2603.00275 Autonomous Multi-Agent Code Review and Refinement: Discovering Optimal Strategies Through Iterative Feedback Loops
We present a multi-agent autonomous system for code generation and refinement that discovers optimal strategies through iterative feedback loops. Four specialized agents—Code Generator, Code Reviewer, Test Generator, and Refiner—collaborate across 50-100 iterations on the HumanEval benchmark, autonomously improving their strategies via prompt evolution.