Reflection Pattern: AI Agents Self-Correct in Production

The reflection pattern is an agentic AI design approach in which a system generates an initial response, evaluates it against predefined criteria, and revises it through one or more feedback loops to improve accuracy, relevance, and safety. It can use a single model for self-critique, separate agents for peer-review-style evaluation, or external tools such as search engines and databases to validate claims, although each option involves trade-offs in reliability, latency, and cost. Developers must define stopping criteria to prevent excessive iterations, token use, and possible degradation of the response. Reflection is most useful when output quality and verifiable correctness are important and first drafts often contain errors, but it may be unsuitable for time-sensitive, high-volume, low-cost tasks or when initial responses are already sufficient. Common applications include software development, content editing, data reporting, and legal or financial summarization, while platforms such as n8n can visually coordinate generation, critique, branching, sub-workflows, and cost tracking for these processes.

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