agentic-eval

To evaluate and improve AI agent outputs through self-critique, you apply iterative refinement patterns where the agent evaluates its own generation against defined criteria and automatically corrects identified flaws before final presentation.

An evaluator-optimizer pipeline for AI quality is a system pattern where an agent generates content, an evaluator mechanism critiques it against specific rubrics, and the agent iteratively refines the output to achieve higher quality standards.

To apply test-driven code refinement to AI-generated code, use specialized patterns where the AI agent drafts code, runs tests to evaluate quality, and iteratively revises the implementation until it meets the specified test requirements.

Yes, you can build self-improving AI agents by implementing rubric-based evaluation systems that allow the agent to measure its own response quality and perform iterative refinement to satisfy accuracy and clarity standards.

AI self-critique works for complex report generation by having the agent draft the report, critique its own output for accuracy and clarity, and revise the draft against required standards before presenting the final version.

A limitation of using rubric-based evaluation for AI content refinement is that the quality of the iterative improvement depends entirely on how well the rubrics are defined, potentially missing subjective flaws not covered by the evaluation criteria.