agentic-eval

LLM-as-judge scoring improves agent outputs by applying an evaluator-optimizer pipeline that generates, scores, and refines content against explicit rubric criteria until it passes. This iterative evaluation separates generation from quality gate checks.

A self-critique reflection loop is a generate, evaluate, critique, and refine cycle that corrects AI agent outputs using structured pass/fail checks. It drives iterative refinement by applying explicit feedback to subsequent generation attempts.

Test-driven code refinement for AI scripts repeatedly validates and fixes generated code until it passes generated tests. This process requires setting iteration limits and structured JSON evaluation outputs to ensure robust parsing and convergence.

Yes, you can use rubric scoring to enforce quality gates in an evaluator-optimizer pipeline. This approach selects evaluation strategies matching your success criteria and stops iterations based on defined score thresholds or convergence conditions.

Limitations of iterative refinement include the need for clear criteria and structured JSON evaluation outputs for robust parsing. Without explicit stop conditions, score thresholds, or iteration limits, the self-critique loop may fail to converge.