# Autonomous Code Refactoring Agent: A Legacy Monolithic Architecture Modernization Solution Based on Multi-Agent Workflow

> A multi-agent system built using OpenCode, Aider, and Claude/GPT models that can autonomously scan, understand, refactor, and validate legacy monolithic architecture code. It ensures refactoring quality through a closed-loop verification mechanism, significantly reducing technical debt and maintenance costs.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-02T00:44:13.000Z
- 最近活动: 2026-05-02T01:56:31.262Z
- 热度: 155.8
- 关键词: 代码重构, 遗留系统, 单体架构, 多智能体系统, OpenCode, Aider, Claude, GPT, 技术债务, 自动化测试, 闭环验证
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-21leminh-autonomous-refactoring-agent
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-21leminh-autonomous-refactoring-agent
- Markdown 来源: floors_fallback

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## Introduction: Overview of the Autonomous Code Refactoring Agent Solution

This article introduces a legacy monolithic architecture modernization solution based on multi-agent workflow, built using OpenCode, Aider, and Claude/GPT models. It enables autonomous scanning, understanding, refactoring, and validation of legacy code, ensuring refactoring quality through a closed-loop verification mechanism and significantly reducing technical debt and maintenance costs.

## Problem Background: The Technical Debt Dilemma of Legacy Systems

Originating from an enterprise resource planning (ERP) platform (PHP, MySQL, Apache stack) that has been in operation for over a decade, code structure degradation has occurred due to feature iterations, requirement changes, etc.: highly coupled business logic, undocumented dependencies, and lack of modularization. Team pain points: high investment in adding simple features, high regression risk during deployment; senior engineers spend 40% of their weekly time on code sorting, vulnerability identification, and PR reviews—manual supervision is expensive and error-prone.

## Solution: Fully Automated Technical Debt Reduction System

The goal is to automate the tedious aspects of technical debt reduction and free the team to focus on high-value work. It adopts a multi-agent workflow architecture, combining OpenCode and Aider frameworks, driven by Claude/GPT models to address legacy system modernization challenges.

## Architecture Design: Separation of Strategic and Tactical Layers

The core innovation is the separation of strategic and tactical layers:
- OpenCode Strategic Layer: Claude model (with a large context window) is responsible for global repository scanning, dependency mapping, and business logic understanding;
- Aider Tactical Layer: GPT model handles micro-refactoring, rapid iterative modifications, writing unit tests, and resolving syntax errors.
Separation of concerns balances deep architectural reasoning and precise code generation.

## Four-Stage Autonomous Workflow

The system operates in a four-stage cycle:
1. Ingestion and Static Analysis: Scan directories, perform deep semantic analysis to identify anti-patterns (e.g., large functions violating SOLID principles);
2. Strategy Formulation: Generate a refactoring blueprint, plan decoupling solutions, and ensure complete business logic;
3. Execution and Collaboration: Assign tasks to Aider, execute code rewriting (e.g., SQL migration to ORM), and retain business logic;
4. Closed-Loop Verification: Automatically generate unit tests, execute tests, and if failed, analyze the cause, generate patches for fixes until 100% pass rate is achieved.

## Practical Effects and Business Value

After deployment, it changed team operations:
- Reduced review time, freeing up senior engineers;
- Lowered defect rate, with closed-loop verification automatically capturing and fixing regressions;
- Controllable technical debt, enabling continuous modernization;
- Generated documents and test cases serve as living documents.

## Technical Insights and Future Outlook

Insights: Tool combination (complementing advantages of different models), verification mechanism (autonomous verification is key), progressive modernization (avoiding large-scale rewrite risks). Future: Extend AI agents to software engineering fields such as architecture design, performance optimization, and security auditing.