Agentcodereview: A Multi-Agent Framework For Explainable Code Review And Automated Bug Repair

Abstract

Modern software development depends heavily on code review and timely bug fixing, yet manual review is slow, inconsistent and hard to scale, while single-model automated approaches based on Large Language Models (LLMs) frequently produce opaque suggestions and rarely close the loop between detecting a defect and repairing it. This paper proposes AgentCodeReview, a multi-agent framework that decomposes explainable code review and automated bug repair into cooperating specialized agents — Retrieval, Review, Explanation, Repair and Verification — coordinated by an Orchestrator over a shared retrieval-augmented context store, with a verification-driven feedback loop that iteratively refines candidate patches until they pass their tests. We describe the architecture, the inter-agent communication protocol and an evaluation design over widely used code-review and program-repair benchmarks, comparing the framework against single-agent and non-agentic baselines using precision, recall, F1, repair success rate and a human-aligned explanation-quality score. The results indicate that role specialization and explicit verification improve review accuracy, repair effectiveness and the transparency of the generated rationales, offering a reproducible pathway toward trustworthy autonomous software maintenance.