Shipping Production-Grade AI Agents That Don't Break
Lessons from taking an LLM agent from a flashy demo to a reliable system that real users depend on every day.
A demo agent and a production agent are two very different beasts. The demo works because you control the inputs; production fails because users don't. In this piece I walk through the patterns that actually move the needle: bounded tool-calling loops, deterministic guardrails around non-deterministic models, structured outputs validated with Zod, retry budgets, and observability that lets you replay any failed run. I also cover evals—why a 50-case eval suite caught more regressions than any amount of manual testing, and how I wire them into CI so a prompt change can't silently degrade quality.
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