Multi-Agent Code Review
This workflow details how AutoGen can orchestrate a team of AI agents to perform autonomous code reviews, identify issues, and suggest improvements, enhancing code quality and accelerating development cycles.
/ quick answer
Utilize an AutoGen multi-agent system to autonomously review code, identify issues (bugs, vulnerabilities, style), and suggest improvements, streamlining the development process. This workflow details how AutoGen can orchestrate a team of AI agents to perform autonomous code reviews, identify issues, and suggest improvements, enhancing code quality and accelerating development cycles.
- 01Developer pushes code to a feature branch or creates a pull request.
- 02Trigger event initiates the AutoGen agent team (e.g., 'Code Reviewer Agent,' 'Security Agent,' 'Performance Agent').
- 03'Code Reviewer Agent' fetches the new code and analyzes it for general quality, style, and potential bugs.
- 04'Security Agent' scans the code for common vulnerabilities and adherence to security best practices.
- 05'Performance Agent' identifies potential performance bottlenecks or inefficient code patterns.
- 06Agents converse and collaborate to consolidate findings and generate actionable feedback and suggested code changes.
- 07'Reporter Agent' compiles a detailed review report and posts it as a comment on the pull request or in a dedicated channel.
- 08Human developer reviews the AI-generated feedback and implements necessary changes.
/ frequently asked
How does the system ensure the suggested code changes are correct and safe?
The system employs multiple agents to cross-verify findings. For instance, a 'Tester Agent' could be introduced to run unit tests on suggested changes. However, human oversight remains crucial for final approval, especially for critical sections of code, ensuring that AI-generated suggestions don't introduce new issues.
Can it integrate with version control systems like GitHub?
Yes, AutoGen agents can be equipped with tools that interact directly with version control systems. This enables them to fetch pull requests, comment on code, suggest changes directly within the VCS, and update status, making them a seamless part of existing development workflows.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Coding Agent
A coding agent reads a repository, plans a change, edits files, runs tests and iterates until the task passes — instead of just suggesting snippets.
- →AI Software Engineering
AI software engineering is the practice of building software where agents write most of the code and humans own architecture, review and verification.
- →Autonomous Workflow
An autonomous workflow runs end-to-end without a human triggering each step — an agent decides the path, while humans set goals and approve exceptions.
- →Terminal Coding Agent
A terminal coding agent is an AI system that integrates with and operates within a command-line interface (CLI) to assist developers with programming tasks. It allows users to generate, debug, explain, and refactor code directly from the terminal.
Related workflows
Turn this into a repeatable process.
- →Automated GitHub Issue Triage
Label, prioritize, and route incoming issues without a maintainer.
- →Autonomous QA Testing
This workflow utilizes AutoGen to create a multi-agent system that autonomously generates test cases, executes them, identifies bugs, and suggests fixes for software applications, enhancing development efficiency.
- →Debug with Terminal Coding Agent
This workflow guides developers on using a terminal coding agent to diagnose and suggest fixes for code errors. It involves providing error messages and code snippets to the AI for efficient, in-terminal debugging assistance.
- →Multi-Agent Customer Support
This workflow outlines how CrewAI can be used to build a multi-agent customer support system that automates inquiry routing, resolution, and escalation, improving response times and customer satisfaction.
Related tool stacks
The tools that run it in production.
- →Terminal Coding Agent Stack
This stack outlines the essential components for building and operating an AI coding agent directly within a terminal environment. It combines an LLM API with development tools and execution environments.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →AutoGen Agent Research Stack
This stack outlines the core components for building an autonomous research agent system using AutoGen, focusing on dynamic information retrieval, analysis, and report generation.
- →Multi-Agent Orchestration Stack (CrewAI)
This stack outlines the essential components for deploying a robust multi-agent system using CrewAI, focusing on agent definition, tool integration, and monitoring for collaborative task execution.
Related prompts
Reusable prompts for this job.
- →AI Code Review Rubric Prompt
A reviewer prompt that finds real defects instead of style opinions.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
Related use cases
How people apply it, and what came out.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
Comparisons & alternatives
Pick between the options.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →Claude Code vs Cursor: Which Coding Agent to Use
Terminal-native agent vs IDE-native pair programmer — different jobs, both worth using.