The Production Agent Checklist: What Every AI Agent Needs Before It Touches Real Users

The Production Agent Checklist: What Every AI Agent Needs Before It Touches Real Users Most AI agents that reach production aren't ready for it. They work in demos. They pass the tests the developer wrote. Then they hit real users and start failing in ways that are hard to detect and harder to debug. This is a practical checklist. Not "10 tips to improve your AI," not a sales pitch — a real pre-flight list for teams shipping Python agents to production. Work through it before you flip the traffic switch. 1. Error Handling That Actually Handles Errors The wrong version: def call_llm ( prompt : str ) -> str : response = openai . chat . completions . create ( model = " gpt-4o " , messages = [{ " role " : " user " , " content " : prompt }] ) return response . choices [ 0 ]. message . content This crashes on rate limits, network errors, and API outages. It also returns empty strings or None if the model returns an unexpected response format — which happens more than you'd think. The right v