agentic-eval

Self-critique and reflection loops improve AI agent outputs by applying structured evaluation patterns that generate, evaluate, critique, and refine content. This iterative process continues until the result meets predefined quality criteria and rubrics.

An evaluator-optimizer pipeline assesses quality-critical generation by coordinating modular components. It establishes a Generate, Evaluate, Critique, and Refine loop to iteratively improve code, reports, and analyses until satisfying structured evaluation criteria.

Test-driven code refinement workflows use LLM-as-judge patterns to evaluate generated code against structured rubrics. The system captures results with JSON-structured outputs and applies code-specific reflection to provide final improved outputs.

Rubric-based evaluation systems apply to code, reports, and analyses. By using modular evaluation components, the system measures and improves agent response quality across various high-stakes content types to meet defined outcomes.

Agent evaluation requires no external dependencies. It uses modular components to coordinate generation and refinement steps, capturing evaluation results with JSON-structured outputs to provide final improved outputs meeting defined rubrics.