: deepseek
+    access_key: $DEEPSEEK_API_KEY
+    provider_interface: openai
+    model: deepseek-reasoner
+    base_url: https://api.deepseek.com/
+
+  - name: groq
+    access_key: $GROQ_API_KEY
+    provider_interface: openai
+    model: llama-3.1-8b-instant
+    base_url: https://api.groq.com
+
 tracing:
   random_sampling: 100
diff --git a/demos/use_cases/ollama/arch_config.yaml b/demos/use_cases/ollama/arch_config.yaml
index 5cb77750..c933841d 100644
--- a/demos/use_cases/ollama/arch_config.yaml
+++ b/demos/use_cases/ollama/arch_config.yaml
@@ -1,10 +1,11 @@
 version: v0.1
 
-listener:
-  address: 0.0.0.0
-  port: 10000
-  message_format: huggingface
-  connect_timeout: 0.005s
+listeners:
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
 
 llm_providers:
 
diff --git a/demos/use_cases/ollama/docker-compose.yaml b/demos/use_cases/ollama/docker-compose.yaml
index ee5465a5..44fb20b7 100644
--- a/demos/use_cases/ollama/docker-compose.yaml
+++ b/demos/use_cases/ollama/docker-compose.yaml
@@ -6,7 +6,7 @@ services:
       - "18080:8080"
     environment:
       # this is only because we are running the sample app in the same docker container environemtn as archgw
-      - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
+      - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:12000/v1
     extra_hosts:
       - "host.docker.internal:host-gateway"
     volumes:
diff --git a/demos/use_cases/orchestrating_agents/Dockerfile b/demos/use_cases/orchestrating_agents/Dockerfile
new file mode 100644
index 00000000..b53cb719
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/Dockerfile
@@ -0,0 +1,41 @@
+# took inspiration from https://medium.com/@albertazzir/blazing-fast-python-docker-builds-with-poetry-a78a66f5aed0
+
+# The builder image, used to build the virtual environment
+FROM python:3.10 as builder
+
+RUN pip install poetry==1.8.3
+
+ENV POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_IN_PROJECT=1 \
+    POETRY_VIRTUALENVS_CREATE=1 \
+    POETRY_CACHE_DIR=/tmp/poetry_cache
+
+WORKDIR /code
+
+COPY pyproject.toml poetry.lock ./
+RUN touch README.md
+
+RUN poetry install --no-root && rm -rf $POETRY_CACHE_DIR
+
+# The runtime image, used to just run the code provided its virtual environment
+FROM python:3.10-slim as runtime
+
+RUN apt-get update && apt-get install -y curl
+
+WORKDIR /code
+
+ENV VIRTUAL_ENV=/code/.venv \
+    PATH="/code/.venv/bin:$PATH"
+
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+
+COPY main.py ./
+
+HEALTHCHECK \
+    --interval=5s \
+    --timeout=1s \
+    --start-period=1s \
+    --retries=3 \
+    CMD curl http://localhost:80/healthz
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80", "--log-level", "debug"]
diff --git a/demos/use_cases/orchestrating_agents/arch_config.yaml b/demos/use_cases/orchestrating_agents/arch_config.yaml
new file mode 100644
index 00000000..7cffa101
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/arch_config.yaml
@@ -0,0 +1,46 @@
+version: "0.1-beta"
+
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
+
+  egress_traffic:
+    address: 0.0.0.0
+    port: 12000
+    message_format: openai
+    timeout: 30s
+
+overrides:
+  use_agent_orchestrator: true
+
+endpoints:
+  agent_gateway:
+    endpoint: host.docker.internal:18083
+    connect_timeout: 0.005s
+
+llm_providers:
+  - name: gpt-4o-mini
+    access_key: $OPENAI_API_KEY
+    provider_interface: openai
+    model: gpt-4o-mini
+    default: true
+
+system_prompt: |
+  You are a helpful assistant.
+
+prompt_targets:
+  - name: sales_agent
+    description: handles queries related to sales and purchases
+
+  - name: issues_and_repairs
+    description: handles issues, repairs, or refunds
+
+  - name: escalate_to_human
+    description: escalates to human agent
+
+tracing:
+  random_sampling: 100
+  trace_arch_internal: true
diff --git a/demos/use_cases/orchestrating_agents/docker-compose.yaml b/demos/use_cases/orchestrating_agents/docker-compose.yaml
new file mode 100644
index 00000000..288ecf30
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/docker-compose.yaml
@@ -0,0 +1,29 @@
+services:
+  triage_service:
+    build:
+      context: ./
+    environment:
+      - OLTP_HOST=http://jaeger:4317
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    ports:
+      - "18083:80"
+
+  chatbot_ui:
+    build:
+      context: ../../shared/chatbot_ui
+    ports:
+      - "18080:8080"
+    environment:
+      # this is only because we are running the sample app in the same docker container environemtn as archgw
+      - CHAT_COMPLETION_ENDPOINT=http://host.docker.internal:10000/v1
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+
+  jaeger:
+    build:
+      context: ../../shared/jaeger
+    ports:
+      - "16686:16686"
+      - "4317:4317"
+      - "4318:4318"
diff --git a/demos/use_cases/orchestrating_agents/hurl_tests/simple_issues_repairs.hurl b/demos/use_cases/orchestrating_agents/hurl_tests/simple_issues_repairs.hurl
new file mode 100644
index 00000000..3820de57
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/hurl_tests/simple_issues_repairs.hurl
@@ -0,0 +1,19 @@
+POST http://localhost:10000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "I bought a package recently and it not working properly"
+    }
+  ]
+}
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^gpt-4o-2/
+jsonpath "$.metadata.x-arch-state" != null
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" != null
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/demos/use_cases/orchestrating_agents/hurl_tests/simple_sale_agent.hurl b/demos/use_cases/orchestrating_agents/hurl_tests/simple_sale_agent.hurl
new file mode 100644
index 00000000..4db2c67c
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/hurl_tests/simple_sale_agent.hurl
@@ -0,0 +1,19 @@
+POST http://localhost:10000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "I want to sell red shoes"
+    }
+  ]
+}
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^gpt-4o-mini/
+jsonpath "$.metadata.x-arch-state" != null
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" != null
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/demos/use_cases/orchestrating_agents/hurl_tests/simple_stream.hurl b/demos/use_cases/orchestrating_agents/hurl_tests/simple_stream.hurl
new file mode 100644
index 00000000..f060fed0
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/hurl_tests/simple_stream.hurl
@@ -0,0 +1,16 @@
+POST http://localhost:10000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "I want to sell red shoes"
+    }
+  ],
+  "stream": true
+}
+HTTP 200
+[Asserts]
+header "content-type" matches /text\/event-stream/
+body matches /^data: .*?sales_agent.*?\n/
diff --git a/demos/use_cases/orchestrating_agents/main.py b/demos/use_cases/orchestrating_agents/main.py
new file mode 100644
index 00000000..4f288e50
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/main.py
@@ -0,0 +1,115 @@
+import logging
+import json
+from typing import List, Dict, Any
+from fastapi import FastAPI, Request
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+import openai
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("uvicorn.error")
+
+app = FastAPI()
+
+
+class Message(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionsRequest(BaseModel):
+    messages: List[Message]
+    model: str
+    metadata: Dict[str, Any] = {}
+    stream: bool = False
+
+
+openai_client = openai.OpenAI(
+    api_key="None",  # archgw picks the API key from the config file
+    base_url="http://host.docker.internal:12000/v1",
+)
+
+
+def call_openai(messages: List[Dict[str, str]], stream: bool, model: str):
+    logger.info(f"llm agent model: {model}")
+    completion = openai_client.chat.completions.create(
+        model=model,
+        messages=messages,
+        stream=stream,
+    )
+
+    if stream:
+
+        def stream():
+            for line in completion:
+                if line.choices and len(line.choices) > 0 and line.choices[0].delta:
+                    chunk_response_str = json.dumps(line.model_dump())
+                    yield "data: " + chunk_response_str + "\n\n"
+            yield "data: [DONE]" + "\n\n"
+
+        return StreamingResponse(stream(), media_type="text/event-stream")
+    else:
+        return completion
+
+
+class Agent:
+    def __init__(self, role: str, instructions: str, model: str = ""):
+        self.model = model
+        self.system_prompt = f"You are a {role}.\n{instructions}"
+
+    def handle(self, req: ChatCompletionsRequest):
+        messages = [{"role": "system", "content": self.get_system_prompt()}] + [
+            message.model_dump() for message in req.messages
+        ]
+
+        model = req.model
+        if self.model:
+            model = self.model
+        return call_openai(messages, req.stream, model)
+
+    def get_system_prompt(self) -> str:
+        return self.system_prompt
+
+
+# Define your agents
+AGENTS = {
+    "sales_agent": Agent(
+        role="sales agent",
+        instructions=(
+            "Always answer in a sentence or less.\n"
+            "Follow the following routine with the user:\n"
+            "1. Engage\n"
+            "2. Quote ridiculous price\n"
+            "3. Reveal caveat if user agrees."
+        ),
+        model="gpt-4o-mini",
+    ),
+    "issues_and_repairs": Agent(
+        role="issues and repairs agent",
+        instructions="Propose a solution, offer refund if necessary.",
+        model="gpt-4o",
+    ),
+    "escalate_to_human": Agent(
+        role="human escalation agent",
+        instructions="Escalate issues to a human.",
+        # skipping model name here as arch gateway will pick the default model from the config file
+    ),
+    "unknown_agent": Agent(
+        role="general assistant", instructions="Assist the user in general queries."
+    ),
+}
+
+
+@app.post("/v1/chat/completions")
+def completion_api(req: ChatCompletionsRequest, request: Request):
+    agent_name = req.metadata.get("agent-name", "unknown_agent")
+    agent = AGENTS.get(agent_name)
+    logger.info(f"Routing to agent: {agent_name}")
+
+    return agent.handle(req)
+
+
+@app.get("/healthz")
+async def healthz():
+    return {"status": "ok"}
diff --git a/demos/use_cases/orchestrating_agents/poetry.lock b/demos/use_cases/orchestrating_agents/poetry.lock
new file mode 100644
index 00000000..929a5c86
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/poetry.lock
@@ -0,0 +1,573 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.9.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
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+    {file = "PyYAML-6.0.2-cp39-cp39-win32.whl", hash = "sha256:6395c297d42274772abc367baaa79683958044e5d3835486c16da75d2a694631"},
+    {file = "PyYAML-6.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:39693e1f8320ae4f43943590b49779ffb98acb81f788220ea932a6b6c51004d8"},
+    {file = "pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e"},
+]
+
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
+[[package]]
+name = "starlette"
+version = "0.46.1"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "starlette-0.46.1-py3-none-any.whl", hash = "sha256:77c74ed9d2720138b25875133f3a2dae6d854af2ec37dceb56aef370c1d8a227"},
+    {file = "starlette-0.46.1.tar.gz", hash = "sha256:3c88d58ee4bd1bb807c0d1acb381838afc7752f9ddaec81bbe4383611d833230"},
+]
+
+[package.dependencies]
+anyio = ">=3.6.2,<5"
+
+[package.extras]
+full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
+
+[[package]]
+name = "tqdm"
+version = "4.67.1"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"},
+    {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[[package]]
+name = "uvicorn"
+version = "0.34.0"
+description = "The lightning-fast ASGI server."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "uvicorn-0.34.0-py3-none-any.whl", hash = "sha256:023dc038422502fa28a09c7a30bf2b6991512da7dcdb8fd35fe57cfc154126f4"},
+    {file = "uvicorn-0.34.0.tar.gz", hash = "sha256:404051050cd7e905de2c9a7e61790943440b3416f49cb409f965d9dcd0fa73e9"},
+]
+
+[package.dependencies]
+click = ">=7.0"
+h11 = ">=0.8"
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "d005d82268b6f8c2a68b26c454bced5c34bf3c971c0cbfefde3fc0c45c675f55"
diff --git a/demos/use_cases/orchestrating_agents/pyproject.toml b/demos/use_cases/orchestrating_agents/pyproject.toml
new file mode 100644
index 00000000..7b422438
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/pyproject.toml
@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "api-server"
+version = "0.1.0"
+description = ""
+authors = ["Adil Hafeez <info@katanemo.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+fastapi = "^0.115.4"
+pyyaml = "^6.0.2"
+uvicorn = "^0.34.0"
+openai = "^1.66.5"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.scripts]
+api-server = "api_server.main:app"
diff --git a/demos/use_cases/orchestrating_agents/run_demo.sh b/demos/use_cases/orchestrating_agents/run_demo.sh
new file mode 100644
index 00000000..eb47dce6
--- /dev/null
+++ b/demos/use_cases/orchestrating_agents/run_demo.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+set -e
+
+# Function to start the demo
+start_demo() {
+  # Step 1: Check if .env file exists
+  if [ -f ".env" ]; then
+    echo ".env file already exists. Skipping creation."
+  else
+    # Step 2: Create `.env` file and set OpenAI key
+    if [ -z "$OPENAI_API_KEY" ]; then
+      echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
+      exit 1
+    fi
+
+    echo "Creating .env file..."
+    echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
+    echo ".env file created with OPENAI_API_KEY."
+  fi
+
+  # Step 3: Start Arch
+  echo "Starting Arch with arch_config.yaml..."
+  archgw up arch_config.yaml
+
+  # Step 4: Start developer services
+  echo "Starting Network Agent using Docker Compose..."
+  docker compose up -d  # Run in detached mode
+}
+
+# Function to stop the demo
+stop_demo() {
+  # Step 1: Stop Docker Compose services
+  echo "Stopping Network Agent using Docker Compose..."
+  docker compose down
+
+  # Step 2: Stop Arch
+  echo "Stopping Arch..."
+  archgw down
+}
+
+# Main script logic
+if [ "$1" == "down" ]; then
+  stop_demo
+else
+  # Default action is to bring the demo up
+  start_demo
+fi
diff --git a/demos/use_cases/spotify_bearer_auth/arch_config.yaml b/demos/use_cases/spotify_bearer_auth/arch_config.yaml
index a259a539..1d82a426 100644
--- a/demos/use_cases/spotify_bearer_auth/arch_config.yaml
+++ b/demos/use_cases/spotify_bearer_auth/arch_config.yaml
@@ -1,8 +1,10 @@
 version: v0.1
-listener:
-  address: 127.0.0.1
-  port: 8080 #If you configure port 443, you'll need to update the listener with tls_certificates
-  message_format: huggingface
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 overrides:
   optimize_context_window: true
diff --git a/docs/source/build_with_arch/rag.rst b/docs/source/build_with_arch/rag.rst
index 3f4db41f..eafd6e2b 100644
--- a/docs/source/build_with_arch/rag.rst
+++ b/docs/source/build_with_arch/rag.rst
@@ -46,7 +46,7 @@ Multi-Turn RAG (Follow-up Questions)
 Developers often `struggle <https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/>`_ to efficiently handle
 ``follow-up`` or ``clarification`` questions. Specifically, when users ask for changes or additions to previous responses, it requires developers to
 re-write prompts using LLMs with precise prompt engineering techniques. This process is slow, manual, error prone and adds signifcant latency to the
-user experience. Arch
+user experience.
 
 Arch is highly capable of accurately detecting and processing prompts in a multi-turn scenarios so that you can buil fast and accurate RAG apps in
 minutes. For additional details on how to build multi-turn RAG applications please refer to our :ref:`multi-turn <arch_multi_turn_guide>` docs.
diff --git a/docs/source/concepts/includes/arch_config.yaml b/docs/source/concepts/includes/arch_config.yaml
index 93164401..a7d0a289 100644
--- a/docs/source/concepts/includes/arch_config.yaml
+++ b/docs/source/concepts/includes/arch_config.yaml
@@ -1,10 +1,11 @@
 version: v0.1
 
-listener:
-  address: 0.0.0.0 # or 127.0.0.1
-  port: 10000
-  # Defines how Arch should parse the content from application/json or text/pain Content-type in the http request
-  message_format: huggingface
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 # Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
 llm_providers:
@@ -51,11 +52,6 @@ prompt_targets:
         default: false
         enum: [true, false]
 
-error_target:
-  endpoint:
-    name: error_target_1
-    path: /error
-
 # Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
 endpoints:
   app_server:
diff --git a/docs/source/concepts/llm_provider.rst b/docs/source/concepts/llm_provider.rst
index ba9566ba..551d6074 100644
--- a/docs/source/concepts/llm_provider.rst
+++ b/docs/source/concepts/llm_provider.rst
@@ -4,7 +4,7 @@ LLM Provider
 ============
 
 **LLM provider** is a top-level primitive in Arch, helping developers centrally define, secure, observe,
-and manage the usage of of their LLMs. Arch builds on Envoy's reliable `cluster subsystem <https://www.envoyproxy.io/docs/envoy/v1.31.2/intro/arch_overview/upstream/cluster_manager>`_
+and manage the usage of their LLMs. Arch builds on Envoy's reliable `cluster subsystem <https://www.envoyproxy.io/docs/envoy/v1.31.2/intro/arch_overview/upstream/cluster_manager>`_
 to manage egress traffic to LLMs, which includes intelligent routing, retry and fail-over mechanisms,
 ensuring high availability and fault tolerance. This abstraction also enables developers to seamlessly
 switching between LLM providers or upgrade LLM versions, simplifying the integration and scaling of LLMs
diff --git a/docs/source/concepts/prompt_target.rst b/docs/source/concepts/prompt_target.rst
index b489b0c1..c708a9e7 100644
--- a/docs/source/concepts/prompt_target.rst
+++ b/docs/source/concepts/prompt_target.rst
@@ -3,14 +3,13 @@
 Prompt Target
 ==============
 
-**Prompt Targets** are a fundamental component of Arch, enabling developers to define how different types of user prompts are processed and routed within their generative AI applications.
-This section provides an in-depth look at prompt targets, including their purpose, configuration, usage, and best practices to help you effectively leverage this feature in your projects.
+**Prompt Targets** are a core concept in Arch, empowering developers to clearly define how user prompts are interpreted, processed, and routed within their generative AI applications. Prompts can seamlessly be routed either to specialized AI agents capable of handling sophisticated, context-driven tasks or to targeted tools provided by your application, offering users a fast, precise, and personalized experience.
+
+This section covers the essentials of prompt targets—what they are, how to configure them, their practical uses, and recommended best practices—to help you fully utilize this feature in your applications.
 
 What Are Prompt Targets?
 ------------------------
-Prompt targets are predefined endpoints within Arch that handle specific types of user prompts.
-They act as the bridge between user inputs and your backend services or APIs, enabling Arch to route, process, and manage prompts efficiently.
-By defining prompt targets, you can separate your application's business logic from the complexities of prompt processing, ensuring a cleaner and more maintainable codebase.
+Prompt targets are endpoints within Arch that handle specific types of user prompts. They act as the bridge between user inputs and your backend agents or tools (APIs), enabling Arch to route, process, and manage prompts efficiently. Defining prompt targets helps you decouple your application's core logic from processing and handling complexities, leading to clearer code organization, better scalability, and easier maintenance.
 
 
 .. table::
@@ -21,7 +20,7 @@ By defining prompt targets, you can separate your application's business logic f
     ====================    ============================================
     Intent Recognition      Identify the purpose of a user prompt.
     Parameter Extraction    Extract necessary data from the prompt.
-    API Invocation          Call relevant backend services or functions.
+    Invocation              Call relevant backend agents or tools (APIs).
     Response Handling       Process and return responses to the user.
     ====================    ============================================
 
@@ -30,16 +29,15 @@ Key Features
 
 Below are the key features of prompt targets that empower developers to build efficient, scalable, and personalized GenAI solutions:
 
-- **Modular Design**: Define multiple prompt targets to handle diverse functionalities.
-- **Parameter Management**: Specify required and optional parameters for each target.
-- **Function Integration**: Seamlessly connect prompts to backend APIs or functions.
+- **Design Scenarios**: Define prompt targets to effectively handle specific agentic scenarios.
+- **Input Management**: Specify required and optional parameters for each target.
+- **Tools Integration**: Seamlessly connect prompts to backend APIs or functions.
 - **Error Handling**: Direct errors to designated handlers for streamlined troubleshooting.
 - **Metadata Enrichment**: Attach additional context to prompts for enhanced processing.
 
 Configuring Prompt Targets
 --------------------------
-Configuring prompt targets involves defining them in Arch's configuration file.
-Each Prompt target specifies how a particular type of prompt should be handled, including the endpoint to invoke and any parameters required.
+Configuring prompt targets involves defining them in Arch's configuration file. Each Prompt target specifies how a particular type of prompt should be handled, including the endpoint to invoke and any parameters required.
 
 Basic Configuration
 ~~~~~~~~~~~~~~~~~~~
@@ -50,37 +48,38 @@ A prompt target configuration includes the following elements:
 
 - ``name``: A unique identifier for the prompt target.
 - ``description``: A brief explanation of what the prompt target does.
-- ``endpoint``: The API endpoint or function that handles the prompt.
+- ``endpoint``: Required if you want to call a tool or specific API. ``name`` and ``path`` ``http_method`` are the three attributes of the endpoint.
 - ``parameters`` (Optional): A list of parameters to extract from the prompt.
 
+.. _defining_prompt_target_parameters:
+
 Defining Parameters
 ~~~~~~~~~~~~~~~~~~~
 Parameters are the pieces of information that Arch needs to extract from the user's prompt to perform the desired action.
-Each parameter can be marked as required or optional.
-Here is a full list of parameter attributes that Arch can support:
+Each parameter can be marked as required or optional. Here is a full list of parameter attributes that Arch can support:
 
 .. table::
     :width: 100%
 
-    ====================      ============================================================================
+    ========================  ============================================================================
     **Attribute**             **Description**
-    ====================      ============================================================================
-    ``name``                  Specifies identifier of parameters
-    ``type``                  Specifies the data type of the parameter.
-    ``description``           Provides a human-readable explanation of the parameter's purpose.
-    ``required``              Indicates whether the parameter is mandatory or optional
+    ========================  ============================================================================
+    ``name (req.)``           Specifies name of the parameter.
+    ``description (req.)``    Provides a human-readable explanation of the parameter's purpose.
+    ``type (req.)``           Specifies the data type. Supported types include: **int**, **str**, **float**, **bool**, **list**, **set**, **dict**, **tuple**
+    ``in_path``               Indicates whether the parameter is part of the path in the endpoint url. Valid values: **true** or **false**
     ``default``               Specifies a default value for the parameter if not provided by the user.
-    ``items``                 Used in the context of arrays to define the schema of items within an array.
-    ``format``                Specifies a format for the parameter value, e.g., date and email
-    ``enum``                  Lists the allowable values for the parameter.
-    ``minimum``               Defines the minimum acceptable value for numeric parameters.
-    ``maximum``               Specifies the maximum acceptable value for numeric parameters.
-    ====================      ============================================================================
+    ``format``                Specifies a format for the parameter value. For example: `2019-12-31` for a date value.
+    ``enum``                  Lists of allowable values for the parameter with data type matching the ``type`` attribute. **Usage Example**: ``enum: ["celsius`", "fahrenheit"]``
+    ``items``                 Specifies the attribute of the elements when type equals **list**, **set**, **dict**, **tuple**. **Usage Example**: ``items: {"type": "str"}``
+    ``required``              Indicates whether the parameter is mandatory or optional. Valid values: **true** or **false**
+    ========================  ============================================================================
 
-Example Configuration
-~~~~~~~~~~~~~~~~~~~~~
+Example Configuration For Tools
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code-block:: yaml
+    :caption: Tools and Function Calling Configuration Example
 
     prompt_targets:
       - name: get_weather
@@ -99,16 +98,35 @@ Example Configuration
           name: api_server
           path: /weather
 
+Example Configuration For Agents
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: yaml
+    :caption: Agent Orchestration Configuration Example
+
+    overrides:
+      use_agent_orchestrator: true
+
+    prompt_targets:
+      - name: sales_agent
+        description: handles queries related to sales and purchases
+
+      - name: issues_and_repairs
+        description: handles issues, repairs, or refunds
+
+      - name: escalate_to_human
+        description: escalates to human agent
+
+.. note::
+    Today, you can use Arch to coordinate more specific agentic scenarios via tools and function calling, or use it for high-level agent routing and hand off scenarios. In the future, we plan to offer you the ability to combine these two approaches for more complex scenarios. Please see `github issues <https://github.com/katanemo/archgw/issues/442>`_ for more details.
 
 Routing Logic
 -------------
-Prompt targets determine where and how user prompts are processed.
-Arch uses intelligent routing logic to ensure that prompts are directed to the appropriate targets based on their intent and context.
+Prompt targets determine where and how user prompts are processed. Arch uses intelligent routing logic to ensure that prompts are directed to the appropriate targets based on their intent and context.
 
 Default Targets
 ~~~~~~~~~~~~~~~
-For general-purpose prompts that do not match any specific prompt target, Arch routes them to a designated default target.
-This is useful for handling open-ended queries like document summarization or information extraction.
+For general-purpose prompts that do not match any specific prompt target, Arch routes them to a designated default target. This is useful for handling open-ended queries like document summarization or information extraction.
 
 Intent Matching
 ~~~~~~~~~~~~~~~
@@ -125,5 +143,5 @@ For example:
 Summary
 --------
 Prompt targets are essential for defining how user prompts are handled within your generative AI applications using Arch.
-By carefully configuring prompt targets, you can ensure that prompts are accurately routed, necessary parameters are extracted, and backend services are invoked seamlessly.
-This modular approach not only simplifies your application's architecture but also enhances scalability, maintainability, and overall user experience.
+
+By carefully configuring prompt targets, you can ensure that prompts are accurately routed, necessary parameters are extracted, and backend services are invoked seamlessly. This modular approach not only simplifies your application's architecture but also enhances scalability, maintainability, and overall user experience.
diff --git a/docs/source/concepts/tech_overview/listener.rst b/docs/source/concepts/tech_overview/listener.rst
index 632d5315..b6795ce6 100644
--- a/docs/source/concepts/tech_overview/listener.rst
+++ b/docs/source/concepts/tech_overview/listener.rst
@@ -5,7 +5,7 @@ Listener
 **Listener** is a top level primitive in Arch, which simplifies the configuration required to bind incoming
 connections from downstream clients, and for egress connections to LLMs (hosted or API)
 
-Arch builds on Envoy's Listener subsystem to streamline connection managemet for developers. Arch minimizes
+Arch builds on Envoy's Listener subsystem to streamline connection management for developers. Arch minimizes
 the complexity of Envoy's listener setup by using best-practices and exposing only essential settings,
 making it easier for developers to bind connections without deep knowledge of Envoy’s configuration model. This
 simplification ensures that connections are secure, reliable, and optimized for performance.
@@ -13,7 +13,7 @@ simplification ensures that connections are secure, reliable, and optimized for
 Downstream (Ingress)
 ^^^^^^^^^^^^^^^^^^^^^^
 Developers can configure Arch to accept connections from downstream clients. A downstream listener acts as the
-primary entry point for incoming traffic, handling initial connection setup, including network filtering, gurdrails,
+primary entry point for incoming traffic, handling initial connection setup, including network filtering, guardrails,
 and additional network security checks. For more details on prompt security and safety,
 see :ref:`here <arch_overview_prompt_handling>`.
 
@@ -27,7 +27,7 @@ address like ``arch.local:12000/v1`` for outgoing traffic. For more details on L
 Configure Listener
 ^^^^^^^^^^^^^^^^^^
 
-To configure a Downstream (Ingress) Listner, simply add the ``listener`` directive to your configuration file:
+To configure a Downstream (Ingress) Listener, simply add the ``listener`` directive to your configuration file:
 
 .. literalinclude:: ../includes/arch_config.yaml
     :language: yaml
diff --git a/docs/source/concepts/tech_overview/model_serving.rst b/docs/source/concepts/tech_overview/model_serving.rst
index da4b3341..53a4e377 100644
--- a/docs/source/concepts/tech_overview/model_serving.rst
+++ b/docs/source/concepts/tech_overview/model_serving.rst
@@ -5,7 +5,7 @@ Model Serving
 
 Arch is a set of `two` self-contained processes that are designed to run alongside your application
 servers (or on a separate host connected via a network). The first process is designated to manage low-level
-networking and HTTP related comcerns, and the other process is for model serving, which helps Arch make
+networking and HTTP related concerns, and the other process is for model serving, which helps Arch make
 intelligent decisions about the incoming prompts. The model server is designed to call the purpose-built
 LLMs in Arch.
 
@@ -16,7 +16,7 @@ LLMs in Arch.
 
 Arch' is designed to be deployed in your cloud VPC, on a on-premises host, and can work on devices that don't
 have a GPU. Note, GPU devices are need for fast and cost-efficient use, so that Arch (model server, specifically)
-can process prompts quickly and forward control back to the applicaton host. There are three modes in which Arch
+can process prompts quickly and forward control back to the application host. There are three modes in which Arch
 can be configured to run its **model server** subsystem:
 
 Local Serving (CPU - Moderate)
@@ -32,7 +32,7 @@ might not be available.
 Cloud Serving (GPU - Blazing Fast)
 ----------------------------------
 The command below instructs Arch to intelligently use GPUs locally for fast intent detection, but default to
-cloud serving for function calling and guardails scenarios to dramatically improve the speed and overall performance
+cloud serving for function calling and guardrails scenarios to dramatically improve the speed and overall performance
 of your applications.
 
 .. code-block:: console
@@ -40,6 +40,6 @@ of your applications.
     $ archgw up
 
 .. Note::
-    Arch's model serving in the cloud is priced at $0.05M/token (156x cheaper than GPT-4o) with averlage latency
+    Arch's model serving in the cloud is priced at $0.05M/token (156x cheaper than GPT-4o) with average latency
     of 200ms (10x faster than GPT-4o). Please refer to our :ref:`Get Started <quickstart>` to know
     how to generate API keys for model serving
diff --git a/docs/source/concepts/tech_overview/prompt.rst b/docs/source/concepts/tech_overview/prompt.rst
index d386ffd6..0ba7b316 100644
--- a/docs/source/concepts/tech_overview/prompt.rst
+++ b/docs/source/concepts/tech_overview/prompt.rst
@@ -8,7 +8,7 @@ Arch relies on Envoy's HTTP `connection management <https://www.envoyproxy.io/do
 subsystem and its **prompt handler** subsystem engineered with purpose-built LLMs to
 implement critical functionality on behalf of developers so that you can stay focused on business logic.
 
-Arch's **prompt handler** subsystem interacts with the **model subsytem** through Envoy's cluster manager system to ensure robust, resilient and fault-tolerant experience in managing incoming prompts.
+Arch's **prompt handler** subsystem interacts with the **model subsystem** through Envoy's cluster manager system to ensure robust, resilient and fault-tolerant experience in managing incoming prompts.
 
 .. seealso::
    Read more about the :ref:`model subsystem <model_serving>` and how the LLMs are hosted in Arch.
@@ -28,7 +28,7 @@ Prompt Guard
 -----------------
 
 Arch is engineered with `Arch-Guard <https://huggingface.co/collections/katanemo/arch-guard-6702bdc08b889e4bce8f446d>`_, an industry leading safety layer, powered by a
-compact and high-performimg LLM that monitors incoming prompts to detect and reject jailbreak attempts -
+compact and high-performing LLM that monitors incoming prompts to detect and reject jailbreak attempts -
 ensuring that unauthorized or harmful behaviors are intercepted early in the process.
 
 To add jailbreak guardrails, see example below:
@@ -50,7 +50,7 @@ Prompt Targets
 --------------
 
 Once a prompt passes any configured guardrail checks, Arch processes the contents of the incoming conversation
-and identifies where to forwad the conversation to via its ``prompt target`` primitve. Prompt targets are endpoints
+and identifies where to forward the conversation to via its ``prompt target`` primitive. Prompt targets are endpoints
 that receive prompts that are processed by Arch. For example, Arch enriches incoming prompts with metadata like knowing
 when a user's intent has changed so that you can build faster, more accurate RAG apps.
 
@@ -72,7 +72,7 @@ Intent Matching
 
 Arch uses fast text embedding and intent recognition approaches to first detect the intent of each incoming prompt.
 This intent matching phase analyzes the prompt's content and matches it against predefined prompt targets, ensuring that each prompt is forwarded to the most appropriate endpoint.
-Arch’s intent matching framework considers both the name and description of each prompt target, and uses a composite matching score between embedding similarity and intent classification scores to enchance accuracy in forwarding decisions.
+Arch’s intent matching framework considers both the name and description of each prompt target, and uses a composite matching score between embedding similarity and intent classification scores to enhance accuracy in forwarding decisions.
 
 - **Intent Recognition**: NLI techniques further refine the matching process by evaluating the semantic alignment between the prompt and potential targets.
 
diff --git a/docs/source/concepts/tech_overview/request_lifecycle.rst b/docs/source/concepts/tech_overview/request_lifecycle.rst
index dd3bcc8f..160eb85e 100644
--- a/docs/source/concepts/tech_overview/request_lifecycle.rst
+++ b/docs/source/concepts/tech_overview/request_lifecycle.rst
@@ -5,7 +5,7 @@ Request Lifecycle
 
 Below we describe the events in the lifecycle of a request passing through an Arch gateway instance. We first
 describe how Arch fits into the request path and then the internal events that take place following
-the arrival of a request at Arch from downtream clients. We follow the request until the corresponding
+the arrival of a request at Arch from downstream clients. We follow the request until the corresponding
 dispatch upstream and the response path.
 
 .. image:: /_static/img/network-topology-ingress-egress.jpg
@@ -59,7 +59,7 @@ The request processing path in Arch has three main parts:
   lifecycle. The downstream and upstream HTTP/2 codec lives here.
 * :ref:`Prompt handler subsystem <arch_overview_prompt_handling>` which is responsible for selecting and
   forwarding prompts ``prompt_targets`` and establishes the lifecycle of any **upstream** connection to a
-  hosted endpoint that implements domain-specific business logic for incoming promots. This is where knowledge
+  hosted endpoint that implements domain-specific business logic for incoming prompts. This is where knowledge
   of targets and endpoint health, load balancing and connection pooling exists.
 * :ref:`Model serving subsystem <model_serving>` which helps Arch make intelligent decisions about the
   incoming prompts. The model server is designed to call the purpose-built LLMs in Arch.
@@ -67,7 +67,7 @@ The request processing path in Arch has three main parts:
 The three subsystems are bridged with either the HTTP router filter, and the cluster manager subsystems of Envoy.
 
 Also, Arch utilizes `Envoy event-based thread model <https://blog.envoyproxy.io/envoy-threading-model-a8d44b922310>`_.
-A main thread is responsible forthe server lifecycle, configuration processing, stats, etc. and some number of
+A main thread is responsible for the server lifecycle, configuration processing, stats, etc. and some number of
 :ref:`worker threads <arch_overview_threading>` process requests. All threads operate around an event loop (`libevent <https://libevent.org/>`_)
 and any given downstream TCP connection will be handled by exactly one worker thread for its lifetime. Each worker
 thread maintains its own pool of TCP connections to upstream endpoints.
@@ -99,7 +99,7 @@ A brief outline of the lifecycle of a request and response using the example con
    that harmful or unwanted behaviors are detected early in the request processing pipeline.
 
 3. **Intent Matching**:
-   The decrypted data stream is deframed by the HTTP/2 codec in Arch's HTTP connection manager. Arch performs
+   The decrypted data stream is de-framed by the HTTP/2 codec in Arch's HTTP connection manager. Arch performs
    intent matching via is **prompt-handler** subsystem using the name and description of the defined prompt targets,
    determining which endpoint should handle the prompt.
 
@@ -162,7 +162,7 @@ Post-request processing
 Once a request completes, the stream is destroyed. The following also takes places:
 
 * The post-request :ref:`monitoring <monitoring>` are updated (e.g. timing, active requests, upgrades, health checks).
-  Some statistics are updated earlier however, during request processing. Stats are batchedand written by the main
+  Some statistics are updated earlier however, during request processing. Stats are batched and written by the main
   thread periodically.
 * :ref:`Access logs <arch_access_logging>` are written to the access log
 * :ref:`Trace <arch_overview_tracing>` spans are finalized. If our example request was traced, a
diff --git a/docs/source/concepts/tech_overview/terminology.rst b/docs/source/concepts/tech_overview/terminology.rst
index dff32957..0184ed40 100644
--- a/docs/source/concepts/tech_overview/terminology.rst
+++ b/docs/source/concepts/tech_overview/terminology.rst
@@ -7,12 +7,12 @@ A few definitions before we dive into the main architecture documentation. Also
 to keep things consistent in logs and traces, and introduces and clarifies concepts are is relates to LLM applications.
 
 **Agent**: An application that uses LLMs to handle wide-ranging tasks from users via prompts. This could be as simple
-as retrieving or summarizing data from an API, or being able to trigger compleix actions like adjusting ad campaigns, or
+as retrieving or summarizing data from an API, or being able to trigger complex actions like adjusting ad campaigns, or
 changing travel plans via prompts.
 
 **Arch Config**: Arch operates based on a configuration that controls the behavior of a single instance of the Arch gateway.
 This where you enable capabilities like LLM routing, fast function calling (via prompt_targets), applying guardrails, and enabling critical
-features like metrics and tracing. For the full configuration reference of `arch_config.yaml` see :ref:`here <configuration_refernce>`.
+features like metrics and tracing. For the full configuration reference of `arch_config.yaml` see :ref:`here <configuration_reference>`.
 
 **Downstream(Ingress)**: An downstream client (web application, etc.) connects to Arch, sends prompts, and receives responses.
 
@@ -37,11 +37,11 @@ code to LLMs.
 undifferentiated work in building generative AI apps. Prompt targets are endpoints that receive prompts that are processed by Arch.
 For example, Arch enriches incoming prompts with metadata like knowing when a request is a follow-up or clarifying prompt so that you
 can build faster, more accurate retrieval (RAG) apps. To support agentic apps, like scheduling travel plans or sharing comments on a
-document - via prompts, Arch uses its function calling abilities to extract critical information fromthe incoming prompt (or a set of
+document - via prompts, Arch uses its function calling abilities to extract critical information from the incoming prompt (or a set of
 prompts) needed by a downstream backend API or function call before calling it directly.
 
 **Model Serving**: Arch is a set of `two` self-contained processes that are designed to run alongside your application servers
-(or on a separate hostconnected via a network).The :ref:`model serving <model_serving>` process helps Arch make intelligent decisions
+(or on a separate host connected via a network).The :ref:`model serving <model_serving>` process helps Arch make intelligent decisions
 about the incoming prompts. The model server is designed to call the (fast) purpose-built LLMs in Arch.
 
 **Error Target**: :ref:`Error targets <error_target>` are those endpoints that receive forwarded errors from Arch when issues arise,
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 3ea0822c..8079a4c2 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -15,7 +15,7 @@ from sphinxawesome_theme.postprocess import Icons
 project = "Arch Docs"
 copyright = "2025, Katanemo Labs, Inc"
 author = "Katanemo Labs, Inc"
-release = " v0.2.1"
+release = " v0.2.6"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
diff --git a/docs/source/get_started/intro_to_arch.rst b/docs/source/get_started/intro_to_arch.rst
index 97188ab1..de00c974 100644
--- a/docs/source/get_started/intro_to_arch.rst
+++ b/docs/source/get_started/intro_to_arch.rst
@@ -3,10 +3,17 @@
 Intro to Arch
 =============
 
-Arch is an intelligent `(Layer 7) <https://www.cloudflare.com/learning/ddos/what-is-layer-7/>`_ gateway designed for generative AI apps, agents, copilots that work with prompts.
-Engineered with purpose-built large language models (LLMs), Arch handles all the critical but undifferentiated tasks related to the handling and processing of prompts, including
-detecting and rejecting jailbreak attempts, intelligently calling “backend” APIs to fulfill the user's request represented in a prompt, routing to and offering disaster recovery
-between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way.
+Arch is an intelligent proxy server designed agentic applications. **Move faster** by letting Arch handle the **pesky heavy lifting** in building agents:
+fast input clarification, agent routing, seamless integration of prompts with tools for common tasks, and unified access and observability of LLMs.
+
+Past the thrill of an AI demo, have you found yourself hitting these walls? You know, the all too familiar ones:
+
+- You break a prompt into specialized ones, but **get stuck writing routing** and handoff logic?
+- You want use new LLMs, but **struggle to quickly add LLMs** without writing integration logic?
+- You're **trapped in tedious prompting work** to clarify inputs and user intents?
+- You're **wasting cycles** choosing and integrating **code for observability** instead of it just happening transparently?
+
+And you think to yourself, can't I move faster by focusing on higher-level objectives in a language and framework agnostic way? Well, you can!
 
 .. figure:: /_static/img/arch_network_diagram_high_level.png
    :width: 100%
@@ -15,7 +22,7 @@ between upstream LLMs, and managing the observability of prompts and LLM interac
    High-level network flow of where Arch Gateway sits in your agentic stack. Designed for both ingress and egress prompt traffic.
 
 
-**The project was born out of the belief that:**
+**Arch Gateway was built by the contributors of Envoy Proxy with the belief that:**
 
   *Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests
   including secure handling, intelligent routing, robust observability, and integration with backend (API)
@@ -28,7 +35,7 @@ Arch takes a dependency on Envoy and is a self-contained process that is designe
 Arch uses Envoy's HTTP connection management subsystem, HTTP L7 filtering and telemetry capabilities to extend the functionality exclusively for prompts and LLMs.
 This gives Arch several advantages:
 
-* Arch builds on Envoy's proven success. Envoy is used at masssive scale by the leading technology companies of our time including `AirBnB <https://www.airbnb.com>`_, `Dropbox <https://www.dropbox.com>`_, `Google <https://www.google.com>`_, `Reddit <https://www.reddit.com>`_, `Stripe <https://www.stripe.com>`_, etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters: application features and business logic.
+* Arch builds on Envoy's proven success. Envoy is used at massive scale by the leading technology companies of our time including `AirBnB <https://www.airbnb.com>`_, `Dropbox <https://www.dropbox.com>`_, `Google <https://www.google.com>`_, `Reddit <https://www.reddit.com>`_, `Stripe <https://www.stripe.com>`_, etc. Its battle tested and scales linearly with usage and enables developers to focus on what really matters: application features and business logic.
 
 * Arch works with any application language. A single Arch deployment can act as gateway for AI applications written in Python, Java, C++, Go, Php, etc.
 
@@ -47,7 +54,7 @@ These LLMs are designed to be best-in-class for critical prompt-related tasks li
   With prompt guardrails you can prevent ``jailbreak attempts`` present in user's prompts without having to write a single line of code.
   To learn more about how to configure guardrails available in Arch, read :ref:`Prompt Guard <prompt_guard>`.
 
-**Traffic Management:** Arch offers several capabilities for LLM calls originating from your applications, including smart retries on errors from upstream LLMs, and automatic cutover to other LLMs configured in Arch for continuous availability and disaster recovery scenarios.
+**Traffic Management:** Arch offers several capabilities for LLM calls originating from your applications, including smart retries on errors from upstream LLMs, and automatic cut-over to other LLMs configured in Arch for continuous availability and disaster recovery scenarios.
 Arch extends Envoy's `cluster subsystem <https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/upstream/cluster_manager>`_ to manage upstream connections to LLMs so that you can build resilient AI applications.
 
 **Front/edge Gateway:** There is substantial benefit in using the same software at the edge (observability, traffic shaping algorithms, applying guardrails, etc.) as for outbound LLM inference use cases.
diff --git a/docs/source/get_started/overview.rst b/docs/source/get_started/overview.rst
index 07a347af..0f1930b1 100644
--- a/docs/source/get_started/overview.rst
+++ b/docs/source/get_started/overview.rst
@@ -3,7 +3,11 @@
 
 Overview
 ============
-Welcome to Arch, the intelligent prompt gateway designed to help developers build **fast**, **secure**, and **personalized** generative AI apps at ANY scale.
+Welcome to Arch, The intelligent (edge and LLM) proxy server for agentic applications.
+
+Move **faster** by letting Arch handle the pesky heavy lifting in building agents: **fast input clarification**, **agent routing**,
+seamless integration of prompts with **tools for common tasks**, and **unified access and observability of LLMs**.
+
 In this documentation, you will learn how to quickly set up Arch to trigger API calls via prompts, apply prompt guardrails without writing any application-level logic,
 simplify the interaction with upstream LLMs, and improve observability all while simplifying your application development process.
 
diff --git a/docs/source/get_started/quickstart.rst b/docs/source/get_started/quickstart.rst
index 268bf45d..93ceebad 100644
--- a/docs/source/get_started/quickstart.rst
+++ b/docs/source/get_started/quickstart.rst
@@ -25,7 +25,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
 
    $ python -m venv venv
    $ source venv/bin/activate   # On Windows, use: venv\Scripts\activate
-   $ pip install archgw==0.2.1
+   $ pip install archgw==0.2.6
 
 
 Build AI Agent with Arch Gateway
@@ -42,11 +42,12 @@ Create ``arch_config.yaml`` file with the following content:
 
    version: v0.1
 
-   listener:
-     address: 0.0.0.0
-     port: 10000
-     message_format: huggingface
-     connect_timeout: 0.005s
+  listeners:
+    ingress_traffic:
+      address: 0.0.0.0
+      port: 10000
+      message_format: openai
+      timeout: 30s
 
    llm_providers:
      - name: gpt-4o
@@ -144,22 +145,23 @@ Create ``arch_config.yaml`` file with the following content:
 
    version: v0.1
 
-   listener:
-     address: 0.0.0.0
-     port: 10000
-     message_format: huggingface
-     connect_timeout: 0.005s
+  listeners:
+    egress_traffic:
+      address: 0.0.0.0
+      port: 12000
+      message_format: openai
+      timeout: 30s
 
    llm_providers:
      - name: gpt-4o
        access_key: $OPENAI_API_KEY
-       provider: openai
+       provider_interface: openai
        model: gpt-4o
        default: true
 
      - name: ministral-3b
        access_key: $MISTRAL_API_KEY
-       provider: mistral
+       provider_interface: openai
        model: ministral-3b-latest
 
 Step 2. Start arch gateway
diff --git a/docs/source/guides/agent_routing.rst b/docs/source/guides/agent_routing.rst
new file mode 100644
index 00000000..5effad69
--- /dev/null
+++ b/docs/source/guides/agent_routing.rst
@@ -0,0 +1,105 @@
+.. _agent_routing:
+
+Agent Routing and Hand Off
+===========================
+
+Agent Routing and Hand Off is a key feature in Arch that enables intelligent routing of user prompts to specialized AI agents or human agents based on the nature and complexity of the user's request.
+
+This capability significantly enhances the efficiency and personalization of interactions, ensuring each prompt receives the most appropriate and effective handling. The following section describes
+the workflow, configuration, and implementation of Agent routing and hand off in Arch.
+
+#. **Agent Selection**
+   When a user submits a prompt, Arch analyzes the input to determine the intent and complexity. Based on the analysis, Arch selects the most suitable agent configured within your application to handle the specific category of the user's request—such as sales inquiries, technical issues, or complex scenarios requiring human attention.
+
+#. **Prompt Routing**
+   After selecting the appropriate agent, Arch routes the user's prompt to the designated agent's endpoint and waits for the agent to respond back with the processed output or further instructions.
+
+#. **Hand Off**
+   Based on follow-up queries from the user, Arch repeats the process of analysis, agent selection, and routing to ensure a seamless hand off between AI agents as needed.
+
+.. code-block:: yaml
+    :caption: Agent Routing and Hand Off Configuration Example
+
+    prompt_targets:
+      - name: sales_agent
+        description: Handles queries related to sales and purchases
+
+      - name: issues_and_repairs
+        description: handles issues, repairs, or refunds
+
+      - name: escalate_to_human
+        description: escalates to human agent
+
+.. code-block:: python
+    :caption: Agent Routing and Hand Off Implementation Example via FastAPI
+
+    class Agent:
+        def __init__(self, role: str, instructions: str):
+            self.system_prompt = f"You are a {role}.\n{instructions}"
+
+        def handle(self, req: ChatCompletionsRequest):
+            messages = [{"role": "system", "content": self.get_system_prompt()}] + [
+                message.model_dump() for message in req.messages
+            ]
+            return call_openai(messages, req.stream) #call_openai is a placeholder for the actual API call
+
+        def get_system_prompt(self) -> str:
+            return self.system_prompt
+
+    # Define your agents
+    AGENTS = {
+        "sales_agent": Agent(
+            role="sales agent",
+            instructions=(
+                "Always answer in a sentence or less.\n"
+                "Follow the following routine with the user:\n"
+                "1. Engage\n"
+                "2. Quote ridiculous price\n"
+                "3. Reveal caveat if user agrees."
+            ),
+        ),
+        "issues_and_repairs": Agent(
+            role="issues and repairs agent",
+            instructions="Propose a solution, offer refund if necessary.",
+        ),
+        "escalate_to_human": Agent(
+            role="human escalation agent", instructions="Escalate issues to a human."
+        ),
+        "unknown_agent": Agent(
+            role="general assistant", instructions="Assist the user in general queries."
+        ),
+    }
+
+    #handle the request from arch gateway
+    @app.post("/v1/chat/completions")
+    def completion_api(req: ChatCompletionsRequest, request: Request):
+
+        agent_name = req.metadata.get("agent-name", "unknown_agent")
+        agent = AGENTS.get(agent_name)
+        logger.info(f"Routing to agent: {agent_name}")
+
+        return agent.handle(req)
+
+.. note::
+    The above example demonstrates a simple implementation of Agent Routing and Hand Off using FastAPI. For the full implementation of this example
+    please see our `GitHub demo <https://github.com/katanemo/archgw/tree/main/demos/use_cases/orchestrating_agents>`_.
+
+Example Use Cases
+-----------------
+Agent Routing and Hand Off is particularly beneficial in scenarios such as:
+
+- **Customer Support**: Routing common customer queries to automated support agents, while escalating complex or sensitive issues to human support staff.
+- **Sales and Marketing**: Automatically directing potential leads and sales inquiries to specialized sales agents for timely and targeted follow-ups.
+- **Technical Assistance**: Managing user-reported issues, repairs, or refunds by assigning them to the correct technical or support agent efficiently.
+
+Best Practices and Tips
+------------------------
+When implementing Agent Routing and Hand Off in your applications, consider these best practices:
+
+- Clearly define agent responsibilities: Ensure each agent or human endpoint has a clear, specific description of the prompts they handle, reducing mis-routing.
+- Monitor and optimize routes: Regularly review how prompts are routed to adjust and optimize agent definitions and configurations.
+
+.. note::
+    To observe traffic to and from agents, please read more about :ref:`observability <observability>` in Arch.
+
+By carefully configuring and managing your Agent routing and hand off, you can significantly improve your application's responsiveness, performance, and overall user satisfaction.
diff --git a/docs/source/guides/function_calling.rst b/docs/source/guides/function_calling.rst
index 036d0e00..072571d2 100644
--- a/docs/source/guides/function_calling.rst
+++ b/docs/source/guides/function_calling.rst
@@ -118,6 +118,9 @@ Specify the parameters your function needs and how Arch should interpret these.
           name: api_server
           path: /weather
 
+.. Note::
+    For a complete refernce of attributes that you can configure in a prompt target, see :ref:`here <defining_prompt_target_parameters>`.
+
 Step 3: Arch Takes Over
 ~~~~~~~~~~~~~~~~~~~~~~~
 Once you have defined the functions and configured the prompt targets, Arch Gateway takes care of the remaining work.
diff --git a/docs/source/guides/includes/arch_config.yaml b/docs/source/guides/includes/arch_config.yaml
index 33c1748c..e86c6072 100644
--- a/docs/source/guides/includes/arch_config.yaml
+++ b/docs/source/guides/includes/arch_config.yaml
@@ -1,10 +1,11 @@
 version: v0.1
 
-listener:
-  address: 0.0.0.0 # or 127.0.0.1
-  port: 10000
-  # Defines how Arch should parse the content from application/json or text/pain Content-type in the http request
-  message_format: huggingface
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 # Centralized way to manage LLMs, manage keys, retry logic, failover and limits in a central way
 llm_providers:
@@ -53,11 +54,6 @@ prompt_targets:
         default: false
         enum: [true, false]
 
-error_target:
-  endpoint:
-    name: error_target_1
-    path: /error
-
 # Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
 endpoints:
   app_server:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 3588382d..aefc63bf 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -12,11 +12,11 @@ Welcome to Arch!
    <p>Build <strong>fast</strong>, <strong>observable</strong>, and <strong>personalized</strong> GenAI apps</p>
    </div>
 
-   <a href="https://www.producthunt.com/posts/arch-3?embed=true&utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-arch&#0045;3" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=565761&theme=light&period=daily" alt="Arch - Build&#0032;fast&#0044;&#0032;hyper&#0045;personalized&#0032;agents&#0032;with&#0032;intelligent&#0032;infra | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+   <a href="https://www.producthunt.com/posts/arch-3?embed=true&utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-arch&#0045;3" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=565761&theme=dark&period=daily&t=1742433071161" alt="Arch - Build&#0032;fast&#0044;&#0032;hyper&#0045;personalized&#0032;agents&#0032;with&#0032;intelligent&#0032;infra | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
 
-`Arch <https://github.com/katanemo/arch>`_ is an intelligent gateway for agents - an infrastructure primitive for GenAI (built by the contributors of `Envoy <https://www.envoyproxy.io/>`_ ). The project was born out of the belief that:
+`Arch <https://github.com/katanemo/arch>`_ is an intelligent (edge and LLM) proxy, exclusively designed for prompts and agents - and built by the contributors of the widely adopted and loved `Envoy <https://www.envoyproxy.io/>`_ ).
 
-  *Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization - all outside business logic.*
+**Move faster** by letting Arch handle the **pesky heavy lifting** in building agents: fast input clarification, agent routing, seamless integration of prompts with tools for common tasks, and unified access and observability of LLMs - all outside business logic.
 
 .. tab-set::
 
@@ -50,6 +50,7 @@ Welcome to Arch!
       :maxdepth: 2
 
       guides/prompt_guard
+      guides/agent_routing
       guides/function_calling
       guides/observability/observability
 
diff --git a/docs/source/resources/configuration_reference.rst b/docs/source/resources/configuration_reference.rst
index ef7baceb..af60a642 100644
--- a/docs/source/resources/configuration_reference.rst
+++ b/docs/source/resources/configuration_reference.rst
@@ -1,9 +1,9 @@
-.. _configuration_refernce:
+.. _configuration_reference:
 
 Configuration Reference
 =======================
 
-The following is a complete reference of the ``arch_conifg.yml`` that controls the behavior of a single instance of
+The following is a complete reference of the ``arch_config.yml`` that controls the behavior of a single instance of
 the Arch gateway. This where you enable capabilities like routing to upstream LLm providers, defining prompt_targets
 where prompts get routed to, apply guardrails, and enable critical agent observability features.
 
diff --git a/docs/source/resources/includes/arch_config_full_reference.yaml b/docs/source/resources/includes/arch_config_full_reference.yaml
index 2389389c..5ef2639c 100644
--- a/docs/source/resources/includes/arch_config_full_reference.yaml
+++ b/docs/source/resources/includes/arch_config_full_reference.yaml
@@ -1,14 +1,16 @@
 version: v0.1
 
 listeners:
-  prompt_gateway:
+  ingress_traffic:
     address: 0.0.0.0
     port: 10000
     message_format: openai
     timeout: 5s
-  llm_gateway:
+  egress_traffic:
     address: 0.0.0.0
     port: 12000
+    message_format: openai
+    timeout: 5s
 
 # Arch creates a round-robin load balancing between different endpoints, managed via the cluster subsystem.
 endpoints:
@@ -33,14 +35,6 @@ llm_providers:
     access_key: $OPENAI_API_KEY
     model: gpt-4o
     default: true
-    rate_limits:
-      selector: #optional headers, to add rate limiting based on http headers like JWT tokens or API keys
-        http_header:
-          name: Authorization
-          value: "" # Empty value means each separate value has a separate limit
-      limit:
-        tokens: 100000 # Tokens per unit
-        unit: minute
 
   - name: Mistral8x7b
     provider_interface: openai
@@ -54,8 +48,8 @@ llm_providers:
 
 # provides a way to override default settings for the arch system
 overrides:
-  # By default Arch uses an NLI + embedding approach to match an incomming prompt to a prompt target.
-  # The intent matching threshold is kept at 0.80, you can overide this behavior if you would like
+  # By default Arch uses an NLI + embedding approach to match an incoming prompt to a prompt target.
+  # The intent matching threshold is kept at 0.80, you can override this behavior if you would like
   prompt_target_intent_matching_threshold: 0.60
 
 # default system prompt used by all prompt targets
@@ -96,11 +90,6 @@ prompt_targets:
         default: false
         enum: [true, false]
 
-error_target:
-  endpoint:
-    name: error_target_1
-    path: /error
-
 tracing:
   # sampling rate. Note by default Arch works on OpenTelemetry compatible tracing.
   sampling_rate: 0.1
diff --git a/model_server/poetry.lock b/model_server/poetry.lock
index c08f77ca..a050d664 100644
--- a/model_server/poetry.lock
+++ b/model_server/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "accelerate"
-version = "1.2.1"
+version = "1.6.0"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.9.0"
 files = [
-    {file = "accelerate-1.2.1-py3-none-any.whl", hash = "sha256:be1cbb958cf837e7cdfbde46b812964b1b8ae94c9c7d94d921540beafcee8ddf"},
-    {file = "accelerate-1.2.1.tar.gz", hash = "sha256:03e161fc69d495daf2b9b5c8d5b43d06e2145520c04727b5bda56d49f1a43ab5"},
+    {file = "accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa"},
+    {file = "accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32"},
 ]
 
 [package.dependencies]
@@ -18,18 +18,18 @@ packaging = ">=20.0"
 psutil = "*"
 pyyaml = "*"
 safetensors = ">=0.4.3"
-torch = ">=1.10.0"
+torch = ">=2.0.0"
 
 [package.extras]
 deepspeed = ["deepspeed"]
-dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.6.4,<0.7.0)"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.11.2,<0.12.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.11.2,<0.12.0)"]
 rich = ["rich"]
 sagemaker = ["sagemaker"]
 test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"]
-test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
-testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"]
+test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "tensorboard", "wandb"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
 [[package]]
 name = "annotated-types"
@@ -44,13 +44,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.8.0"
+version = "4.9.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
-    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
+    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
 ]
 
 [package.dependencies]
@@ -60,8 +60,8 @@ sniffio = ">=1.1"
 typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -83,13 +83,13 @@ tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
 
 [[package]]
 name = "certifi"
-version = "2024.12.14"
+version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
-    {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
+    {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
+    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
 ]
 
 [[package]]
@@ -220,42 +220,42 @@ files = [
 
 [[package]]
 name = "dateparser"
-version = "1.2.0"
+version = "1.2.1"
 description = "Date parsing library designed to parse dates from HTML pages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "dateparser-1.2.0-py2.py3-none-any.whl", hash = "sha256:0b21ad96534e562920a0083e97fd45fa959882d4162acc358705144520a35830"},
-    {file = "dateparser-1.2.0.tar.gz", hash = "sha256:7975b43a4222283e0ae15be7b4999d08c9a70e2d378ac87385b1ccf2cffbbb30"},
+    {file = "dateparser-1.2.1-py3-none-any.whl", hash = "sha256:bdcac262a467e6260030040748ad7c10d6bacd4f3b9cdb4cfd2251939174508c"},
+    {file = "dateparser-1.2.1.tar.gz", hash = "sha256:7e4919aeb48481dbfc01ac9683c8e20bfe95bb715a38c1e9f6af889f4f30ccc3"},
 ]
 
 [package.dependencies]
-python-dateutil = "*"
-pytz = "*"
-regex = "<2019.02.19 || >2019.02.19,<2021.8.27 || >2021.8.27"
-tzlocal = "*"
+python-dateutil = ">=2.7.0"
+pytz = ">=2024.2"
+regex = ">=2015.06.24,<2019.02.19 || >2019.02.19,<2021.8.27 || >2021.8.27"
+tzlocal = ">=0.2"
 
 [package.extras]
-calendars = ["convertdate", "hijri-converter"]
-fasttext = ["fasttext"]
-langdetect = ["langdetect"]
+calendars = ["convertdate (>=2.2.1)", "hijridate"]
+fasttext = ["fasttext (>=0.9.1)", "numpy (>=1.19.3,<2)"]
+langdetect = ["langdetect (>=1.0.0)"]
 
 [[package]]
 name = "deprecated"
-version = "1.2.15"
+version = "1.2.18"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
 files = [
-    {file = "Deprecated-1.2.15-py2.py3-none-any.whl", hash = "sha256:353bc4a8ac4bfc96800ddab349d89c25dec1079f65fd53acdcc1e0b975b21320"},
-    {file = "deprecated-1.2.15.tar.gz", hash = "sha256:683e561a90de76239796e6b6feac66b99030d2dd3fcf61ef996330f14bbb9b0d"},
+    {file = "Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec"},
+    {file = "deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d"},
 ]
 
 [package.dependencies]
 wrapt = ">=1.10,<2"
 
 [package.extras]
-dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
 
 [[package]]
 name = "distro"
@@ -304,29 +304,29 @@ standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "htt
 
 [[package]]
 name = "filelock"
-version = "3.16.1"
+version = "3.18.0"
 description = "A platform independent file lock."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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-    {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
+    {file = "filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de"},
+    {file = "filelock-3.18.0.tar.gz", hash = "sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2"},
 ]
 
 [package.extras]
-docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
 typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "fsspec"
-version = "2024.12.0"
+version = "2025.3.2"
 description = "File-system specification"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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+    {file = "fsspec-2025.3.2-py3-none-any.whl", hash = "sha256:2daf8dc3d1dfa65b6aa37748d112773a7a08416f6c70d96b264c96476ecaf711"},
+    {file = "fsspec-2025.3.2.tar.gz", hash = "sha256:e52c77ef398680bbd6a98c0e628fbc469491282981209907bbc8aea76a04fdc6"},
 ]
 
 [package.extras]
@@ -353,93 +353,89 @@ sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
 test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
-test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
 test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.66.0"
+version = "1.70.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"},
 ]
 
 [package.dependencies]
-protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
+protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0"
 
 [package.extras]
-grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
+grpc = ["grpcio (>=1.44.0,<2.0.0)"]
 
 [[package]]
 name = "grpcio"
-version = "1.69.0"
+version = "1.71.0"
 description = "HTTP/2-based RPC framework"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 [package.extras]
-protobuf = ["grpcio-tools (>=1.69.0)"]
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 [[package]]
 name = "h11"
@@ -454,13 +450,13 @@ files = [
 
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 [package.dependencies]
@@ -500,13 +496,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
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+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
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@@ -571,24 +568,24 @@ type = ["pytest-mypy"]
 
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 [[package]]
@@ -790,66 +787,66 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
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 [[package]]
 name = "opentelemetry-api"
@@ -1259,69 +1257,63 @@ testing = ["pytest", "pytest-benchmark"]
 
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 test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
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 [package.dependencies]
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 typing-extensions = ">=4.12.2"
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 [package.extras]
 email = ["email-validator (>=2.0.0)"]
@@ -1329,111 +1321,110 @@ timezone = ["tzdata"]
 
 [[package]]
 name = "pydantic-core"
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 [package.dependencies]
@@ -1441,13 +1432,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pytest"
-version = "8.3.4"
+version = "8.3.5"
 description = "pytest: simple powerful testing with Python"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -1463,13 +1454,13 @@ dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments
 
 [[package]]
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-version = "0.25.2"
+version = "0.26.0"
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 [package.dependencies]
@@ -1481,13 +1472,13 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
 
 [[package]]
 name = "pytest-httpserver"
-version = "1.1.0"
+version = "1.1.3"
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-python-versions = ">=3.8"
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 [package.dependencies]
@@ -1495,13 +1486,13 @@ Werkzeug = ">=2.0.0"
 
 [[package]]
 name = "pytest-retry"
-version = "1.6.3"
+version = "1.7.0"
 description = "Adds the ability to retry flaky tests in CI environments"
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 [package.dependencies]
@@ -1526,13 +1517,13 @@ six = ">=1.5"
 
 [[package]]
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+version = "2025.2"
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 python-versions = "*"
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 [[package]]
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 python-versions = ">=3.9"
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 [[package]]
 name = "six"
@@ -1836,26 +1827,26 @@ dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
 [[package]]
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@@ -1984,71 +1975,74 @@ telegram = ["requests"]
 
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 ]
 
 [[package]]
@@ -2261,4 +2283,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "b8e46140dfec3638e577f0e40f6f46dfc35d46390fb63e7ac687323009f2dbce"
+content-hash = "88cf76348bd82068cfc2e48d082ac09c9ae50c829f6c9bcb6e07b879214d6f0d"
diff --git a/model_server/pyproject.toml b/model_server/pyproject.toml
index 397b58fb..9c471f58 100644
--- a/model_server/pyproject.toml
+++ b/model_server/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "archgw_modelserver"
-version = "0.2.1"
+version = "0.2.6"
 description = "A model server for serving models"
 authors = ["Katanemo Labs, Inc <info@katanemo.com>"]
 license = "Apache 2.0"
@@ -29,6 +29,7 @@ opentelemetry-instrumentation-fastapi = "^0.49b0"
 overrides = "^7.7.0"
 pytest-retry = "^1.6.3"
 pytest-httpserver = "^1.1.0"
+setuptools = "75.5.0"
 
 [tool.poetry.scripts]
 archgw_modelserver = "src.cli:main"
diff --git a/model_server/src/commons/globals.py b/model_server/src/commons/globals.py
index 68c2e53a..a39be7c3 100644
--- a/model_server/src/commons/globals.py
+++ b/model_server/src/commons/globals.py
@@ -3,8 +3,8 @@ from openai import OpenAI
 from src.commons.utils import get_model_server_logger
 from src.core.guardrails import get_guardrail_handler
 from src.core.function_calling import (
-    ArchIntentConfig,
-    ArchIntentHandler,
+    ArchAgentConfig,
+    ArchAgentHandler,
     ArchFunctionConfig,
     ArchFunctionHandler,
 )
@@ -15,22 +15,27 @@ logger = get_model_server_logger()
 
 
 # Define the client
-ARCH_ENDPOINT = os.getenv("ARCH_ENDPOINT", "https://api.fc.archgw.com/v1")
+# ARCH_ENDPOINT = os.getenv("ARCH_ENDPOINT", "https://archfc.katanemo.dev/v1")
+# use temporary endpoint until we deprecate archfc-v1.0 from archfc.katanemo.dev
+# and officially release archfc-v1.1 on archfc.katanemo.dev
+ARCH_ENDPOINT = os.getenv("ARCH_ENDPOINT", "http://34.72.123.163:8000/v1")
 ARCH_API_KEY = "EMPTY"
 ARCH_CLIENT = OpenAI(base_url=ARCH_ENDPOINT, api_key=ARCH_API_KEY)
+ARCH_AGENT_CLIENT = ARCH_CLIENT
 
 # Define model names
 ARCH_INTENT_MODEL_ALIAS = "Arch-Intent"
 ARCH_FUNCTION_MODEL_ALIAS = "Arch-Function"
+ARCH_AGENT_MODEL_ALIAS = ARCH_FUNCTION_MODEL_ALIAS
 ARCH_GUARD_MODEL_ALIAS = "katanemo/Arch-Guard"
 
 # Define model handlers
 handler_map = {
-    "Arch-Intent": ArchIntentHandler(
-        ARCH_CLIENT, ARCH_INTENT_MODEL_ALIAS, ArchIntentConfig
-    ),
     "Arch-Function": ArchFunctionHandler(
         ARCH_CLIENT, ARCH_FUNCTION_MODEL_ALIAS, ArchFunctionConfig
     ),
+    "Arch-Agent": ArchAgentHandler(
+        ARCH_AGENT_CLIENT, ARCH_AGENT_MODEL_ALIAS, ArchAgentConfig
+    ),
     "Arch-Guard": get_guardrail_handler(ARCH_GUARD_MODEL_ALIAS),
 }
diff --git a/model_server/src/core/function_calling.py b/model_server/src/core/function_calling.py
index 99dd29ba..bec5dfdf 100644
--- a/model_server/src/core/function_calling.py
+++ b/model_server/src/core/function_calling.py
@@ -1,8 +1,8 @@
 import ast
+import copy
 import json
 import random
 import builtins
-import textwrap
 import src.commons.utils as utils
 
 from openai import OpenAI
@@ -21,179 +21,25 @@ from src.core.utils.model_utils import (
 logger = utils.get_model_server_logger()
 
 
-class ArchIntentConfig:
-    TASK_PROMPT = textwrap.dedent(
-        """
-    You are a helpful assistant.
-    """
-    ).strip()
-
-    TOOL_PROMPT_TEMPLATE = textwrap.dedent(
-        """
-    You task is to check if there are any tools that can be used to help the last user message in conversations according to the available tools listed below.
-
-    <tools>
-    {tool_text}
-    </tools>
-    """
-    ).strip()
-
-    FORMAT_PROMPT = textwrap.dedent(
-        """
-    Provide your tool assessment for ONLY THE LAST USER MESSAGE in the above conversation:
-    - First line must read 'Yes' or 'No'.
-    - If yes, a second line must include a comma-separated list of tool indexes.
-    """
-    ).strip()
-
-    EXTRA_INSTRUCTION = "Are there any tools can help?"
-
-    GENERATION_PARAMS = {
-        "temperature": 0.01,
-        "max_tokens": 1,
-        "stop_token_ids": [151645],
-    }
-
-
-class ArchIntentHandler(ArchBaseHandler):
-    def __init__(self, client: OpenAI, model_name: str, config: ArchIntentConfig):
-        """
-        Initializes the intent handler.
-
-        Args:
-            client (OpenAI): An OpenAI client instance.
-            model_name (str): Name of the model to use.
-            config (ArchIntentConfig): The configuration for Arch-Intent.
-        """
-
-        super().__init__(
-            client,
-            model_name,
-            config.TASK_PROMPT,
-            config.TOOL_PROMPT_TEMPLATE,
-            config.FORMAT_PROMPT,
-            config.GENERATION_PARAMS,
-        )
-
-        self.extra_instruction = config.EXTRA_INSTRUCTION
-
-    @override
-    def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
-        """
-        Converts a list of tools into a JSON-like format with indexed keys.
-
-        Args:
-            tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
-
-        Returns:
-            str: A string representation of converted tools.
-        """
-
-        converted = [
-            json.dumps({"index": f"T{idx}"} | tool) for idx, tool in enumerate(tools)
-        ]
-        return "\n".join(converted)
-
-    def detect_intent(self, content: str) -> bool:
-        """
-        Detect if any intent match with prompts
-
-        Args:
-            content: str: Model response that contains intent detection results
-
-        Returns:
-            bool: A boolean value to indicate if any intent match with prompts or not
-        """
-        if hasattr(content.choices[0].message, "content"):
-            return content.choices[0].message.content == "Yes"
-        else:
-            return False
-
-    @override
-    async def chat_completion(self, req: ChatMessage) -> ChatCompletionResponse:
-        """
-        Generates a chat completion for a given request.
-
-        Args:
-            req (ChatMessage): A chat message request object.
-
-        Returns:
-            ChatCompletionResponse: The model's response to the chat request.
-
-        Note:
-            Currently only support vllm inference
-        """
-        logger.info("[Arch-Intent] - ChatCompletion")
-
-        # In the case that no tools are available, simply return `No` to avoid making a call
-        if len(req.tools) == 0:
-            model_response = Message(content="No", tool_calls=[])
-            logger.info("No tools found, return `No` as the model response.")
-        else:
-            messages = self._process_messages(
-                req.messages, req.tools, self.extra_instruction
-            )
-
-            logger.info(f"[request to arch-fc (intent)]: {json.dumps(messages)}")
-
-            model_response = self.client.chat.completions.create(
-                messages=messages,
-                model=self.model_name,
-                stream=False,
-                extra_body=self.generation_params,
-            )
-
-            logger.info(f"[response]: {json.dumps(model_response.model_dump())}")
-
-            model_response = Message(
-                content=model_response.choices[0].message.content, tool_calls=[]
-            )
-
-        chat_completion_response = ChatCompletionResponse(
-            choices=[Choice(message=model_response)], model=self.model_name
-        )
-
-        return chat_completion_response
-
-
-# =============================================================================================================
+# ==============================================================================================================================================
 
 
 class ArchFunctionConfig:
-    TASK_PROMPT = textwrap.dedent(
-        """
-    You are a helpful assistant.
+    TASK_PROMPT = (
+        "You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
+        "\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{tools}\n</tools>"
+        "\n\nYour task is to decide which functions are needed and collect missing parameters if necessary."
+    )
 
-    Today's date: {}
-    """.format(
-            utils.get_today_date()
-        )
-    ).strip()
-
-    TOOL_PROMPT_TEMPLATE = textwrap.dedent(
-        """
-    # Tools
-
-    You may call one or more functions to assist with the user query.
-
-    You are provided with function signatures within <tools></tools> XML tags:
-    <tools>
-    {tool_text}
-    </tools>
-    """
-    ).strip()
-
-    FORMAT_PROMPT = textwrap.dedent(
-        """
-    For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
-    <tool_call>
-    {"name": <function-name>, "arguments": <args-json-object>}
-    </tool_call>
-    """
-    ).strip()
+    FORMAT_PROMPT = (
+        "\n\nBased on your analysis, provide your response in one of the following JSON formats:"
+        '\n1. If no functions are needed:\n```json\n{"response": "Your response text here"}\n```'
+        '\n2. If functions are needed but some required parameters are missing:\n```json\n{"required_functions": ["func_name1", "func_name2", ...], "clarification": "Text asking for missing parameters"}\n```'
+        '\n3. If functions are needed and all required parameters are available:\n```json\n{"tool_calls": [{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},... (more tool calls as required)]}\n```'
+    )
 
     GENERATION_PARAMS = {
-        "temperature": 0.6,
+        "temperature": 0.1,
         "top_p": 1.0,
         "top_k": 10,
         "max_tokens": 1024,
@@ -202,22 +48,6 @@ class ArchFunctionConfig:
         "top_logprobs": 10,
     }
 
-    PREFILL_CONFIG = {
-        "prefill_params": {
-            "continue_final_message": True,
-            "add_generation_prompt": False,
-        },
-        "prefill_prefix": [
-            "May",
-            "Could",
-            "Sure",
-            "Definitely",
-            "Certainly",
-            "Of course",
-            "Can",
-        ],
-    }
-
     SUPPORT_DATA_TYPES = ["int", "float", "bool", "str", "list", "tuple", "set", "dict"]
 
 
@@ -241,13 +71,17 @@ class ArchFunctionHandler(ArchBaseHandler):
             client,
             model_name,
             config.TASK_PROMPT,
-            config.TOOL_PROMPT_TEMPLATE,
             config.FORMAT_PROMPT,
             config.GENERATION_PARAMS,
         )
 
-        self.prefill_params = config.PREFILL_CONFIG["prefill_params"]
-        self.prefill_prefix = config.PREFILL_CONFIG["prefill_prefix"]
+        self.generation_params = self.generation_params | {
+            "continue_final_message": True,
+            "add_generation_prompt": False,
+        }
+
+        self.default_prefix = '```json\n{"'
+        self.clarify_prefix = '```json\n{"required_functions":'
 
         self.hallucination_state = None
 
@@ -270,7 +104,7 @@ class ArchFunctionHandler(ArchBaseHandler):
             str: A string representation of converted tools.
         """
 
-        converted = [json.dumps(tool) for tool in tools]
+        converted = [json.dumps(tool["function"], ensure_ascii=False) for tool in tools]
         return "\n".join(converted)
 
     def _fix_json_string(self, json_str: str) -> str:
@@ -318,10 +152,14 @@ class ArchFunctionHandler(ArchBaseHandler):
             unmatched_opening = stack.pop()
             fixed_str += opening_bracket[unmatched_opening]
 
-        # Attempt to parse the corrected string to ensure it’s valid JSON
-        return fixed_str.replace("'", '"')
+        try:
+            fixed_str = json.loads(fixed_str)
+        except Exception:
+            fixed_str = json.loads(fixed_str.replace("'", '"'))
 
-    def _extract_tool_calls(self, content: str) -> Dict[str, any]:
+        return json.dumps(fixed_str)
+
+    def _parse_model_response(self, content: str) -> Dict[str, any]:
         """
         Extracts tool call information from a given string.
 
@@ -330,48 +168,55 @@ class ArchFunctionHandler(ArchBaseHandler):
 
         Returns:
             Dict: A dictionary of extraction, including:
-                - "result": A list of tool call dictionaries.
-                - "status": A boolean indicating if the extraction was valid.
-                - "message": An error message or exception if extraction failed.
+                - "required_functions": A list of detected intents.
+                - "clarification": Text to collect missing parameters
+                - "tool_calls": A list of tool call dictionaries.
+                - "is_valid": A boolean indicating if the extraction was valid.
+                - "error_message": An error message or exception if parsing failed.
         """
 
-        tool_calls, is_valid, error_message = [], True, ""
+        response_dict = {
+            "raw_response": [],
+            "response": [],
+            "required_functions": [],
+            "clarification": "",
+            "tool_calls": [],
+            "is_valid": True,
+            "error_message": "",
+        }
 
-        flag = False
-        for line in content.split("\n"):
-            if not is_valid:
-                break
+        try:
+            if content.startswith("```") and content.endswith("```"):
+                content = content.strip("```").strip()
+                if content.startswith("json"):
+                    content = content[4:].strip()
 
-            if "<tool_call>" == line:
-                flag = True
-            elif "</tool_call>" == line:
-                flag = False
-            else:
-                if flag:
-                    try:
-                        tool_content = json.loads(line)
-                    except Exception as e:
-                        fixed_content = self._fix_json_string(line)
-                        try:
-                            tool_content = json.loads(fixed_content)
-                        except Exception:
-                            tool_calls, is_valid, error_message = [], False, e
-                            break
+            content = self._fix_json_string(content)
+            response_dict["raw_response"] = f"```json\n{content}\n```"
 
-                    tool_calls.append(
-                        {
-                            "id": f"call_{random.randint(1000, 10000)}",
-                            "type": "function",
-                            "function": {
-                                "name": tool_content["name"],
-                                "arguments": tool_content["arguments"],
-                            },
-                        }
-                    )
+            model_response = json.loads(content)
+            response_dict["response"] = model_response.get("response", "")
+            response_dict["required_functions"] = model_response.get(
+                "required_functions", []
+            )
+            response_dict["clarification"] = model_response.get("clarification", "")
 
-                flag = False
+            for tool_call in model_response.get("tool_calls", []):
+                response_dict["tool_calls"].append(
+                    {
+                        "id": f"call_{random.randint(1000, 10000)}",
+                        "type": "function",
+                        "function": {
+                            "name": tool_call.get("name", ""),
+                            "arguments": tool_call.get("arguments", {}),
+                        },
+                    }
+                )
+        except Exception as e:
+            response_dict["is_valid"] = False
+            response_dict["error_message"] = f"Fail to parse model responses: {e}"
 
-        return {"result": tool_calls, "status": is_valid, "message": error_message}
+        return response_dict
 
     def _convert_data_type(self, value: str, target_type: str):
         # TODO: Add more conversion rules as needed
@@ -403,15 +248,18 @@ class ArchFunctionHandler(ArchBaseHandler):
                 - "message": An error message.
         """
 
-        is_valid, invalid_tool_call, error_message = True, None, ""
+        verification_dict = {
+            "is_valid": True,
+            "invalid_tool_call": {},
+            "error_message": "",
+        }
 
         functions = {}
         for tool in tools:
-            if tool["type"] == "function":
-                functions[tool["function"]["name"]] = tool["function"]["parameters"]
+            functions[tool["function"]["name"]] = tool["function"]["parameters"]
 
         for tool_call in tool_calls:
-            if not is_valid:
+            if not verification_dict["is_valid"]:
                 break
 
             func_name = tool_call["function"]["name"]
@@ -419,28 +267,32 @@ class ArchFunctionHandler(ArchBaseHandler):
 
             # Check whether the function is available or not
             if func_name not in functions:
-                is_valid = False
-                invalid_tool_call = tool_call
-                error_message = f"{func_name} is not defined!"
-                break
-
+                verification_dict["is_valid"] = False
+                verification_dict["invalid_tool_call"] = tool_call
+                verification_dict["error_message"] = f"{func_name} is not available!"
             else:
                 # Check if all the requried parameters can be found in the tool calls
                 for required_param in functions[func_name].get("required", []):
                     if required_param not in func_args:
-                        is_valid = False
-                        invalid_tool_call = tool_call
-                        error_message = f"`{required_param}` is requiried by the function `{func_name}` but not found in the tool call!"
+                        verification_dict["is_valid"] = False
+                        verification_dict["invalid_tool_call"] = tool_call
+                        verification_dict[
+                            "error_message"
+                        ] = f"`{required_param}` is required by the function `{func_name}` but not found in the tool call!"
                         break
 
                 # Verify the data type of each parameter in the tool calls
                 function_properties = functions[func_name]["properties"]
 
+                logger.info("== func_args ==")
+                logger.info(func_args)
                 for param_name in func_args:
                     if param_name not in function_properties:
-                        is_valid = False
-                        invalid_tool_call = tool_call
-                        error_message = f"Parameter `{param_name}` is not defined in the function `{func_name}`."
+                        verification_dict["is_valid"] = False
+                        verification_dict["invalid_tool_call"] = tool_call
+                        verification_dict[
+                            "error_message"
+                        ] = f"Parameter `{param_name}` is not defined in the function `{func_name}`."
                         break
                     else:
                         param_value = func_args[param_name]
@@ -454,22 +306,22 @@ class ArchFunctionHandler(ArchBaseHandler):
                                     param_value, data_type
                                 )
                                 if not isinstance(param_value, data_type):
-                                    is_valid = False
-                                    invalid_tool_call = tool_call
-                                    error_message = f"Parameter `{param_name}` is expected to have the data type `{data_type}`, got `{type(param_value)}`."
+                                    verification_dict["is_valid"] = False
+                                    verification_dict["invalid_tool_call"] = tool_call
+                                    verification_dict[
+                                        "error_message"
+                                    ] = f"Parameter `{param_name}` is expected to have the data type `{data_type}`, got `{type(param_value)}`."
                                     break
                         else:
-                            error_message = (
-                                f"Data type `{target_type}` is not supported."
-                            )
+                            verification_dict["is_valid"] = False
+                            verification_dict["invalid_tool_call"] = tool_call
+                            verification_dict[
+                                "error_message"
+                            ] = f"Data type `{target_type}` is not supported."
 
-        return {
-            "status": is_valid,
-            "invalid_tool_call": invalid_tool_call,
-            "message": error_message,
-        }
+        return verification_dict
 
-    def _add_prefill_message(self, messages: List[Dict[str, str]]):
+    def _prefill_message(self, messages: List[Dict[str, str]], prefill_message):
         """
         Update messages and generation params for prompt prefilling
 
@@ -479,29 +331,7 @@ class ArchFunctionHandler(ArchBaseHandler):
         Returns:
             prefill_messages (List[Dict[str, str]]): A list of messages.
         """
-
-        return messages + [
-            {
-                "role": "assistant",
-                "content": random.choice(self.prefill_prefix),
-            }
-        ]
-
-    def _engage_parameter_gathering(self, messages: List[Dict[str, str]]):
-        """
-        Engage parameter gathering for tool calls
-        """
-
-        # TODO: log enaging parameter gathering
-        prefill_response = self.client.chat.completions.create(
-            messages=self._add_prefill_message(messages),
-            model=self.model_name,
-            extra_body={
-                **self.generation_params,
-                **self.prefill_params,
-            },
-        )
-        return prefill_response
+        return messages + [{"role": "assistant", "content": prefill_message}]
 
     @override
     async def chat_completion(self, req: ChatMessage) -> ChatCompletionResponse:
@@ -523,78 +353,168 @@ class ArchFunctionHandler(ArchBaseHandler):
             req.messages, req.tools, metadata=req.metadata
         )
 
-        logger.info(f"[request to arch-fc]: {json.dumps(messages)}")
+        logger.info(
+            f"[request to arch-fc]: model: {self.model_name}, extra_body: {self.generation_params}, body: {json.dumps(messages)}"
+        )
 
         # always enable `stream=True` to collect model responses
         response = self.client.chat.completions.create(
-            messages=messages,
+            messages=self._prefill_message(messages, self.default_prefix),
             model=self.model_name,
             stream=True,
             extra_body=self.generation_params,
         )
 
-        # initialize the hallucination handler, which is an iterator
-        self.hallucination_state = HallucinationState(
-            response_iterator=response, function=req.tools
-        )
-
+        use_agent_orchestrator = req.metadata.get("use_agent_orchestrator", False)
         model_response = ""
+        if use_agent_orchestrator:
+            for chunk in response:
+                if len(chunk.choices) > 0 and chunk.choices[0].delta.content:
+                    model_response += chunk.choices[0].delta.content
+            logger.info(f"[Agent Orchestrator]: response received: {model_response}")
+        else:
+            # initialize the hallucination handler, which is an iterator
+            self.hallucination_state = HallucinationState(
+                response_iterator=response, function=req.tools
+            )
 
-        has_tool_calls, has_hallucination = None, False
-        for _ in self.hallucination_state:
-            # check if the first token is <tool_call>
-            if len(self.hallucination_state.tokens) > 0 and has_tool_calls is None:
-                if self.hallucination_state.tokens[0] == "<tool_call>":
-                    has_tool_calls = True
-                else:
-                    has_tool_calls = False
+            has_tool_calls, has_hallucination = None, False
+            for _ in self.hallucination_state:
+                # check if moodel response starts with tool calls, we do it after 5 tokens because we only check the first part of the response.
+                if len(self.hallucination_state.tokens) > 5 and has_tool_calls is None:
+                    content = "".join(self.hallucination_state.tokens)
+                    if "tool_calls" in content:
+                        has_tool_calls = True
+                    else:
+                        has_tool_calls = False
+
+                # if the model is hallucinating, start parameter gathering
+                if self.hallucination_state.hallucination is True:
+                    has_hallucination = True
                     break
 
-            # if the model is hallucinating, start parameter gathering
-            if self.hallucination_state.hallucination is True:
-                has_hallucination = True
-                break
-
-        if has_tool_calls:
-            if has_hallucination:
+            if has_tool_calls and has_hallucination:
                 # start prompt prefilling if hallcuination is found in tool calls
                 logger.info(
                     f"[Hallucination]: {self.hallucination_state.error_message}"
                 )
-                prefill_response = self._engage_parameter_gathering(messages)
-                model_response = prefill_response.choices[0].message.content
+                response = self.client.chat.completions.create(
+                    messages=self._prefill_message(messages, self.clarify_prefix),
+                    model=self.model_name,
+                    stream=False,
+                    extra_body=self.generation_params,
+                )
+                model_response = response.choices[0].message.content
             else:
                 model_response = "".join(self.hallucination_state.tokens)
-        else:
-            # start parameter gathering if the model is not generating tool calls
-            prefill_response = self._engage_parameter_gathering(messages)
-            model_response = prefill_response.choices[0].message.content
 
         # Extract tool calls from model response
-        extracted = self._extract_tool_calls(model_response)
+        response_dict = self._parse_model_response(model_response)
+        logger.info(f"[arch-fc]: raw model response: {response_dict['raw_response']}")
 
-        if len(extracted["result"]) and extracted["status"]:
-            verified = self._verify_tool_calls(
-                tools=req.tools, tool_calls=extracted["result"]
-            )
-
-            if verified["status"]:
-                logger.info(
-                    f"[Tool calls]: {json.dumps([tool_call['function'] for tool_call in extracted['result']])}"
-                )
-                model_response = Message(content="", tool_calls=extracted["result"])
+        # General model response
+        if response_dict.get("response", ""):
+            model_message = Message(content="", tool_calls=[])
+        # Parameter gathering
+        elif response_dict.get("required_functions", []):
+            if not use_agent_orchestrator:
+                clarification = response_dict.get("clarification", "")
+                model_message = Message(content=clarification, tool_calls=[])
             else:
-                logger.error(f"Invalid tool call - {verified['message']}")
-                # raise ValueError(
-                #     f"[Arch-Function]: Invalid tool call - {verified['message']}"
-                # )
+                model_message = Message(content="", tool_calls=[])
+        # Function Calling
+        elif response_dict.get("tool_calls", []):
+            if response_dict["is_valid"]:
+                if not use_agent_orchestrator:
+                    verification_dict = self._verify_tool_calls(
+                        tools=req.tools, tool_calls=response_dict["tool_calls"]
+                    )
+
+                    if verification_dict["is_valid"]:
+                        logger.info(
+                            f"[Tool calls]: {json.dumps([tool_call['function'] for tool_call in response_dict['tool_calls']])}"
+                        )
+                        model_message = Message(
+                            content="", tool_calls=response_dict["tool_calls"]
+                        )
+                    else:
+                        logger.error(
+                            f"Invalid tool call - {verification_dict['error_message']}"
+                        )
+                        model_message = Message(content="", tool_calls=[])
+                else:
+                    # skip tool call verification if using agent orchestrator
+                    logger.info(
+                        f"[Tool calls]: {json.dumps([tool_call['function'] for tool_call in response_dict['tool_calls']])}"
+                    )
+                    model_message = Message(
+                        content="", tool_calls=response_dict["tool_calls"]
+                    )
+
+            else:
+                # Response with tool calls but invalid
+                model_message = Message(content="", tool_calls=[])
+        # Response not in the desired format
         else:
-            model_response = Message(content=model_response, tool_calls=[])
+            logger.error(f"Invalid model response - {model_response}")
+            model_message = Message(content="", tool_calls=[])
 
         chat_completion_response = ChatCompletionResponse(
-            choices=[Choice(message=model_response)], model=self.model_name
+            choices=[Choice(message=model_message)],
+            model=self.model_name,
+            metadata={"x-arch-fc-model-response": response_dict["raw_response"]},
+            role="assistant",
         )
 
-        logger.info(f"[response]: {json.dumps(chat_completion_response.model_dump())}")
+        logger.info(
+            f"[response arch-fc]: {json.dumps(chat_completion_response.model_dump(exclude_none=True))}"
+        )
 
         return chat_completion_response
+
+
+# ==============================================================================================================================================
+
+
+class ArchAgentConfig(ArchFunctionConfig):
+    GENERATION_PARAMS = {
+        "temperature": 0.01,
+        "top_p": 1.0,
+        "top_k": 10,
+        "max_tokens": 1024,
+        "stop_token_ids": [151645],
+        "logprobs": True,
+        "top_logprobs": 10,
+    }
+
+
+class ArchAgentHandler(ArchFunctionHandler):
+    def __init__(self, client: OpenAI, model_name: str, config: ArchAgentConfig):
+        super().__init__(client, model_name, config)
+
+    @override
+    def _convert_tools(self, tools: List[Dict[str, Any]]) -> str:
+        """
+        Converts a list of tools into JSON format.
+
+        Args:
+            tools (List[Dict[str, Any]]): A list of tools represented as dictionaries.
+
+        Returns:
+            str: A string representation of converted tools.
+        """
+
+        converted = []
+        # delete parameters key if its empty in tool
+        for tool in tools:
+            if (
+                "parameters" in tool["function"]
+                and "properties" in tool["function"]["parameters"]
+                and not tool["function"]["parameters"]["properties"]
+            ):
+                tool_copy = copy.deepcopy(tool)
+                del tool_copy["function"]["parameters"]
+                converted.append(json.dumps(tool_copy["function"], ensure_ascii=False))
+            else:
+                converted.append(json.dumps(tool["function"], ensure_ascii=False))
+        return "\n".join(converted)
diff --git a/model_server/src/core/utils/hallucination_utils.py b/model_server/src/core/utils/hallucination_utils.py
index 50006224..992f8aa4 100644
--- a/model_server/src/core/utils/hallucination_utils.py
+++ b/model_server/src/core/utils/hallucination_utils.py
@@ -13,16 +13,15 @@ from src.commons.utils import get_model_server_logger
 logger = get_model_server_logger()
 
 # constants
-FUNC_NAME_START_PATTERN = ('<tool_call>\n{"name":"', "<tool_call>\n{'name':'")
+FUNC_NAME_START_PATTERN = ('{"name":"', "{'name':'")
 FUNC_NAME_END_TOKEN = ('",', "',")
-TOOL_CALL_TOKEN = "<tool_call>"
-END_TOOL_CALL_TOKEN = "</tool_call>"
+END_TOOL_CALL_TOKEN = "}}"
 
 FIRST_PARAM_NAME_START_PATTERN = ('"arguments":{"', "'arguments':{'")
-PARAMETER_NAME_END_TOKENS = ('":', ':"', "':", ":'")
-PARAMETER_NAME_START_PATTERN = (',"', ",'")
+PARAMETER_NAME_END_TOKENS = ('":', ':"', "':", ":'", '":"', "':'")
+PARAMETER_NAME_START_PATTERN = ('","', "','")
 PARAMETER_VALUE_START_PATTERN = ('":', "':")
-PARAMETER_VALUE_END_TOKEN = ('",', "}}\n", "',")
+PARAMETER_VALUE_END_TOKEN = ('",', '"}')
 
 BRACKETS = {"(": ")", "{": "}", "[": "]"}
 
@@ -37,16 +36,9 @@ class MaskToken(Enum):
 
 
 HALLUCINATION_THRESHOLD_DICT = {
-    MaskToken.TOOL_CALL.value: {
-        "entropy": 0.35,
-        "varentropy": 1.7,
-        "probability": 0.8,
-    },
-    MaskToken.PARAMETER_VALUE.value: {
-        "entropy": 0.28,
-        "varentropy": 1.2,
-        "probability": 0.8,
-    },
+    "entropy": 0.0001,
+    "varentropy": 0.0001,
+    "probability": 0.8,
 }
 
 
@@ -60,7 +52,7 @@ def check_threshold(entropy: float, varentropy: float, thd: Dict) -> bool:
         thd (dict): A dictionary containing the threshold values with keys 'entropy' and 'varentropy'.
 
     Returns:
-        bool: True if either the entropy or varentropy exceeds their respective thresholds, False otherwise.
+        bool: True if both the entropy and varentropy exceeds their respective thresholds, False otherwise.
     """
     return entropy > thd["entropy"] and varentropy > thd["varentropy"]
 
@@ -82,7 +74,7 @@ def calculate_uncertainty(log_probs: List[float]) -> Tuple[float, float]:
     token_probs = torch.exp(log_probs)
     entropy = -torch.sum(log_probs * token_probs, dim=-1) / math.log(2, math.e)
     varentropy = torch.sum(
-        token_probs * (log_probs / math.log(2, math.e)) + entropy.unsqueeze(-1) ** 2,
+        token_probs * (log_probs / math.log(2, math.e) + entropy.unsqueeze(-1)) ** 2,
         dim=-1,
     )
     return entropy.item(), varentropy.item(), token_probs[0].item()
@@ -160,6 +152,7 @@ class HallucinationState:
         self._process_function(function)
         self.open_bracket = False
         self.bracket = None
+        self.function_name = ""
         self.check_parameter_name = {}
         self.HALLUCINATION_THRESHOLD_DICT = HALLUCINATION_THRESHOLD_DICT
 
@@ -208,22 +201,20 @@ class HallucinationState:
                 r = next(self.response_iterator)
                 if hasattr(r.choices[0].delta, "content"):
                     token_content = r.choices[0].delta.content
-                    if token_content:
+                    if token_content != "":
                         try:
                             logprobs = [
                                 p.logprob
                                 for p in r.choices[0].logprobs.content[0].top_logprobs
                             ]
-                        except Exception as e:
-                            raise ValueError(
-                                f"Error extracting logprobs from response: {e}"
-                            )
-                        if token_content == END_TOOL_CALL_TOKEN:
-                            self._reset_parameters()
-                        else:
                             self.append_and_check_token_hallucination(
                                 token_content, logprobs
                             )
+                        except Exception as e:
+                            self.append_and_check_token_hallucination(
+                                token_content, [None]
+                            )
+
                         return token_content
             except StopIteration:
                 raise StopIteration
@@ -234,12 +225,12 @@ class HallucinationState:
         Detects hallucinations based on the token type and log probabilities.
         """
         content = "".join(self.tokens).replace(" ", "")
-        if self.tokens[-1] == TOOL_CALL_TOKEN:
-            self.mask.append(MaskToken.TOOL_CALL)
-            self._check_logprob()
 
         # Function name extraction logic
         # If the state is function name and the token is not an end token, add to the mask
+        if content.endswith(END_TOOL_CALL_TOKEN):
+            self._reset_parameters()
+
         if self.state == "function_name":
             if self.tokens[-1] not in FUNC_NAME_END_TOKEN:
                 self.mask.append(MaskToken.FUNCTION_NAME)
@@ -303,22 +294,30 @@ class HallucinationState:
                 self.mask.append(MaskToken.PARAMETER_VALUE)
 
                 # checking if the parameter doesn't have enum and the token is the first parameter value token
-                if (
-                    len(self.mask) > 1
-                    and self.mask[-2] != MaskToken.PARAMETER_VALUE
-                    and is_parameter_required(
-                        self.function_properties[self.function_name],
-                        self.parameter_name[-1],
+                # check if function name is in function properties
+                if self.function_name in self.function_properties:
+                    if (
+                        len(self.mask) > 1
+                        and self.mask[-2] != MaskToken.PARAMETER_VALUE
+                        and is_parameter_required(
+                            self.function_properties[self.function_name],
+                            self.parameter_name[-1],
+                        )
+                        and not is_parameter_property(
+                            self.function_properties[self.function_name],
+                            self.parameter_name[-1],
+                            "enum",
+                        )
+                    ):
+                        if self.parameter_name[-1] not in self.check_parameter_name:
+                            self._check_logprob()
+                            self.check_parameter_name[self.parameter_name[-1]] = True
+                else:
+                    self._check_logprob()
+                    self.error_message = f"Function name {self.function_name} not found in function properties"
+                    logger.warning(
+                        f"Function name {self.function_name} not found in function properties"
                     )
-                    and not is_parameter_property(
-                        self.function_properties[self.function_name],
-                        self.parameter_name[-1],
-                        "enum",
-                    )
-                ):
-                    if self.parameter_name[-1] not in self.check_parameter_name:
-                        self._check_logprob()
-                        self.check_parameter_name[self.parameter_name[-1]] = True
             else:
                 self.mask.append(MaskToken.NOT_USED)
         # if the state is parameter value and the token is an end token, change the state
@@ -351,7 +350,7 @@ class HallucinationState:
         if check_threshold(
             entropy,
             varentropy,
-            self.HALLUCINATION_THRESHOLD_DICT[self.mask[-1].value],
+            self.HALLUCINATION_THRESHOLD_DICT,
         ):
             self.hallucination = True
             self.error_message = f"token '{self.tokens[-1]}' is uncertain. Generated response:\n{''.join(self.tokens)}"
diff --git a/model_server/src/core/utils/model_utils.py b/model_server/src/core/utils/model_utils.py
index 7dc71acf..9dfac528 100644
--- a/model_server/src/core/utils/model_utils.py
+++ b/model_server/src/core/utils/model_utils.py
@@ -1,4 +1,5 @@
 import json
+import src.commons.utils as utils
 
 from openai import OpenAI
 from pydantic import BaseModel
@@ -56,7 +57,6 @@ class ArchBaseHandler:
         client: OpenAI,
         model_name: str,
         task_prompt: str,
-        tool_prompt_template: str,
         format_prompt: str,
         generation_params: Dict,
     ):
@@ -67,7 +67,6 @@ class ArchBaseHandler:
             client (OpenAI): An OpenAI client instance.
             model_name (str): Name of the model to use.
             task_prompt (str): The main task prompt for the system.
-            tool_prompt (str): A prompt to describe tools.
             format_prompt (str): A prompt specifying the desired output format.
             generation_params (Dict): Generation parameters for the model.
         """
@@ -75,7 +74,6 @@ class ArchBaseHandler:
         self.model_name = model_name
 
         self.task_prompt = task_prompt
-        self.tool_prompt_template = tool_prompt_template
         self.format_prompt = format_prompt
 
         self.generation_params = generation_params
@@ -105,13 +103,11 @@ class ArchBaseHandler:
             str: A formatted system prompt.
         """
 
-        tool_text = self._convert_tools(tools)
+        today_date = utils.get_today_date()
+        tools = self._convert_tools(tools)
 
         system_prompt = (
-            self.task_prompt
-            + "\n\n"
-            + self.tool_prompt_template.format(tool_text=tool_text)
-            + "\n\n"
+            self.task_prompt.format(today_date=today_date, tools=tools)
             + self.format_prompt
         )
 
@@ -146,7 +142,7 @@ class ArchBaseHandler:
                 {"role": "system", "content": self._format_system_prompt(tools)}
             )
 
-        for message in messages:
+        for idx, message in enumerate(messages):
             role, content, tool_calls = (
                 message.role,
                 message.content,
@@ -162,16 +158,31 @@ class ArchBaseHandler:
                 if metadata.get("optimize_context_window", "false").lower() == "true":
                     content = f"<tool_response>\n\n</tool_response>"
                 else:
-                    content = (
-                        f"<tool_response>\n{json.dumps(content)}\n</tool_response>"
+                    # sample response below
+                    # "content": "<tool_response>\n{'name': 'get_stock_price', 'result': '$196.66'}\n</tool_response>"
+                    # msg[idx-1] contains tool call = '{"tool_calls": [{"name": "currency_exchange", "arguments": {"currency_symbol": "NZD"}}]}'
+                    tool_call_msg = messages[idx - 1].content
+                    if tool_call_msg.startswith("```") and tool_call_msg.endswith(
+                        "```"
+                    ):
+                        tool_call_msg = tool_call_msg.strip("```").strip()
+                        if tool_call_msg.startswith("json"):
+                            tool_call_msg = tool_call_msg[4:].strip()
+                    func_name = json.loads(tool_call_msg)["tool_calls"][0].get(
+                        "name", "no_name"
                     )
+                    tool_response = {
+                        "name": func_name,
+                        "result": content,
+                    }
+                    content = f"<tool_response>\n{json.dumps(tool_response)}\n</tool_response>"
 
             processed_messages.append({"role": role, "content": content})
 
         assert processed_messages[-1]["role"] == "user"
 
         if extra_instruction:
-            processed_messages[-1]["content"] += extra_instruction
+            processed_messages[-1]["content"] += "\n" + extra_instruction
 
         # keep the first system message and shift conversation if the total token length exceeds the limit
         def truncate_messages(messages: List[Dict[str, Any]]):
diff --git a/model_server/src/main.py b/model_server/src/main.py
index 74f60011..34856498 100644
--- a/model_server/src/main.py
+++ b/model_server/src/main.py
@@ -4,7 +4,8 @@ import time
 import logging
 import src.commons.utils as utils
 
-from src.commons.globals import handler_map
+from src.commons.globals import ARCH_ENDPOINT, handler_map
+from src.core.function_calling import ArchFunctionHandler
 from src.core.utils.model_utils import (
     ChatMessage,
     ChatCompletionResponse,
@@ -51,6 +52,8 @@ logging.getLogger("opentelemetry.exporter.otlp.proto.grpc.exporter").setLevel(
 app = FastAPI()
 FastAPIInstrumentor().instrument_app(app)
 
+logger.info(f"using archfc endpoint: {ARCH_ENDPOINT}")
+
 
 @app.get("/healthz")
 async def healthz():
@@ -68,48 +71,59 @@ async def models():
 @app.post("/function_calling")
 async def function_calling(req: ChatMessage, res: Response):
     logger.info("[Endpoint: /function_calling]")
-    logger.info(f"[request body]: {json.dumps(req.model_dump())}")
+    logger.info(f"[request body]: {json.dumps(req.model_dump(exclude_none=True))}")
 
     final_response: ChatCompletionResponse = None
     error_messages = None
 
+    use_agent_orchestrator = req.metadata.get("use_agent_orchestrator", False)
+    logger.info(f"Use agent orchestrator: {use_agent_orchestrator}")
+
     try:
-        intent_start_time = time.perf_counter()
-        intent_response = await handler_map["Arch-Intent"].chat_completion(req)
-        intent_latency = time.perf_counter() - intent_start_time
+        handler_name = "Arch-Agent" if use_agent_orchestrator else "Arch-Function"
+        model_handler: ArchFunctionHandler = handler_map[handler_name]
 
-        if handler_map["Arch-Intent"].detect_intent(intent_response):
-            # TODO: measure agreement between intent detection and function calling
-            try:
-                function_start_time = time.perf_counter()
-                final_response = await handler_map["Arch-Function"].chat_completion(req)
-                function_latency = time.perf_counter() - function_start_time
+        start_time = time.perf_counter()
+        final_response = await model_handler.chat_completion(req)
+        latency = time.perf_counter() - start_time
 
-                final_response.metadata = {
-                    "intent_latency": str(round(intent_latency * 1000, 3)),
-                    "function_latency": str(round(function_latency * 1000, 3)),
-                    "hallucination": str(
-                        handler_map["Arch-Function"].hallucination_state.hallucination
-                    ),
-                }
-            except ValueError as e:
-                res.statuscode = 503
-                error_messages = f"[Arch-Function] - Error in tool call extraction: {e}"
-            except StopIteration as e:
-                res.statuscode = 500
-                error_messages = f"[Arch-Function] - Error in hallucination check: {e}"
-            except Exception as e:
-                res.status_code = 500
-                error_messages = f"[Arch-Function] - Error in ChatCompletion: {e}"
+        if not final_response.metadata:
+            final_response.metadata = {}
+
+        # Parameter gathering for detected intents
+        if final_response.choices[0].message.content:
+            final_response.metadata["function_latency"] = str(round(latency * 1000, 3))
+        # Function Calling
+        elif final_response.choices[0].message.tool_calls:
+            final_response.metadata["function_latency"] = str(round(latency * 1000, 3))
+
+            if not use_agent_orchestrator:
+                final_response.metadata["hallucination"] = str(
+                    model_handler.hallucination_state.hallucination
+                )
+        # No intent detected
         else:
-            intent_response.metadata = {
-                "intent_latency": str(round(intent_latency * 1000, 3)),
-            }
-            final_response = intent_response
+            final_response.metadata["intent_latency"] = str(round(latency * 1000, 3))
 
+        if not use_agent_orchestrator:
+            final_response.metadata["intent_latency"] = str(round(latency * 1000, 3))
+
+            final_response.metadata["hallucination"] = str(
+                model_handler.hallucination_state.hallucination
+            )
+
+    except ValueError as e:
+        res.statuscode = 503
+        error_messages = f"[{handler_name}] - Error in tool call extraction: {e}"
+        raise
+    except StopIteration as e:
+        res.statuscode = 500
+        error_messages = f"[{handler_name}] - Error in hallucination check: {e}"
+        raise
     except Exception as e:
         res.status_code = 500
-        error_messages = f"[Arch-Intent] - Error in ChatCompletion: {e}"
+        error_messages = f"[{handler_name}] - Error in ChatCompletion: {e}"
+        raise
 
     if error_messages is not None:
         logger.error(error_messages)
@@ -121,7 +135,7 @@ async def function_calling(req: ChatMessage, res: Response):
 @app.post("/guardrails")
 async def guardrails(req: GuardRequest, res: Response, max_num_words=300):
     logger.info("[Endpoint: /guardrails] - Gateway")
-    logger.info(f"[request body]: {json.dumps(req.model_dump())}")
+    logger.info(f"[request body]: {json.dumps(req.model_dump(exclude_none=True))}")
 
     final_response: GuardResponse = None
     error_messages = None
diff --git a/model_server/tests/core/test_function_calling.py b/model_server/tests/core/test_function_calling.py
index 3f7971d8..6005d5e6 100644
--- a/model_server/tests/core/test_function_calling.py
+++ b/model_server/tests/core/test_function_calling.py
@@ -1,5 +1,5 @@
 import pytest
-
+import time
 from src.commons.globals import handler_map
 from src.core.utils.model_utils import ChatMessage, Message
 
@@ -37,40 +37,9 @@ get_weather_api = {
 # get_data class return request, intent, hallucination, parameter_gathering
 
 
-def get_hallucination_data_complex():
+def get_hallucination_data():
     # Create instances of the Message class
-    message1 = Message(role="user", content="How is the weather in Seattle?")
-    message2 = Message(
-        role="assistant", content="Can you specify the unit you want the weather in?"
-    )
-    message3 = Message(role="user", content="In celcius please!")
-
-    # Create a list of tools
-    tools = [get_weather_api]
-
-    # Create an instance of the ChatMessage class
-    req = ChatMessage(messages=[message1, message2, message3], tools=tools)
-
-    return req, True, True, True
-
-
-def get_hallucination_data_easy():
-    # Create instances of the Message class
-    message1 = Message(role="user", content="How is the weather in Seattle?")
-
-    # Create a list of tools
-    tools = [get_weather_api]
-
-    # Create an instance of the ChatMessage class
-    req = ChatMessage(messages=[message1], tools=tools)
-
-    # model will hallucinate
-    return req, True, True, True
-
-
-def get_hallucination_data_medium():
-    # Create instances of the Message class
-    message1 = Message(role="user", content="How is the weather in?")
+    message1 = Message(role="user", content="How is the weather in Seattle in days?")
 
     # Create a list of tools
     tools = [get_weather_api]
@@ -79,26 +48,10 @@ def get_hallucination_data_medium():
     req = ChatMessage(messages=[message1], tools=tools)
 
     # first token will not be tool call
-    return req, True, True, True
+    return req, False, True
 
 
-def get_complete_data_2():
-    # Create instances of the Message class
-    message1 = Message(
-        role="user",
-        content="what is the weather forecast for seattle in the next 10 days?",
-    )
-
-    # Create a list of tools
-    tools = [get_weather_api]
-
-    # Create an instance of the ChatMessage class
-    req = ChatMessage(messages=[message1], tools=tools)
-
-    return req, True, False, False
-
-
-def get_complete_data():
+def get_success_tool_call_data():
     # Create instances of the Message class
     message1 = Message(role="user", content="How is the weather in Seattle in 7 days?")
 
@@ -108,7 +61,7 @@ def get_complete_data():
     # Create an instance of the ChatMessage class
     req = ChatMessage(messages=[message1], tools=tools)
 
-    return req, True, False, False
+    return req, True, False
 
 
 def get_irrelevant_data():
@@ -121,7 +74,7 @@ def get_irrelevant_data():
     # Create an instance of the ChatMessage class
     req = ChatMessage(messages=[message1], tools=tools)
 
-    return req, False, False, False
+    return req, False, False
 
 
 def get_greeting_data():
@@ -134,39 +87,29 @@ def get_greeting_data():
     # Create an instance of the ChatMessage class
     req = ChatMessage(messages=[message1], tools=tools)
 
-    return req, False, False, False
+    return req, False, False
 
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "get_data_func",
     [
-        get_hallucination_data_complex,
-        get_hallucination_data_easy,
-        get_complete_data,
+        get_hallucination_data,
+        get_greeting_data,
         get_irrelevant_data,
-        get_complete_data_2,
+        get_success_tool_call_data,
     ],
 )
 async def test_function_calling(get_data_func):
-    req, intent, hallucination, parameter_gathering = get_data_func()
+    req, intent, hallucination = get_data_func()
+    handler_name = "Arch-Function"
+    use_agent_orchestrator = False
+    model_handler: ArchFunctionHandler = handler_map[handler_name]
 
-    intent_response = await handler_map["Arch-Intent"].chat_completion(req)
+    start_time = time.perf_counter()
+    final_response = await model_handler.chat_completion(req)
+    latency = time.perf_counter() - start_time
 
-    assert handler_map["Arch-Intent"].detect_intent(intent_response) == intent
+    assert intent == (len(final_response.choices[0].message.tool_calls) >= 1)
 
-    if intent:
-        function_calling_response = await handler_map["Arch-Function"].chat_completion(
-            req
-        )
-        assert (
-            handler_map["Arch-Function"].hallucination_state.hallucination
-            == hallucination
-        )
-        response_txt = function_calling_response.choices[0].message.content
-
-        if parameter_gathering:
-            prefill_prefix = handler_map["Arch-Function"].prefill_prefix
-            assert any(
-                response_txt.startswith(prefix) for prefix in prefill_prefix
-            ), f"Response '{response_txt}' does not start with any of the prefixes: {prefill_prefix}"
+    assert hallucination == model_handler.hallucination_state.hallucination
diff --git a/model_server/tests/core/test_state.py b/model_server/tests/core/test_state.py
index b5a0332a..331b8d3f 100644
--- a/model_server/tests/core/test_state.py
+++ b/model_server/tests/core/test_state.py
@@ -1,5 +1,5 @@
 from src.commons.globals import handler_map
-from src.core.function_calling import Message
+from src.core.function_calling import ArchFunctionHandler, Message
 
 
 test_input_history = [
@@ -7,34 +7,19 @@ test_input_history = [
     {
         "role": "assistant",
         "model": "Arch-Function",
-        "tool_calls": [
-            {
-                "id": "call_3394",
-                "type": "function",
-                "function": {
-                    "name": "weather_forecast",
-                    "arguments": {"city": "Chicago", "days": 5},
-                },
-            }
-        ],
+        "content": '```json\n{"tool_calls": [{"name": "get_current_weather", "arguments": {"days": 5, "location": "Chicago, Illinois"}}]}\n```',
+    },
+    {
+        "role": "tool",
+        "model": "Arch-Function",
+        "content": '{"location":"Chicago%2C%20Illinois","temperature":[{"date":"2025-04-14","temperature":{"min":53,"max":65},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432817+00:00"},{"date":"2025-04-15","temperature":{"min":85,"max":97},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432830+00:00"},{"date":"2025-04-16","temperature":{"min":62,"max":78},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432835+00:00"},{"date":"2025-04-17","temperature":{"min":89,"max":101},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432839+00:00"},{"date":"2025-04-18","temperature":{"min":86,"max":104},"units":"Farenheit","query_time":"2025-04-14 17:01:52.432843+00:00"}],"units":"Farenheit"}',
     },
-    {"role": "tool", "content": "--", "tool_call_id": "call_3394"},
-    {"role": "assistant", "content": "--", "model": "gpt-3.5-turbo-0125"},
-    {"role": "user", "content": "how is the weather in chicago for next 5 days?"},
     {
         "role": "assistant",
-        "tool_calls": [
-            {
-                "id": "call_5306",
-                "type": "function",
-                "function": {
-                    "name": "weather_forecast",
-                    "arguments": {"city": "Chicago", "days": 5},
-                },
-            }
-        ],
+        "model": "gpt-4o-2024-08-06",
+        "content": '{"response": "Based on the forecast data you provided, here is the weather for the next 5 days in Chicago:\\n\\n- **April 14, 2025**: The temperature will range between 53\\u00b0F and 65\\u00b0F. \\n- **April 15, 2025**: The temperature will range between 85\\u00b0F and 97\\u00b0F.\\n- **April 16, 2025**: The temperature will range between 62\\u00b0F and 78\\u00b0F.\\n- **April 17, 2025**: The temperature will range between 89\\u00b0F and 101\\u00b0F.\\n- **April 18, 2025**: The temperature will range between 86\\u00b0F and 104\\u00b0F.\\n\\nPlease note that the temperatures are given in Fahrenheit."}',
     },
-    {"role": "tool", "content": "--", "tool_call_id": "call_5306"},
+    {"role": "user", "content": "what about seattle?"},
 ]
 
 
@@ -44,7 +29,8 @@ def test_update_fc_history():
     for h in test_input_history:
         message_history.append(Message(**h))
 
-    updated_history = handler_map["Arch-Function"]._process_messages(message_history)
-    assert len(updated_history) == 7
+    handler: ArchFunctionHandler = handler_map["Arch-Function"]
+    updated_history = handler._process_messages(message_history)
+    assert len(updated_history) == 5
     # ensure that tool role does not exist anymore
     assert all([h["role"] != "tool" for h in updated_history])
diff --git a/tests/archgw/arch_config.yaml b/tests/archgw/arch_config.yaml
index 2c3d85d5..d1990330 100644
--- a/tests/archgw/arch_config.yaml
+++ b/tests/archgw/arch_config.yaml
@@ -1,10 +1,11 @@
 version: "0.1-beta"
 
-listener:
-  address: 0.0.0.0
-  port: 10000
-  message_format: huggingface
-  connect_timeout: 0.005s
+listeners:
+  ingress_traffic:
+    address: 0.0.0.0
+    port: 10000
+    message_format: openai
+    timeout: 30s
 
 endpoints:
   weather_forecast_service:
diff --git a/tests/archgw/common.py b/tests/archgw/common.py
index 404d8ef9..f87f6403 100644
--- a/tests/archgw/common.py
+++ b/tests/archgw/common.py
@@ -47,14 +47,11 @@ TEST_CASE_FIXTURES = {
                         "tool_call_id": "",
                         "tool_calls": [
                             {
-                                "id": "call_6009",
+                                "id": "call_2925",
                                 "type": "function",
                                 "function": {
                                     "name": "get_current_weather",
-                                    "arguments": {
-                                        "location": "Seattle, WA",
-                                        "days": "2",
-                                    },
+                                    "arguments": {"location": "Seattle", "days": "2"},
                                 },
                             }
                         ],
@@ -63,7 +60,11 @@ TEST_CASE_FIXTURES = {
                 }
             ],
             "model": "Arch-Function",
-            "metadata": {"intent_latency": "455.092", "function_latency": "312.744"},
+            "metadata": {
+                "x-arch-fc-model-response": '{"tool_calls": [{"name": "get_current_weather", "arguments": {"location": "Seattle", "days": "2"}}]}',
+                "function_latency": "361.841",
+                "intent_latency": "361.841",
+            },
         },
         "api_server_response": [
             {
diff --git a/tests/e2e/poetry.lock b/tests/e2e/poetry.lock
index de08f7ee..7e296f88 100644
--- a/tests/e2e/poetry.lock
+++ b/tests/e2e/poetry.lock
@@ -1,14 +1,14 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
 
 [[package]]
 name = "attrs"
-version = "24.3.0"
+version = "25.1.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
-    {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
+    {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"},
+    {file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"},
 ]
 
 [package.extras]
@@ -21,13 +21,13 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "certifi"
-version = "2024.12.14"
+version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
-    {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
+    {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
+    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
 ]
 
 [[package]]
@@ -111,116 +111,103 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.7"
 files = [
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-    {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0de7b687289d3c1b3e8660d0741874abe7888100efe14bd0f9fd7141bcbda92b"},
-    {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5ed2e36c3e9b4f21dd9422f6893dec0abf2cca553af509b10cd630f878d3eb99"},
-    {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:40d3ff7fc90b98c637bda91c89d51264a3dcf210cade3a2c6f838c7268d7a4ca"},
-    {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1110e22af8ca26b90bd6364fe4c763329b0ebf1ee213ba32b68c73de5752323d"},
-    {file = "charset_normalizer-3.4.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:86f4e8cca779080f66ff4f191a685ced73d2f72d50216f7112185dc02b90b9b7"},
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-    {file = "deepdiff-8.1.1-py3-none-any.whl", hash = "sha256:b0231fa3afb0f7184e82535f2b4a36636442ed21e94a0cf3aaa7982157e7ebca"},
-    {file = "deepdiff-8.1.1.tar.gz", hash = "sha256:dd7bc7d5c8b51b5b90f01b0e2fe23c801fd8b4c6a7ee7e31c5a3c3663fcc7ceb"},
+    {file = "deepdiff-8.2.0-py3-none-any.whl", hash = "sha256:5091f2cdfd372b1b9f6bfd8065ba323ae31118dc4e42594371b38c8bea3fd0a4"},
+    {file = "deepdiff-8.2.0.tar.gz", hash = "sha256:6ec78f65031485735545ffbe7a61e716c3c2d12ca6416886d5e9291fc76c46c3"},
 ]
 
 [package.dependencies]
-orderly-set = ">=5.2.3,<6"
+orderly-set = ">=5.3.0,<6"
 
 [package.extras]
-cli = ["click (==8.1.7)", "pyyaml (==6.0.2)"]
+cli = ["click (==8.1.8)", "pyyaml (==6.0.2)"]
 optimize = ["orjson"]
 
+[[package]]
+name = "exceptiongroup"
+version = "1.2.2"
+description = "Backport of PEP 654 (exception groups)"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
+    {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
+]
+
+[package.extras]
+test = ["pytest (>=6)"]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -364,13 +369,13 @@ files = [
 
 [[package]]
 name = "orderly-set"
-version = "5.2.3"
+version = "5.3.0"
 description = "Orderly set"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "orderly_set-5.2.3-py3-none-any.whl", hash = "sha256:d357cedcf67f4ebff0d4cbd5b0997e98eeb65dd24fdf5c990a501ae9e82c7d34"},
-    {file = "orderly_set-5.2.3.tar.gz", hash = "sha256:571ed97c5a5fca7ddeb6b2d26c19aca896b0ed91f334d9c109edd2f265fb3017"},
+    {file = "orderly_set-5.3.0-py3-none-any.whl", hash = "sha256:c2c0bfe604f5d3d9b24e8262a06feb612594f37aa3845650548befd7772945d1"},
+    {file = "orderly_set-5.3.0.tar.gz", hash = "sha256:80b3d8fdd3d39004d9aad389eaa0eab02c71f0a0511ba3a6d54a935a6c6a0acc"},
 ]
 
 [[package]]
@@ -438,20 +443,22 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "8.3.4"
+version = "8.3.5"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.3.4-py3-none-any.whl", hash = "sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6"},
-    {file = "pytest-8.3.4.tar.gz", hash = "sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761"},
+    {file = "pytest-8.3.5-py3-none-any.whl", hash = "sha256:c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820"},
+    {file = "pytest-8.3.5.tar.gz", hash = "sha256:f4efe70cc14e511565ac476b57c279e12a855b11f48f212af1080ef2263d3845"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
+exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
 pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
@@ -476,13 +483,13 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
 name = "pytest-retry"
-version = "1.6.3"
+version = "1.7.0"
 description = "Adds the ability to retry flaky tests in CI environments"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "pytest_retry-1.6.3-py3-none-any.whl", hash = "sha256:e96f7df77ee70b0838d1085f9c3b8b5b7d74bf8947a0baf32e2b8c71b27683c8"},
-    {file = "pytest_retry-1.6.3.tar.gz", hash = "sha256:36ccfa11c8c8f9ddad5e20375182146d040c20c4a791745139c5a99ddf1b557d"},
+    {file = "pytest_retry-1.7.0-py3-none-any.whl", hash = "sha256:a2dac85b79a4e2375943f1429479c65beb6c69553e7dae6b8332be47a60954f4"},
+    {file = "pytest_retry-1.7.0.tar.gz", hash = "sha256:f8d52339f01e949df47c11ba9ee8d5b362f5824dff580d3870ec9ae0057df80f"},
 ]
 
 [package.dependencies]
@@ -533,13 +540,13 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "selenium"
-version = "4.27.1"
+version = "4.29.0"
 description = "Official Python bindings for Selenium WebDriver"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "selenium-4.27.1-py3-none-any.whl", hash = "sha256:b89b1f62b5cfe8025868556fe82360d6b649d464f75d2655cb966c8f8447ea18"},
-    {file = "selenium-4.27.1.tar.gz", hash = "sha256:5296c425a75ff1b44d0d5199042b36a6d1ef76c04fb775b97b40be739a9caae2"},
+    {file = "selenium-4.29.0-py3-none-any.whl", hash = "sha256:ce5d26f1ddc1111641113653af33694c13947dd36c2df09cdd33f554351d372e"},
+    {file = "selenium-4.29.0.tar.gz", hash = "sha256:3a62f7ec33e669364a6c0562a701deb69745b569c50d55f1a912bf8eb33358ba"},
 ]
 
 [package.dependencies]
@@ -587,19 +594,61 @@ files = [
 tests = ["pytest", "pytest-cov"]
 
 [[package]]
-name = "trio"
-version = "0.27.0"
-description = "A friendly Python library for async concurrency and I/O"
+name = "tomli"
+version = "2.2.1"
+description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "trio-0.27.0-py3-none-any.whl", hash = "sha256:68eabbcf8f457d925df62da780eff15ff5dc68fd6b367e2dde59f7aaf2a0b884"},
-    {file = "trio-0.27.0.tar.gz", hash = "sha256:1dcc95ab1726b2da054afea8fd761af74bad79bd52381b84eae408e983c76831"},
+    {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
+    {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
+    {file = "tomli-2.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ece47d672db52ac607a3d9599a9d48dcb2f2f735c6c2d1f34130085bb12b112a"},
+    {file = "tomli-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6972ca9c9cc9f0acaa56a8ca1ff51e7af152a9f87fb64623e31d5c83700080ee"},
+    {file = "tomli-2.2.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c954d2250168d28797dd4e3ac5cf812a406cd5a92674ee4c8f123c889786aa8e"},
+    {file = "tomli-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8dd28b3e155b80f4d54beb40a441d366adcfe740969820caf156c019fb5c7ec4"},
+    {file = "tomli-2.2.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:e59e304978767a54663af13c07b3d1af22ddee3bb2fb0618ca1593e4f593a106"},
+    {file = "tomli-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:33580bccab0338d00994d7f16f4c4ec25b776af3ffaac1ed74e0b3fc95e885a8"},
+    {file = "tomli-2.2.1-cp311-cp311-win32.whl", hash = "sha256:465af0e0875402f1d226519c9904f37254b3045fc5084697cefb9bdde1ff99ff"},
+    {file = "tomli-2.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:2d0f2fdd22b02c6d81637a3c95f8cd77f995846af7414c5c4b8d0545afa1bc4b"},
+    {file = "tomli-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4a8f6e44de52d5e6c657c9fe83b562f5f4256d8ebbfe4ff922c495620a7f6cea"},
+    {file = "tomli-2.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8d57ca8095a641b8237d5b079147646153d22552f1c637fd3ba7f4b0b29167a8"},
+    {file = "tomli-2.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4e340144ad7ae1533cb897d406382b4b6fede8890a03738ff1683af800d54192"},
+    {file = "tomli-2.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:db2b95f9de79181805df90bedc5a5ab4c165e6ec3fe99f970d0e302f384ad222"},
+    {file = "tomli-2.2.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40741994320b232529c802f8bc86da4e1aa9f413db394617b9a256ae0f9a7f77"},
+    {file = "tomli-2.2.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:400e720fe168c0f8521520190686ef8ef033fb19fc493da09779e592861b78c6"},
+    {file = "tomli-2.2.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:02abe224de6ae62c19f090f68da4e27b10af2b93213d36cf44e6e1c5abd19fdd"},
+    {file = "tomli-2.2.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b82ebccc8c8a36f2094e969560a1b836758481f3dc360ce9a3277c65f374285e"},
+    {file = "tomli-2.2.1-cp312-cp312-win32.whl", hash = "sha256:889f80ef92701b9dbb224e49ec87c645ce5df3fa2cc548664eb8a25e03127a98"},
+    {file = "tomli-2.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:7fc04e92e1d624a4a63c76474610238576942d6b8950a2d7f908a340494e67e4"},
+    {file = "tomli-2.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f4039b9cbc3048b2416cc57ab3bda989a6fcf9b36cf8937f01a6e731b64f80d7"},
+    {file = "tomli-2.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:286f0ca2ffeeb5b9bd4fcc8d6c330534323ec51b2f52da063b11c502da16f30c"},
+    {file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13"},
+    {file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281"},
+    {file = "tomli-2.2.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272"},
+    {file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140"},
+    {file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2"},
+    {file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744"},
+    {file = "tomli-2.2.1-cp313-cp313-win32.whl", hash = "sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec"},
+    {file = "tomli-2.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69"},
+    {file = "tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc"},
+    {file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"},
+]
+
+[[package]]
+name = "trio"
+version = "0.29.0"
+description = "A friendly Python library for async concurrency and I/O"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "trio-0.29.0-py3-none-any.whl", hash = "sha256:d8c463f1a9cc776ff63e331aba44c125f423a5a13c684307e828d930e625ba66"},
+    {file = "trio-0.29.0.tar.gz", hash = "sha256:ea0d3967159fc130acb6939a0be0e558e364fee26b5deeecc893a6b08c361bdf"},
 ]
 
 [package.dependencies]
 attrs = ">=23.2.0"
 cffi = {version = ">=1.14", markers = "os_name == \"nt\" and implementation_name != \"pypy\""}
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 idna = "*"
 outcome = "*"
 sniffio = ">=1.3.0"
@@ -607,16 +656,18 @@ sortedcontainers = "*"
 
 [[package]]
 name = "trio-websocket"
-version = "0.11.1"
+version = "0.12.2"
 description = "WebSocket library for Trio"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "trio-websocket-0.11.1.tar.gz", hash = "sha256:18c11793647703c158b1f6e62de638acada927344d534e3c7628eedcb746839f"},
-    {file = "trio_websocket-0.11.1-py3-none-any.whl", hash = "sha256:520d046b0d030cf970b8b2b2e00c4c2245b3807853ecd44214acd33d74581638"},
+    {file = "trio_websocket-0.12.2-py3-none-any.whl", hash = "sha256:df605665f1db533f4a386c94525870851096a223adcb97f72a07e8b4beba45b6"},
+    {file = "trio_websocket-0.12.2.tar.gz", hash = "sha256:22c72c436f3d1e264d0910a3951934798dcc5b00ae56fc4ee079d46c7cf20fae"},
 ]
 
 [package.dependencies]
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+outcome = ">=1.2.0"
 trio = ">=0.11"
 wsproto = ">=0.14"
 
@@ -633,13 +684,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
-    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
 ]
 
 [package.dependencies]
@@ -683,5 +734,5 @@ h11 = ">=0.9.0,<1"
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.12"
-content-hash = "f52803b5290905f3958defcb44ab9478469e8a7c14ec3d16594d13427efa4444"
+python-versions = "^3.10"
+content-hash = "a40015b90325879e50f82cca6a26a730d763cad26589671df798832d41c42db3"
diff --git a/tests/e2e/pyproject.toml b/tests/e2e/pyproject.toml
index bf557ef2..f9a0defd 100644
--- a/tests/e2e/pyproject.toml
+++ b/tests/e2e/pyproject.toml
@@ -8,7 +8,7 @@ readme = "README.md"
 package-mode = false
 
 [tool.poetry.dependencies]
-python = "^3.12"
+python = "^3.10"
 pytest = "^8.3.3"
 requests = "^2.29.0"
 selenium = "^4.11.2"
diff --git a/tests/e2e/test_prompt_gateway.py b/tests/e2e/test_prompt_gateway.py
index e4f64d89..e6a10f3a 100644
--- a/tests/e2e/test_prompt_gateway.py
+++ b/tests/e2e/test_prompt_gateway.py
@@ -2,6 +2,7 @@ import json
 import pytest
 import requests
 from deepdiff import DeepDiff
+import re
 
 from common import (
     PROMPT_GATEWAY_ENDPOINT,
@@ -11,6 +12,15 @@ from common import (
 )
 
 
+def cleanup_tool_call(tool_call):
+    pattern = r"```json\n(.*?)\n```"
+    match = re.search(pattern, tool_call, re.DOTALL)
+    if match:
+        tool_call = match.group(1)
+
+    return tool_call.strip()
+
+
 @pytest.mark.parametrize("stream", [True, False])
 def test_prompt_gateway(stream):
     expected_tool_call = {
@@ -42,9 +52,14 @@ def test_prompt_gateway(stream):
         assert "role" in choices[0]["delta"]
         role = choices[0]["delta"]["role"]
         assert role == "assistant"
-        tool_calls = choices[0].get("delta", {}).get("tool_calls", [])
+        print(f"choices: {choices}")
+        tool_call_str = choices[0].get("delta", {}).get("content", "")
+        print("tool_call_str: ", tool_call_str)
+        cleaned_tool_call_str = cleanup_tool_call(tool_call_str)
+        print("cleaned_tool_call_str: ", cleaned_tool_call_str)
+        tool_calls = json.loads(cleaned_tool_call_str).get("tool_calls", [])
         assert len(tool_calls) > 0
-        tool_call = tool_calls[0]["function"]
+        tool_call = tool_calls[0]
         location = tool_call["arguments"]["location"]
         assert expected_tool_call["arguments"]["location"] in location.lower()
         del expected_tool_call["arguments"]["location"]
@@ -62,7 +77,7 @@ def test_prompt_gateway(stream):
 
         # third..end chunk is summarization (role = assistant)
         response_json = json.loads(chunks[2])
-        assert response_json.get("model").startswith("gpt-4o-mini")
+        assert response_json.get("model").startswith("gpt-4o")
         choices = response_json.get("choices", [])
         assert len(choices) > 0
         assert "role" in choices[0]["delta"]
@@ -71,18 +86,24 @@ def test_prompt_gateway(stream):
 
     else:
         response_json = response.json()
-        assert response_json.get("model").startswith("gpt-4o-mini")
+        assert response_json.get("model").startswith("gpt-4o")
         choices = response_json.get("choices", [])
         assert len(choices) > 0
         assert "role" in choices[0]["message"]
         assert choices[0]["message"]["role"] == "assistant"
         # now verify arch_messages (tool call and api response) that are sent as response metadata
         arch_messages = get_arch_messages(response_json)
+        print("arch_messages: ", json.dumps(arch_messages))
         assert len(arch_messages) == 2
         tool_calls_message = arch_messages[0]
-        tool_calls = tool_calls_message.get("tool_calls", [])
-        assert len(tool_calls) > 0
-        tool_call = tool_calls[0]["function"]
+        print("tool_calls_message: ", tool_calls_message)
+        tool_calls = tool_calls_message.get("content", [])
+        cleaned_tool_call_str = cleanup_tool_call(tool_calls)
+        cleaned_tool_call_json = json.loads(cleaned_tool_call_str)
+        print("cleaned_tool_call_json: ", json.dumps(cleaned_tool_call_json))
+        tool_calls_list = cleaned_tool_call_json.get("tool_calls", [])
+        assert len(tool_calls_list) > 0
+        tool_call = tool_calls_list[0]
         location = tool_call["arguments"]["location"]
         assert expected_tool_call["arguments"]["location"] in location.lower()
         del expected_tool_call["arguments"]["location"]
@@ -231,7 +252,7 @@ def test_prompt_gateway_param_tool_call(stream):
 
         # third..end chunk is summarization (role = assistant)
         response_json = json.loads(chunks[2])
-        assert response_json.get("model").startswith("gpt-4o-mini")
+        assert response_json.get("model").startswith("gpt-4o")
         choices = response_json.get("choices", [])
         assert len(choices) > 0
         assert "role" in choices[0]["delta"]
@@ -240,7 +261,7 @@ def test_prompt_gateway_param_tool_call(stream):
 
     else:
         response_json = response.json()
-        assert response_json.get("model").startswith("gpt-4o-mini")
+        assert response_json.get("model").startswith("gpt-4o")
         choices = response_json.get("choices", [])
         assert len(choices) > 0
         assert "role" in choices[0]["message"]
@@ -257,13 +278,12 @@ def test_prompt_gateway_param_tool_call(stream):
 
 
 @pytest.mark.parametrize("stream", [True, False])
-@pytest.mark.skip("no longer needed")
 def test_prompt_gateway_default_target(stream):
     body = {
         "messages": [
             {
                 "role": "user",
-                "content": "hello, what can you do for me?",
+                "content": "hello",
             },
         ],
         "stream": stream,
@@ -274,17 +294,20 @@ def test_prompt_gateway_default_target(stream):
         chunks = get_data_chunks(response, n=3)
         assert len(chunks) > 0
         response_json = json.loads(chunks[0])
+        print("response_json chunks[0]: ", response_json)
         assert response_json.get("model").startswith("api_server")
         assert len(response_json.get("choices", [])) > 0
         assert response_json.get("choices")[0]["delta"]["role"] == "assistant"
 
         response_json = json.loads(chunks[1])
+        print("response_json chunks[1]: ", response_json)
         choices = response_json.get("choices", [])
         assert len(choices) > 0
         content = choices[0]["delta"]["content"]
         assert content == "I can help you with weather forecast"
     else:
         response_json = response.json()
+        print("response_json: ", response_json)
         assert response_json.get("model").startswith("api_server")
         assert len(response_json.get("choices")) > 0
         assert response_json.get("choices")[0]["message"]["role"] == "assistant"
diff --git a/tests/hurl/llm_gateway_model_default_llm.hurl b/tests/hurl/llm_gateway_model_default_llm.hurl
new file mode 100644
index 00000000..0bca20c8
--- /dev/null
+++ b/tests/hurl/llm_gateway_model_default_llm.hurl
@@ -0,0 +1,24 @@
+POST http://localhost:12000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant.\n"
+    },
+    {
+      "role": "user",
+      "content": "I want to sell red shoes"
+    }
+  ],
+  "stream": false
+}
+
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^gpt-4o-mini/
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" != null
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/tests/hurl/llm_gateway_model_explicit_model.hurl b/tests/hurl/llm_gateway_model_explicit_model.hurl
new file mode 100644
index 00000000..8ba7ccd1
--- /dev/null
+++ b/tests/hurl/llm_gateway_model_explicit_model.hurl
@@ -0,0 +1,25 @@
+POST http://localhost:12000/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "gpt-3.5-turbo",
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant.\n"
+    },
+    {
+      "role": "user",
+      "content": "I want to sell red shoes"
+    }
+  ],
+  "stream": false
+}
+
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^gpt-3.5-turbo/
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" != null
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/tests/hurl/llm_gateway_model_hint.hurl b/tests/hurl/llm_gateway_model_hint.hurl
new file mode 100644
index 00000000..9f45c670
--- /dev/null
+++ b/tests/hurl/llm_gateway_model_hint.hurl
@@ -0,0 +1,25 @@
+POST http://localhost:12000/v1/chat/completions
+Content-Type: application/json
+x-arch-llm-provider-hint: gpt-4o
+
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant.\n"
+    },
+    {
+      "role": "user",
+      "content": "I want to sell red shoes"
+    }
+  ],
+  "stream": false
+}
+
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^gpt-4o-2/
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" != null
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/tests/model_tests/arch_fc.hurl b/tests/model_tests/arch_fc.hurl
new file mode 100644
index 00000000..a16cdb92
--- /dev/null
+++ b/tests/model_tests/arch_fc.hurl
@@ -0,0 +1,22 @@
+POST https://archfc.katanemo.dev/v1/chat/completions
+Content-Type: application/json
+
+{
+  "model": "Arch-Intent",
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant.\n\nYou task is to check if there are any tools that can be used to help the last user message in conversations according to the available tools listed below.\n\n<tools>\n{\"index\": \"T0\", \"type\": \"function\", \"function\": {\"name\": \"weather_forecast\", \"parameters\": {\"type\": \"object\", \"properties\": {\"city\": {\"type\": \"str\"}, \"days\": {\"type\": \"int\"}}, \"required\": [\"city\", \"days\"]}}}\n</tools>\n\nProvide your tool assessment for ONLY THE LAST USER MESSAGE in the above conversation:\n- First line must read 'Yes' or 'No'.\n- If yes, a second line must include a comma-separated list of tool indexes.\n"
+    },
+    { "role": "user", "content": "how is the weather in seattle? Are there any tools can help?" }
+  ],
+  "stream": false
+}
+
+HTTP 200
+[Asserts]
+header "content-type" == "application/json"
+jsonpath "$.model" matches /^Arch-Function/
+jsonpath "$.usage" != null
+jsonpath "$.choices[0].message.content" matches /Yes/
+jsonpath "$.choices[0].message.role" == "assistant"
diff --git a/tests/modelserver/test_hallucination.py b/tests/modelserver/test_hallucination.py
index f1a3d9b4..323db3fc 100644
--- a/tests/modelserver/test_hallucination.py
+++ b/tests/modelserver/test_hallucination.py
@@ -4,6 +4,9 @@ import requests
 import logging
 import yaml
 
+pytestmark = pytest.mark.skip(
+    reason="Skipping entire test file as hallucination is not enabled for archfc 1.1 yet"
+)
 
 MODEL_SERVER_ENDPOINT = os.getenv(
     "MODEL_SERVER_ENDPOINT", "http://localhost:51000/function_calling"
diff --git a/tests/modelserver/test_hallucination_data.yaml b/tests/modelserver/test_hallucination_data.yaml
index 8e6e6b26..935a8f5f 100644
--- a/tests/modelserver/test_hallucination_data.yaml
+++ b/tests/modelserver/test_hallucination_data.yaml
@@ -63,7 +63,7 @@ test_cases:
         - role: "assistant"
           content: "Can you please provide me the days for the weather forecast?"
         - role: "user"
-          content: "los angeles in 5 days"
+          content: "5 days"
       tools:
         - type: "function"
           function:
@@ -82,7 +82,7 @@ test_cases:
               required: ["location", "days"]
     expected:
     - type: "metadata"
-      hallucination: true
+      hallucination: false
 
   - id: "[WEATHER AGENT] - multi turn, single tool, clarification"
     input:
diff --git a/tests/modelserver/test_modelserver.py b/tests/modelserver/test_modelserver.py
index 75e6d27e..4596606f 100644
--- a/tests/modelserver/test_modelserver.py
+++ b/tests/modelserver/test_modelserver.py
@@ -5,6 +5,9 @@ import yaml
 
 from deepdiff import DeepDiff
 
+pytestmark = pytest.mark.skip(
+    reason="Skipping entire test file as this these tests are heavily dependent on model output"
+)
 
 MODEL_SERVER_ENDPOINT = os.getenv(
     "MODEL_SERVER_ENDPOINT", "http://localhost:51000/function_calling"
diff --git a/tests/rest/api_model_server.rest b/tests/rest/api_model_server.rest
index ed86fac3..5fdbf968 100644
--- a/tests/rest/api_model_server.rest
+++ b/tests/rest/api_model_server.rest
@@ -1,6 +1,5 @@
 @model_server_endpoint = http://localhost:51000
-@archfc_endpoint = https://api.fc.archgw.com
-
+@archfc_endpoint = https://archfc.katanemo.dev
 
 ### talk to function calling endpoint
 POST {{model_server_endpoint}}/function_calling HTTP/1.1
@@ -119,7 +118,7 @@ Content-Type: application/json
 }
 
 ### talk to Arch-Intent directly for completion
-POST {{archfc_endpoint}}/v1/chat/completions HTTP/1.1
+POST {{{{archfc_endpoint}}}}/v1/chat/completions HTTP/1.1
 Content-Type: application/json
 
 {
@@ -239,7 +238,7 @@ POST {{model_server_endpoint}}/function_calling HTTP/1.1
 Content-Type: application/json
 
 {
-  "model": "--",
+  "model": "None",
   "messages": [
     {
       "role": "user",
diff --git a/tests/rest/api_prompt_gateway.rest b/tests/rest/api_prompt_gateway.rest
index db06cafe..4a1f77e0 100644
--- a/tests/rest/api_prompt_gateway.rest
+++ b/tests/rest/api_prompt_gateway.rest
@@ -68,7 +68,7 @@ Content-Type: application/json
     {
       "role": "assistant",
       "content": "It seems I'm missing some information. Could you provide the following details days ?",
-      "model": "Arch-Function-1.5b"
+      "model": "Arch-Function"
     },
     {
       "role": "user",
@@ -82,7 +82,7 @@ POST {{prompt_endpoint}}/v1/chat/completions HTTP/1.1
 Content-Type: application/json
 
 {
-  "model": "--",
+  "model": "None",
   "messages": [
     {
       "role": "user",
@@ -91,7 +91,7 @@ Content-Type: application/json
     {
       "role": "assistant",
       "content": "It seems I'm missing some information. Could you provide the following details days ?",
-      "model": "Arch-Function-1.5b"
+      "model": "Arch-Function"
     },
     {
       "role": "user",
diff --git a/tests/rest/insurance_agent.rest b/tests/rest/insurance_agent.rest
index 225c7d8a..c45ebb85 100644
--- a/tests/rest/insurance_agent.rest
+++ b/tests/rest/insurance_agent.rest
@@ -1,5 +1,5 @@
 @model_server_endpoint = http://localhost:51000
-@archfc_endpoint = https://api.fc.archgw.com
+@archfc_endpoint = https://archfc.katanemo.dev
 
 ### multi turn conversation with intent, except parameter gathering
 
@@ -55,7 +55,7 @@ Content-Type: application/json
   ]
 }
 ### talk to Arch-Intent directly for completion
-POST https://api.fc.archgw.com/v1/chat/completions HTTP/1.1
+POST https://archfc.katanemo.dev/v1/chat/completions HTTP/1.1
 Content-Type: application/json
 
 {
@@ -126,7 +126,7 @@ Content-Type: application/json
   ]
 }
 ### talk to Arch-Intent directly for completion, expect No
-POST https://api.fc.archgw.com/v1/chat/completions HTTP/1.1
+POST https://archfc.katanemo.dev/v1/chat/completions HTTP/1.1
 Content-Type: application/json
 
 {
diff --git a/tests/rest/network_agent.rest b/tests/rest/network_agent.rest
index 319b5106..dc03fa6c 100644
--- a/tests/rest/network_agent.rest
+++ b/tests/rest/network_agent.rest
@@ -1,5 +1,5 @@
 @model_server_endpoint = http://localhost:51000
-@archfc_endpoint = https://api.fc.archgw.com
+@archfc_endpoint = https://archfc.katanemo.dev
 
 ### single turn function calling all parameters insurance agent summary