trustgraph/docs/tech-specs/confidence-based-agents.md

TrustGraph Confidence-Based Agent Architecture

Technical Specification v1.0

Executive Summary

This document specifies a new agent architecture for TrustGraph that introduces confidence-based execution control as an alternative to the existing ReAct-based agent system. The architecture will be implemented as a new module set under trustgraph-flow/trustgraph/agent/confidence/ to provide enhanced reliability, auditability, and reduced hallucinations for critical knowledge graph operations.

1. Architecture Overview

1.1 Design Principles

• Modularity: New confidence-based agent lives alongside existing ReAct agent
• Service-Oriented: Follows TrustGraph's existing Pulsar-based service patterns
• Schema-Driven: Leverages existing schema definitions with minimal extensions
• Tool Agnostic: Works with existing tools (KnowledgeQuery, TextCompletion, McpTool)

1.2 High-Level Architecture

┌──────────────────────────────────────────────────────────────────┐
│                      Gateway Service Layer                       │
│                   (dispatch/agent_confidence.py)                 │
└─────────────────────────────┬─────────────────────────────────────┘
                              │
                     Pulsar Message Bus
                              │
┌─────────────────────────────┴────────────────────────────────────┐
│              Confidence Agent Service                            │
│            (agent/confidence/service.py)                         │
│                                                                  │
│  ┌──────────────┐   ┌─────────────────┐   ┌────────────────┐     │
│  │   Planner    │   │ Flow Controller │   │   Confidence   │     │
│  │   Module     │─▶│      Module     │─▶│   Evaluator    │     │
│  └──────────────┘   └─────────────────┘   └────────────────┘     │
│         │                  │                       │             │
│         ▼                  ▼                       ▼             │
│  ┌──────────────┐   ┌───────────────┐     ┌────────────────┐     │
│  │   Execution  │   │    Memory     │     │     Audit      │     │
│  │    Engine    │◄──│    Manager    │     │     Logger     │     │
│  └──────────────┘   └───────────────┘     └────────────────┘     │
└──────────────────────────────────────────────────────────────────┘
                             │
                    Tool Service Clients
                             │
     ┌───────────────┬───────┴─────────┬─────────────────┐
     ▼               ▼                 ▼                 ▼
KnowledgeQuery  TextCompletion      McpTool         PromptService

2. Module Specifications

2.1 Core Modules Location

All new modules will be created under:

trustgraph-flow/trustgraph/agent/confidence/
├── __init__.py
├── __main__.py
├── service.py           # Main service entry point
├── planner.py          # Planning module
├── flow_controller.py  # Flow orchestration
├── confidence.py       # Confidence evaluation
├── memory.py          # Memory management
├── executor.py        # Step execution
├── audit.py           # Audit logging
└── types.py           # Type definitions

2.2 External Interface - Drop-in Replacement

The confidence-based agent uses the existing AgentRequest and AgentResponse schemas as its external interface, making it a drop-in replacement for the ReAct agent:

Input: AgentRequest (from trustgraph-base/trustgraph/schema/services/agent.py) Output: AgentResponse (from trustgraph-base/trustgraph/schema/services/agent.py)

This ensures complete compatibility with existing gateway dispatchers and client code.

2.3 Internal Schemas

New internal schemas in trustgraph-base/trustgraph/schema/services/agent_confidence.py:

ConfidenceMetrics

• score: Float - Confidence score (0.0 to 1.0)
• reasoning: String - Explanation of score calculation
• retry_count: Integer - Number of retries attempted

ExecutionStep

• id: String - Unique step identifier
• function: String - Tool/function to execute
• arguments: Map(String) - Arguments for the function
• dependencies: Array(String) - IDs of prerequisite steps
• confidence_threshold: Float - Minimum acceptable confidence
• timeout_ms: Integer - Execution timeout

ExecutionPlan

• id: String - Plan identifier
• steps: Array(ExecutionStep) - Ordered execution steps
• context: Map(String) - Global context for plan

StepResult

• step_id: String - Reference to ExecutionStep
• success: Boolean - Execution success status
• output: String - Step execution output
• confidence: ConfidenceMetrics - Confidence evaluation
• execution_time_ms: Integer - Actual execution time

These internal schemas are used for:

• Passing structured data between confidence agent modules
• Storing execution state and metrics
• Audit logging and debugging

2.4 Communication Pattern

The confidence agent sends multiple AgentResponse messages during execution, similar to ReAct's thought/observation pattern:

1. Planning Phase: Sends responses with planning thoughts and observations about the generated execution plan
2. Execution Phase: For each step, sends responses with:
   • thought: Current step being executed and confidence reasoning
   • observation: Tool output and confidence evaluation
3. Final Response: Sends the final answer with overall confidence assessment

This streaming approach provides real-time visibility into the agent's reasoning and confidence evaluations while maintaining compatibility with existing clients.

3. Module Implementation Details

3.1 Planner Module (planner.py)

The Planner Module generates structured execution plans from user requests using an LLM to create confidence-scored step sequences.

Key Responsibilities:

• Parse user requests into structured plans
• Assign confidence thresholds based on operation criticality
• Determine step dependencies
• Select appropriate tool combinations

3.2 Flow Controller (flow_controller.py)

The Flow Controller orchestrates plan execution with confidence-based control flow, managing step dependencies and retry logic.

Key Capabilities:

• Step dependency resolution
• Confidence-based retry logic
• User override handling
• Graceful failure modes

Configuration Schema:

confidence_agent:
  default_confidence_threshold: 0.7
  max_retries: 3
  retry_backoff_factor: 2.0
  override_enabled: true
  step_timeout_ms: 30000
  parallel_execution: false

3.3 Confidence Evaluator (confidence.py)

The Confidence Evaluator calculates confidence scores for execution results based on multiple factors to ensure reliability.

Confidence Scoring Factors:

• Graph query result size and consistency
• Entity extraction precision scores
• Vector search similarity thresholds
• LLM response coherence metrics

3.4 Memory Manager (memory.py)

The Memory Manager handles inter-step data flow and context preservation, ensuring efficient memory usage while maintaining necessary state.

Memory Strategies:

• Selective context passing based on dependencies
• Graph data serialization for efficiency
• Automatic context window management
• Result caching with TTL

3.5 Executor Module (executor.py)

The Step Executor handles individual plan step execution using registered tools, managing tool selection, error handling, and result transformation.

Tool Mapping:

• GraphQuery → GraphRagClient
• TextCompletion → TextCompletionClient
• McpTool → McpToolClient
• Prompt → PromptClient

3.6 Service Implementation (service.py)

The main service class coordinates all confidence agent components and handles request/response flow through the Pulsar message bus.

Service Workflow:

1. Generate execution plan via Planner Module
2. Execute plan with confidence control via Flow Controller
3. Generate response with confidence metrics and audit trail

Client Specifications:

• TextCompletionClientSpec for LLM operations
• GraphRagClientSpec for knowledge graph queries
• ToolClientSpec for MCP tool invocations

4. Integration Points

4.1 Gateway Integration

The confidence agent reuses the existing gateway dispatcher trustgraph-flow/trustgraph/gateway/dispatch/agent.py since it uses the same AgentRequest and AgentResponse schemas. No new dispatcher is needed, making it a true drop-in replacement.

4.2 Configuration Integration

Configuration in deployment YAML:

services:
  - name: confidence-agent
    module: trustgraph.agent.confidence
    instances: 2
    config:
      max_iterations: 15
      confidence_threshold: 0.75
      
  # Existing react agent continues to work
  - name: react-agent  
    module: trustgraph.agent.react
    instances: 2

4.3 Tool Integration

The confidence agent reuses existing tool implementations:

• KnowledgeQueryImpl for graph RAG operations
• TextCompletionImpl for LLM completions
• McpToolImpl for MCP tool invocations
• PromptImpl for prompt-based operations

No changes required to existing tools.

5. End-to-End Execution Flow

5.1 Module Interaction Overview

When an AgentRequest arrives, the confidence agent orchestrates the following flow:

1. Service Entry: The main service receives the AgentRequest via Pulsar
2. Planning Phase: Service invokes Planner Module to generate an ExecutionPlan
3. Execution Loop: Service passes plan to Flow Controller, which:
   • Resolves step dependencies
   • For each step, calls Executor with context from Memory Manager
   • Evaluator assesses confidence after each execution
   • Retry logic triggered if confidence below threshold
4. Response Stream: Service sends AgentResponse messages at key points
5. Audit Trail: Logger records all decisions and confidence scores

5.2 Detailed Message Flow

sequenceDiagram
    participant Client
    participant Service as ConfidenceAgent<br/>Service
    participant Planner
    participant FlowCtrl as Flow<br/>Controller
    participant Memory
    participant Executor
    participant Evaluator
    participant Tools
    
    Client->>Service: AgentRequest
    Service->>Service: Parse request,<br/>extract config
    
    %% Planning Phase
    Service->>Planner: generate_plan(request)
    Planner->>Tools: Query available tools
    Planner->>Planner: LLM generates<br/>ExecutionPlan
    Planner-->>Service: ExecutionPlan
    Service->>Client: AgentResponse<br/>(planning thought)
    
    %% Execution Phase
    Service->>FlowCtrl: execute_plan(plan)
    
    loop For each ExecutionStep
        FlowCtrl->>Memory: get_context(step)
        Memory-->>FlowCtrl: context + dependencies
        
        FlowCtrl->>Executor: execute_step(step, context)
        Executor->>Tools: invoke_tool(name, args)
        Tools-->>Executor: raw_result
        
        Executor->>Evaluator: evaluate(result)
        Evaluator-->>Executor: ConfidenceMetrics
        
        alt Confidence >= threshold
            Executor-->>FlowCtrl: StepResult (success)
            FlowCtrl->>Memory: store_result(step, result)
            FlowCtrl->>Service: Send progress
            Service->>Client: AgentResponse<br/>(step observation)
        else Confidence < threshold
            FlowCtrl->>FlowCtrl: Retry with backoff
            Note over FlowCtrl: Max 3 retries by default
            alt After max retries
                FlowCtrl->>Service: Request override
                Service->>Client: AgentResponse<br/>(override request)
            end
        end
    end
    
    FlowCtrl-->>Service: All StepResults
    Service->>Service: Generate final answer
    Service->>Client: AgentResponse<br/>(final answer)

5.3 Confidence Decision Points

The confidence mechanism affects execution at three critical points:

1. Planning Confidence

• Planner assigns confidence thresholds to each step based on:
  • Operation criticality (graph mutations = higher threshold)
  • Tool reliability history
  • Query complexity
• Default thresholds: GraphQuery (0.8), TextCompletion (0.7), McpTool (0.6)

2. Execution Confidence

• After each tool execution, Evaluator calculates confidence based on:
  • Output completeness and structure
  • Consistency with expected schemas
  • Semantic coherence (for text outputs)
  • Result size and validity (for graph queries)

3. Retry Decision

• If confidence < threshold:
  • First retry: Same parameters with backoff
  • Second retry: Adjusted parameters (e.g., broader query)
  • Third retry: Simplified approach
  • After max retries: User override or graceful failure

5.4 Example: Graph Query with Low Confidence

Scenario: User asks "What are the connections between Entity X and Entity Y?"

Step 1: Planning

AgentRequest arrives:
  question: "What are the connections between Entity X and Entity Y?"
  
Planner generates ExecutionPlan:
  Step 1: GraphQuery
    function: "GraphQuery"
    arguments: {"query": "MATCH path=(x:Entity {name:'X'})-[*..3]-(y:Entity {name:'Y'}) RETURN path"}
    confidence_threshold: 0.8

Step 2: First Execution

Executor runs GraphQuery:
  Result: Empty result set []
  
Evaluator assesses confidence:
  Score: 0.3 (low - empty results suspicious)
  Reasoning: "Empty result may indicate entities don't exist or query too restrictive"
  
Flow Controller decides:
  0.3 < 0.8 threshold → RETRY

Step 3: Retry with Adjusted Query

Flow Controller adjusts parameters:
  New query: "MATCH (x:Entity), (y:Entity) WHERE x.name CONTAINS 'X' AND y.name CONTAINS 'Y' RETURN x, y"
  
Executor runs adjusted query:
  Result: Found 2 entities but no connections
  
Evaluator assesses confidence:
  Score: 0.85
  Reasoning: "Entities exist but genuinely unconnected"
  
Flow Controller decides:
  0.85 >= 0.8 threshold → SUCCESS

Step 4: Response Stream

AgentResponse 1 (planning):
  thought: "Planning graph traversal query to find connections"
  observation: "Generated query with 3-hop path search"

AgentResponse 2 (retry):
  thought: "Initial query returned empty, adjusting search parameters"
  observation: "Retrying with broader entity matching"

AgentResponse 3 (final):
  answer: "Entity X and Entity Y exist in the graph but have no direct or indirect connections within 3 hops"
  thought: "Query successful with high confidence after parameter adjustment"
  observation: "Confidence: 0.85 - Entities verified to exist but unconnected"

5.5 Example: Multi-Step Plan with Dependencies

Scenario: "Summarize the main topics discussed about AI regulation"

ExecutionPlan Generated:

Step 1: GraphQuery - Find documents about AI regulation
  confidence_threshold: 0.75
  
Step 2: TextCompletion - Extract key topics from documents
  dependencies: [Step 1]
  confidence_threshold: 0.7
  
Step 3: TextCompletion - Generate summary
  dependencies: [Step 2]
  confidence_threshold: 0.8

Execution Flow:

1. Step 1 Success (confidence: 0.9)

   • Found 15 relevant documents
   • Memory Manager stores document list

2. Step 2 Initial Failure (confidence: 0.5)

   • Topics extraction unclear
   • Retry with more specific prompt
   • Retry Success (confidence: 0.75)
   • Memory Manager stores topics list

3. Step 3 Success (confidence: 0.85)

   • Uses topics from memory
   • Generates coherent summary

Total AgentResponses sent: 6

• 1 for planning
• 2 for Step 1 (execution + success)
• 2 for Step 2 (failure + retry success)
• 1 for Step 3
• 1 final response

6. Monitoring and Observability

6.1 Metrics

New metrics to expose via Prometheus:

Confidence Metrics:

• agent_confidence_score - Histogram of confidence scores with buckets [0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
• agent_confidence_failures - Counter of steps failing confidence thresholds

Retry Metrics:

• agent_retry_count - Counter of retries by function name
• agent_retry_success_rate - Gauge of retry success percentage

Plan Execution Metrics:

• agent_plan_execution_seconds - Histogram of total plan execution time
• agent_step_execution_seconds - Histogram of individual step execution time
• agent_plan_complexity - Histogram of number of steps per plan

6.2 Audit Trail

Structured audit logging format:

{
    "execution_id": "550e8400-e29b-41d4-a716-446655440000",
    "timestamp": "2024-01-15T10:30:00Z",
    "request": {
        "question": "Find relationships between entities X and Y",
        "confidence_threshold": 0.75
    },
    "plan": {
        "steps": [
            {
                "id": "step-1",
                "function": "GraphQuery",
                "confidence_threshold": 0.8
            }
        ]
    },
    "execution": [
        {
            "step_id": "step-1",
            "start_time": "2024-01-15T10:30:01Z",
            "end_time": "2024-01-15T10:30:02Z",
            "confidence_score": 0.85,
            "retry_count": 0,
            "success": true
        }
    ],
    "final_confidence": 0.85,
    "total_duration_ms": 1500
}

7. Testing Strategy

7.1 Unit Tests

Location: tests/unit/test_agent/test_confidence/

Test Coverage Areas:

• Plan generation with various request types
• Confidence score calculation and validation
• Memory manager context handling
• Flow controller retry logic
• Executor tool mapping and error handling

7.2 Integration Tests

Location: tests/integration/test_agent_confidence/

Test Scenarios:

• End-to-end confidence flow with mock services
• Multi-step plan execution with dependencies
• Retry behavior under various confidence scores
• User override flow simulation
• Fallback to ReAct agent on failure

7.3 Contract Tests

Contract Validation:

• Pulsar message schema serialization/deserialization
• Compatibility with existing tool service interfaces
• Gateway dispatcher protocol compliance
• Response format consistency with ReAct agent where applicable

8. Performance Considerations

8.1 Expected Performance Impact

Metric	ReAct Agent	Confidence Agent	Impact
Latency (p50)	500ms	650ms	+30% due to planning
Latency (p99)	2000ms	3000ms	+50% with retries
Success Rate	85%	92%	+7% improvement
Memory Usage	512MB	768MB	+50% for context

8.2 Optimization Strategies

• Plan Caching: Cache plans for similar requests
• Parallel Execution: Execute independent steps concurrently
• Confidence Precomputation: Pre-calculate confidence for common operations
• Context Pruning: Aggressive memory management for large contexts

9. Security Considerations

9.1 Data Protection

• Confidence scores must not leak sensitive information
• Audit trails sanitized before logging
• Memory manager respects data classification levels

9.2 Access Control

• Inherit existing TrustGraph RBAC policies
• Override functionality requires elevated privileges
• Audit trail access restricted to administrators

10. Phase 2: Microservices Architecture

10.1 Evolution from Monolithic to Microservices

The initial implementation of the confidence-based agent will be a single monolithic service containing all modules (Planner, Flow Controller, Executor, Evaluator, Memory Manager, and Audit Logger) as described in this specification. This approach simplifies initial development and testing.

In Phase 2, the monolithic service will be decomposed into separate microservices, each exposed as independent Pulsar request/response services. This evolution provides:

• Better scalability and resource allocation
• Independent deployment and versioning
• Fault isolation and resilience
• Reusability across different agent architectures

10.2 Proposed Microservice Decomposition

The following microservices will be created, each with its own request/response schema:

1. Planning Service (trustgraph-flow/trustgraph/planning/)

• Purpose: Generate execution plans from natural language requests
• Request Schema: PlanningRequest (question, context, available_tools)
• Response Schema: PlanningResponse (execution_plan, confidence_thresholds)
• Queue: planning-request / planning-response
• Reusability: Can be used by other agent architectures needing structured plans

2. Confidence Evaluation Service (trustgraph-flow/trustgraph/confidence/)

• Purpose: Evaluate confidence scores for any execution result
• Request Schema: ConfidenceRequest (function_name, input, output, context)
• Response Schema: ConfidenceResponse (score, reasoning, recommendations)
• Queue: confidence-request / confidence-response
• Reusability: Can evaluate confidence for any service output

3. Execution Context Service (trustgraph-flow/trustgraph/context/)

• Purpose: Manage execution context and memory across steps
• Request Schema: ContextRequest (operation: store/retrieve, step_id, data)
• Response Schema: ContextResponse (context_data, dependencies)
• Queue: context-request / context-response
• Reusability: General-purpose context management for workflows

4. Flow Orchestration Service (trustgraph-flow/trustgraph/orchestration/)

• Purpose: Execute plans with dependency resolution and retry logic
• Request Schema: OrchestrationRequest (execution_plan, config)
• Response Schema: OrchestrationResponse (step_results, status)
• Queue: orchestration-request / orchestration-response
• Reusability: Can orchestrate any structured execution plan

5. Audit Service (trustgraph-flow/trustgraph/audit/)

• Purpose: Centralized audit logging for all agent operations
• Request Schema: AuditRequest (execution_id, event_type, details)
• Response Schema: AuditResponse (logged, audit_id)
• Queue: audit-request / audit-response
• Reusability: System-wide audit trail service

10.3 Phase 2 Architecture

┌──────────────────────────────────────────────────────────────────┐
│                      Gateway Service Layer                       │
└─────────────────────────────┬─────────────────────────────────────┘
                              │
                     Pulsar Message Bus
                              │
┌─────────────────────────────┴────────────────────────────────────┐
│         Confidence Agent Coordinator Service                     │
│            (thin orchestration layer)                            │
└─────────────────────────────┬────────────────────────────────────┘
                              │
            ┌─────────────────┼─────────────────┐
            │                 │                 │
     ┌──────▼───────┐  ┌─────▼──────┐  ┌──────▼───────┐
     │   Planning   │  │ Confidence │  │   Context    │
     │   Service    │  │ Evaluation │  │   Service    │
     └──────────────┘  │  Service   │  └──────────────┘
                       └────────────┘
            │                 │                 │
     ┌──────▼───────┐  ┌─────▼──────┐
     │     Flow     │  │   Audit    │
     │ Orchestration│  │  Service   │
     │   Service    │  └────────────┘
     └──────────────┘

10.4 Migration Strategy

Phase 1 to Phase 2 Migration Path:

1. Extract Interfaces: Define clean interfaces between existing modules
2. Create Service Wrappers: Wrap each module with Pulsar service endpoints
3. Gradual Extraction: Extract one service at a time:
   • Start with Audit Service (least coupled)
   • Then Confidence Evaluation Service
   • Follow with Context Service
   • Extract Planning Service
   • Finally, Flow Orchestration Service
4. Coordinator Simplification: Reduce main service to thin coordination layer
5. Testing: Ensure backward compatibility at each step

10.5 Benefits of Microservices Architecture

Operational Benefits:

• Independent Scaling: Scale planning service during peak analysis
• Fault Isolation: Confidence service failure doesn't affect execution
• Technology Flexibility: Use specialized models for different services
• Development Velocity: Teams can work on services independently

Architectural Benefits:

• Reusability: Planning service usable by other agents
• Composability: Mix and match services for different use cases
• Versioning: Deploy service updates without full system changes
• Testing: Easier unit and integration testing per service

10.6 Configuration for Phase 2

# Phase 2 microservices configuration
services:
  - name: confidence-agent-coordinator
    module: trustgraph.agent.confidence_coordinator
    dependencies:
      - planning-service
      - confidence-service
      - context-service
      - orchestration-service
      - audit-service
      
  - name: planning-service
    module: trustgraph.planning
    instances: 3
    
  - name: confidence-service
    module: trustgraph.confidence
    instances: 2
    
  - name: context-service
    module: trustgraph.context
    instances: 2
    storage: redis
    
  - name: orchestration-service
    module: trustgraph.orchestration
    instances: 4
    
  - name: audit-service
    module: trustgraph.audit
    instances: 1
    storage: postgresql

11. Open Questions and Future Work

11.1 Immediate Questions for Implementation

1. LLM Integration: The Planning Module will use the existing prompt service for all LLM interactions, with prompt templates stored in the configuration service following TrustGraph's standard approach. This ensures consistency with existing patterns and centralized template management.

2. Confidence Calibration: What specific calibration methodology should be used to ensure confidence scores are meaningful across different operation types?

3. Parallel Execution: Should Phase 1 include parallel step execution, or defer to Phase 2?

11.2 Future Enhancements

1. Adaptive Thresholds: Machine learning-based threshold adjustment based on historical performance

2. Plan Templates: Pre-defined execution templates for common query patterns

3. Multi-Agent Coordination: Support for confidence-based multi-agent workflows

4. Explainable Confidence: Natural language explanations for confidence scores

12. Conclusion

This specification defines a confidence-based agent architecture that:

• Integrates seamlessly with existing TrustGraph infrastructure
• Provides enhanced reliability through confidence-based control
• Maintains compatibility with existing tools and services
• Enables gradual adoption through side-by-side deployment

The architecture is designed to be implemented incrementally, tested thoroughly, and deployed safely alongside the existing ReAct agent system.

Appendix A: Configuration Strategy

A.1 Configuration Hierarchy

The confidence-based agent follows TrustGraph's standard configuration approach with multiple override levels:

1. Command Line Parameters (highest precedence)

# Example command line with overrides
tg-confidence-agent \
  --confidence-threshold=0.8 \
  --max-retries=5 \
  --timeout-ms=45000 \
  --audit-enabled=true

2. Built-in Defaults (lowest precedence)

• Ensures useful functionality without any configuration
• Default confidence threshold: 0.75
• Default max retries: 3
• Default timeout: 30000ms
• All core functionality enabled by default

3. Future Configuration Sources (open question)

• Request-level overrides in AgentRequest.plan field
• Configuration service stored settings
• Runtime dynamic adjustment based on performance

A.2 Default Configuration Values

The service operates with sensible defaults when no parameters are specified:

# Built-in defaults (no configuration required)
defaults:
  confidence_threshold: 0.75
  max_retries: 3
  retry_backoff_factor: 2.0
  step_timeout_ms: 30000
  max_iterations: 15
  
  # Tool-specific defaults
  tool_thresholds:
    GraphQuery: 0.8
    TextCompletion: 0.7
    McpTool: 0.6
    
  # Memory defaults
  max_context_size: 8192
  cache_ttl_seconds: 300
  
  # Audit defaults
  audit_enabled: true
  audit_level: INFO
  
  # Performance defaults
  parallel_execution: false
  plan_cache_size: 100

A.3 Command Line Override Examples

# High confidence mode
tg-confidence-agent --confidence-threshold=0.9 --max-retries=1

# Development mode with verbose audit
tg-confidence-agent --audit-level=DEBUG --timeout-ms=60000

# Performance optimized
tg-confidence-agent --parallel-execution=true --plan-cache-size=500

# Tool-specific threshold override
tg-confidence-agent --graph-query-threshold=0.85

A.4 Open Questions for Future Configuration

1. Request-Level Overrides: Should confidence thresholds be configurable per-request via the AgentRequest.plan field?

2. Dynamic Configuration: Should the service support runtime configuration updates via the configuration service?

3. User Profiles: Should different user types have different default confidence thresholds?

4. Context-Aware Thresholds: Should confidence thresholds adapt based on query complexity or domain?

5. Configuration Persistence: Should override settings be persisted across service restarts?

A.5 Configuration Priority Resolution

When multiple configuration sources are present:

1. Command line parameters override all others
2. Built-in defaults provide baseline functionality
3. Future sources (request/config service) will insert between these levels
4. Configuration validation ensures all values remain within acceptable ranges

Appendix B: API Examples

Request Example (AgentRequest)

{
  "question": "What are the relationships between Company A and Company B in the knowledge graph?",
  "plan": "{\"confidence_threshold\": 0.8, \"max_retries\": 3}",
  "state": "initial",
  "history": []
}

Interim Response Example (AgentResponse - Planning)

{
  "answer": "",
  "thought": "Creating execution plan with confidence thresholds for graph query",
  "observation": "Plan generated: 1 step with GraphQuery function, confidence threshold 0.8",
  "error": null
}

Interim Response Example (AgentResponse - Execution)

{
  "answer": "",
  "thought": "Executing GraphQuery to find relationships between Company A and Company B",
  "observation": "Query returned 3 relationships with confidence score 0.92",
  "error": null
}

Final Response Example (AgentResponse)

{
  "answer": "Company A and Company B have 3 relationships: 1) Partnership agreement signed 2023, 2) Shared board member John Doe, 3) Joint venture in Project X",
  "thought": "Analysis complete with high confidence (0.92)",
  "observation": "All steps executed successfully. Audit trail available at: execution-log-789",
  "error": null
}