How Reflection Loops Help Python AI Agents Catch and Fix Their Own Errors

Language models frequently produce incorrect outputs, making automated error correction a practical necessity for production deployments. Reflection loops address this by having an AI agent generate a response, evaluate it through a critique step, and retry until the output meets a defined quality threshold. The critique phase typically involves a second LLM call with a different prompt, or a deterministic check such as a JSON schema validator or unit test runner. A key principle behind the pattern is that models find it easier to identify flaws in existing output than to generate flawless content from scratch. To prevent runaway costs, developers are advised to cap reflection cycles at four to five iterations per request and monitor token spend across attempts.