agentic-eval

You can evaluate and improve LLM code quality by running an iterative refinement loop that separates generation, evaluation, and refinement components. This process uses structured rubric-based scoring and self-critique to identify weaknesses and generate targeted improvements until the output meets quality thresholds.

An evaluator-optimizer workflow for AI agents is a pattern that separates generation, evaluation, and refinement for clearer accountability. It enables structured evaluation of reasoning, code, and reports by applying self-critique and automated iteration to continuously refine agent outputs.

To prevent endless loops during AI agent self-critique and refinement, you can use convergence controls with rubric-based scores. These controls evaluate the generated output against specific criteria to determine when to stop iterating and present the refined result.

Yes, you can use structured rubrics to evaluate AI-generated reports and analysis. This approach applies modular evaluation pipelines that output structured JSON or rubric scores, ensuring high-quality reasoning across tasks by systematically identifying and correcting weaknesses.

Yes, this approach supports test-driven feedback for code-focused refinement. It enables automated iteration for coding tasks by separating generation and evaluation, allowing the system to apply targeted improvements based on test results and structured evaluation criteria.

The best way to structure an AI evaluation pipeline for high-quality reasoning is to separate generation, evaluation, and refinement into distinct components. This modular approach uses self-critique and rubric-based scoring to systematically identify weaknesses and apply targeted improvements until convergence.