Mintlify docs (#217)

* add the new docs

* Update to latest comprehensive docs from mintlify2 branch - Add conversations, jobs, triggers guides - Complete tools documentation - Enhanced RAG and agents docs - Add UI screenshots and images - Update contribution guide and quickstart

* added intro gif

* Update introduction.mdx with intro gif

* Add public/videos folder with Intro-Video.gif for proper Next.js/Mintlify serving

* updated gif

* Update introduction.mdx to have a working intro gif

apps/docs/docs/using-rowboat/agents.mdx

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+---
+title: "Agents"
+description: "Learn about creating and configuring individual agents within your multi-agent system"
+icon: "robot"
+---
+
+## Overview
+
+Agents are the core building blocks of Rowboat's multi-agent system. Each agent carries out a specific part of a conversation, handles tasks via tools, and can collaborate with other agents to orchestrate complex workflows.
+
+They are powered by LLMs and can:
+- Respond to user input
+- Trigger tools or APIs
+- Pass control to other agents using @mentions
+- Fetch or process internal data
+- Execute RAG (Retrieval-Augmented Generation) queries
+- Participate in sequential pipeline workflows
+
+---
+
+## Agent Types
+
+Rowboat supports several types of agents, each designed for specific use cases:
+
+| Name | Purpose | Characteristics |
+|------|---------|-----------------|
+| **Conversational Agents** (`conversation`) | Primary user-facing agents that interact directly with users and orchestrate workflows. | • Can respond to users and orchestrate workflows<br />• Typically serve as the start agent (Hub Agent)|
+| **Task Agents** (`internal`) | Specialized agents that perform specific tasks without direct user interaction. | • Focused on specific functions<br />• Return results to parent agents|
+| **Pipeline Agents** (`pipeline`) | Sequential workflow execution agents that process data in a chain. | • Execute in sequence within a pipeline<br />• Cannot transfer to other agents directly|
+
+
+---
+
+## Agent Configuration
+
+Agents are configured through two main tabs in the Rowboat Studio interface:
+
+### **Instructions Tab**
+
+
+#### Description
+A clear description of the agent's role and responsibilities
+
+#### Instructions
+Instructions are the backbone of the agent's behavior. Use the Copilot's structured format for consistency:
+
+**Recommended Structure:**
+```
+## 🧑‍💼 Role:
+[Clear description of the agent's role]
+
+## ⚙️ Steps to Follow:
+1. [Step 1]
+2. [Step 2]
+3. [Step 3]
+
+## 🎯 Scope:
+✅ In Scope:
+- [What the agent should handle]
+
+❌ Out of Scope:
+- [What the agent should NOT handle]
+
+## 📋 Guidelines:
+✔️ Dos:
+- [Positive behaviors]
+
+🚫 Don'ts:
+- [Negative behaviors]
+```
+
+#### Examples
+These help agents behave correctly in specific situations. Each example can include:
+- A sample user message
+- The expected agent response
+- Any tool calls (if applicable)
+
+### **Configurations Tab**
+
+#### Name 
+Name of the agent
+
+
+#### Behaviour
+- **Agent Type**: Choose from `conversation`, `internal`, or `pipeline`
+- **Model**: Select the LLM model (GPT-4o, GPT-4o-mini, google/gemini-2.5-flash, etc.)
+
+#### RAG
+- **Add Source**: Connect data sources to enable RAG capabilities for the agent
+
+---
+
+## Creating Your Initial Set of Agents
+
+Let Copilot bootstrap your agent graph.
+
+### Instruct Copilot
+
+Start by telling Copilot what your assistant is meant to do — it'll generate an initial set of agents with best-practice instructions, role definitions, and connected agents.
+
+<Frame>
+  <img src="/docs/img/create-agents-delivery.png" className="w-full max-w-[400px] rounded-xl" alt="Creating agents with Copilot" />
+</Frame>
+
+### Inspect the Output
+
+After applying the suggested agents, take a close look at each one's:
+- **Instructions**: Define how the agent behaves
+- **Examples**: Guide agent responses and tool use
+
+<Frame>
+  <img src="/docs/img/agent-instruction.png" alt="Inspect agent instructions" />
+</Frame>
+
+---
+
+## Updating Agent Behavior
+
+There are three ways to update an agent:
+
+### 1. With Copilot
+
+Copilot understands the current chat context and can help rewrite or improve an agent's behavior based on how it performed.
+
+<Frame>
+  <img src="/docs/img/update-agent-copilot.png" className="w-full max-w-[400px] rounded-xl" alt="Update agent using Copilot" />
+</Frame>
+
+
+
+### 2. Manual Edits
+
+You can always manually edit the agent's instructions.
+
+<Frame>
+  <img src="/docs/img/update-agent-manual.png" alt="Manually edit agent" />
+</Frame>
+
+---
+
+