Hi HN,
I wanted to share a reference implementation I architected for moving AI Agents from local prototypes to production services.
The Context:
It is relatively easy to get an agent working on a local machine where you can watch the terminal output and restart it if it gets stuck. However, the architecture often breaks down when moving to a headless, hosted environment where the agent needs to handle loops, persistent state, and structured output failures autonomously.
The Solution:
This repo is a 10-lesson lab where you build an "AI Codebase Analyst" designed to handle those operational constraints.
Key Architectural Decisions:
1) State Management (LangGraph): We use LangGraph to implement the State Machine pattern rather than a linear Chain. This provides a standardized way to handle cyclic logic (loops) and persistence without writing "spaghetti code" while loops.
2) Reliability (Pydantic): Treating the LLM as a probabilistic component. We wrap tool calls in strict Pydantic schemas to catch and retry malformed JSON before it hits the application logic.
3) Deployment (Docker): A production-ready Dockerfile setup for serverless environments.
The Repo Structure:
starter branch: A clean boilerplate to build from scratch.
main branch: The full solution code.
curriculum/ folder: The step-by-step guide.
Happy to answer questions about the stack or the trade-offs involved.
