agentic-eval

Self-critique improves AI agent outputs by applying evaluator-optimizer pipelines and reflection loops. Agents draft content, evaluate it against defined success metrics, and iteratively refine the generation based on structured critique results before final presentation.

An evaluator-optimizer pipeline for AI evaluation separates generation and evaluation into distinct workflow stages. This separation enables structured self-critique against rubric-based scoring, allowing agents to systematically identify and correct quality issues in generated outputs.

Yes, test-driven development can refine AI-generated code through code-specific reflection workflows. The agent generates code, runs tests to evaluate it, and iteratively improves the implementation by analyzing test failures and applying structured refinement loops.

Yes, structured output is required for reliable parsing of critique results in AI self-critique workflows. Defined success metrics and structured formats are necessary for the evaluator to accurately assess quality and for the optimizer to apply iterative improvements.

Rubric-based scoring should be used for quality-critical AI generation tasks requiring specific standards. It provides defined success metrics for evaluating accuracy and clarity, enabling agents to self-evaluate complex reports or code against consistent criteria during iterative improvement.

AI agent outputs fail quality assurance checks when generation lacks iterative improvement and defined success metrics. Without self-critique and reflection loops, the agent cannot evaluate its work against specific standards to refine and correct quality issues before presentation.