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Agent Architecture Learning Curriculum ​

Welcome to the deep dive into the Tool-Agnostic Agent Architecture. This documentation is designed to take you from a conceptual understanding of our agent design to a line-by-line code comprehension.

Curriculum Overview ​

This course is split into 4 logical modules. It is recommended to read them in order.

Module 1: The Tool-Agnostic Paradigm ​

  • Concept: What does "tool agnostic" mean?
  • Why: The business case for decoupling reasoning from capabilities.
  • Technology: The Model Context Protocol (MCP).

Module 2: The Orchestrator (The Brain) ​

  • Architecture: Deep dive into agent_orchestrator.py.
  • Logic: Understanding the "Think -> Act -> Observe" loop.
  • Code: StateGraph, route_tools, and sub_agent_node.

Module 3: Dynamic Tooling (The Body) ​

  • Mechanism: How agents are assembled on-the-fly.
  • Discovery: The Role of tool_registry.py and semantic search.
  • Factory: Inside create_final_agent_pipeline.

Module 4: Safety & Persistence (The Conscience) ​

  • Safety: Implementing Human-in-the-Loop with human_review_node.
  • Persistence: How RedisSaver enables long-running tasks.
  • Permissions: The check_tool_approval logic.

Module 5: Debugging & Tracing ​

  • Logs: How to read the uvicorn output.
  • Common Errors: Context windows and stuck states.
  • Redis: Inspecting the agent's memory.

Where do I start in the code? ​

If you want to follow the flow of a request:

  1. Start Here: backend/app/routes/agent.py -> chat() function.
    • This is where the API request lands.
  2. Next: backend/app/services/agent/agent_factory.py -> create_final_agent_pipeline.
    • This is where the agent is built.
  3. Then: backend/app/services/agent/agent_orchestrator.py -> GraphAgentExecutor.invoke.
    • This is where the thinking happens.

Before diving in, you should be familiar with:

  • Python 3.10+ (AsyncIO)
  • LangGraph: Our state machine framework.
  • LangChain: The underlying cognitive architecture.
  • MCP: Model Context Protocol basics.