A demo agent that loops three times, calls one tool, and returns "Hello, I helped you" is easy. A production agent that handles 10k requests a day across paying customers, without lighting your API bill on fire or hallucinating tool arguments at 3am, is a different animal. I've shipped AI agents in production for the last 18 months — search, content generation, support triage, document analysis. The same seven patterns keep showing up in every codebase that actually works. None of them are exotic. Most of them are boring. That's the point: production agents are boring on purpose. Here are the patterns, with Python examples you can drop into your own loop today. 1. The Tool Result Validator Problem: LLMs hallucinate tool arguments. They will confidently call send_email(to="user@example.com", subject="Refund", body="...") when the user never asked for an email. They will pass user_id="123abc" to a function that requires an integer. They will invent product SKUs that don't exist.…