Agentic AI in Production: Patterns That Actually Ship
8 min read
The gap between a LangChain demo and a production agent system is mostly engineering discipline — API boundaries, state persistence, retrieval quality, and failure recovery.
At Tru, I work with FastAPI services that expose stable endpoints while LangGraph handles multi-step agent orchestration. Weaviate stores embeddings for retrieval; PostgreSQL holds agent state and audit trails.
The patterns that survive production: idempotent tool calls, explicit timeouts, human-in-the-loop checkpoints for high-risk actions, and observability at every agent step — not just the final output.
The useful test is whether someone has debugged retrieval misses and agent loops after the thing went live.