Designing Reliable AI Agent Pipelines: Retries, Idempotency, and Observability

A demo agent has to work once, on the happy path, in front of an audience. A production agent has to work every time, including when an upstream API times out, a model returns something malformed, or the same event gets delivered twice.

That means treating agent steps like any other distributed system component: idempotent by default, retried with backoff instead of silently failing, and logged in enough detail that when something does go wrong, you can tell whether it was the model, the tool call, or the data.

This post covers the patterns I build into every agent pipeline before it touches a client's production data — not because they're exciting, but because they're the difference between a system people trust and one they have to babysit.