agentic-eval

To improve AI agent output quality through self-critique, apply pattern-based evaluation frameworks that enable agents to evaluate and refine their own generated content across iterative loops. This enforces structured generate-evaluate-refine cycles for objective scoring.

An evaluator-optimizer pattern for AI prompt engineering separates generation and evaluation into distinct components. This split clarifies responsibilities within the self-critique loop, allowing the evaluator component to objectively score and refine the generator's output.

To prevent runaway loops during iterative AI output refinement, implement built-in safety guardrails within the evaluation framework. These guardrails automatically terminate the generate-evaluate-refine cycle when objective scoring thresholds are met or iteration limits are reached.

Yes, you can get structured JSON outputs from AI self-critique cycles. The evaluation framework produces structured JSON outputs during the generate-evaluate-refine process, enabling robust parsing and seamless integration with downstream applications.

To perform test-driven code refinement, use a code-specific reflection loop that evaluates generated code against tests. The agent iteratively critiques and refines the code output until it passes the defined test cases, ensuring functional correctness.