agentic-eval

Build self-critique loops for AI agents by applying iterative evaluation patterns where agents assess their own outputs against structured rubrics and propose refinements until quality criteria are met.

An evaluator-optimizer workflow separates generation and evaluation into distinct phases to tighten reliability, using rubric-based scoring and JSON-structured feedback to guide iterative improvements.

Apply rubric-based scoring to code reviews and data analysis by configuring evaluation loops with structured dimensions, enabling LLM-as-judge evaluation to guide improvements and enforce safe guardrails.

Yes, you can use self-critique loops for high-stakes decision-making prompts by applying configurable evaluation loops and safe guardrails to prevent unsafe prompts while assessing and improving agent decisions.

The best way to structure feedback for iterative agent refinement is using JSON-structured feedback, which provides organized, machine-readable evaluation data to systematically guide output improvements.

Avoid using automated self-evaluation loops when tasks lack clear quality criteria for rubric-based scoring or when safe guardrails cannot adequately prevent unsafe prompts in highly subjective contexts.