Agentic AI

Reflection Pattern

The Reflection Pattern is an agentic AI design pattern where an AI agent critically evaluates its own outputs and iteratively improves them. A second evaluation step assesses the initial response for correctness, completeness, and relevance — triggering automatic revision when needed.

Why does this matter?

Reflection drastically improves AI output quality — especially for complex business decisions. An agent that critically questions its own supplier analysis delivers well-founded results instead of superficial first responses. This lowers error rates and reduces rework effort for your employees.

How IJONIS uses this

We implement Reflection Patterns as LangGraph cycles where a Critic Agent evaluates Primary Agent outputs. Evaluation criteria are defined domain-specifically — from factual accuracy in contract analysis to completeness in offer comparisons. A maximum of three iterations prevents endless loops.

Frequently Asked Questions

Does the Reflection Pattern increase AI costs?

Yes, additional tokens are consumed for evaluation and potential revision — typically 30-80% more per request. In practice, this pays off because incorrect first responses are far more expensive: wrong decisions, manual corrections, and lost trust.

When should I use the Reflection Pattern?

For all tasks where errors are costly: contract analysis, compliance review, customer communication, or financial calculations. For simple routine tasks like data extraction or classification, the overhead is usually not justified.

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