Agent Debate and Critique Pattern - Interactive Visualization

The agent debate pattern uses multiple LLM agents with distinct roles to iteratively improve output quality. A Proposer generates an initial answer; a Critic identifies specific flaws, gaps, and inaccuracies; a Refiner incorporates the critique to produce a better version. Repeating this cycle across multiple rounds converges toward higher-quality, more complete answers - a form of structured self-refinement that outperforms single-pass generation on complex tasks.

• Proposer generates an initial answer; quality is typically 0.55–0.65 on the first pass
• Critic identifies concrete, specific flaws - not vague feedback - to drive meaningful improvement
• Refiner incorporates critique to produce a measurably better answer each round
• Quality scores consistently rise 0.15–0.25 points across 3 debate rounds across task types

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