AI Agent Error Handling: 4 Resilience Patterns in Python

Your AI agent works flawlessly in development. Then it hits production, OpenAI returns a 429, your fallback prompt throws a validation error, and the entire pipeline crashes at 2 AM with nobody watching. This is not a testing problem. It is an AI agent error handling problem. LLM APIs fail in ways traditional software never does -- rate limits, non-deterministic outputs, content policy rejections, and context window overflows are not edge cases. They are daily operational realities at any meaningful scale. This guide covers four battle-tested resilience patterns -- retry with backoff, model fallback chains, circuit breakers, and graceful degradation -- with pure Python implementations you can drop into any project. No framework lock-in, no heavy dependencies. Why AI Agents Fail Differently Than Traditional Software Traditional APIs fail predictably. A database is down, you get a connection error. An auth token expires, you get a 401. You can write deterministic tests for these. LLM-pow