Building Scalable AI Agents with FastAPI and LangChain

Building autonomous AI agents is one of the most exciting frontiers in software engineering. In this post, I'll walk through the architecture of my Autonomous AI Brain project — a multi-agent system that uses Claude 3.5 Sonnet to diagnose and patch CI/CD failures. The Architecture The system is built on three pillars: 1. Orchestration Layer — FastAPI serves as the backbone, routing tasks between specialized agents that handle diagnosis, code analysis, and patch generation. 2. RAG Pipeline — Using Greptile's vector-graph indexing, the system gains deep codebase observability. This eliminates the hallucination problem that plagues naive LLM-based code analysis. 3. Secure Execution — E2B cloud sandboxes provide isolated environments for testing AI-generated fixes before they're submitted as PRs. Key Learnings - Always validate AI-generated code in sandboxed environments before applying to production - Vector-graph indexing significantly outperforms flat vector search for codebase understanding - Persistent agent memory via PostgreSQL enables the system to learn from past fixes This project taught me that the future of DevOps isn't just automation — it's intelligent, self-healing systems.