videointermediate

I Built an AI That Writes, Reviews & Runs Code 🤯 | AutoGen Multi-Agent System (Python)

By Nidhi ChouhanApril 29, 2026youtube

This tutorial demonstrates building a multi-agent AI system using AutoGen that autonomously writes, reviews, and executes code. The system leverages multiple specialized agents (writer, reviewer, executor) that collaborate through conversation to develop and validate Python code. This approach showcases how AI agents can work together to improve code quality and automate the entire development workflow.

Key Points

•Use AutoGen framework to create multiple specialized agents with distinct roles (code writer, code reviewer, code executor)
•Implement agent-to-agent communication through conversational loops where agents discuss and refine code
•Leverage LLM capabilities to generate production-ready code with built-in quality checks
•Create a code executor agent that safely runs Python code and provides feedback to other agents
•Design a workflow where the code reviewer agent validates logic, style, and best practices before execution
•Use system prompts to define clear agent personalities and responsibilities
•Implement error handling and feedback loops so agents can iterate on code improvements
•Automate the entire development cycle from specification to testing without manual intervention

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Workflow Diagram

Start Process

Step A

Step B

Step C

Complete

Quality★★★★★

Concepts

Skills & Tools Multi-Agent Systems Agent Teams Tool Use Coding Workflows

Artifacts (2)

AutoGen Multi-Agent System Setuppythonscript

# Example AutoGen multi-agent setup structure
from autogen import AssistantAgent, UserProxyAgent

# Define Code Writer Agent
code_writer = AssistantAgent(
    name="CodeWriter",
    system_message="You are an expert Python developer. Write clean, efficient code.",
    llm_config={"model": "gpt-4"}
)

# Define Code Reviewer Agent
code_reviewer = AssistantAgent(
    name="CodeReviewer",
    system_message="You are a code quality expert. Review code for bugs, style, and best practices.",
    llm_config={"model": "gpt-4"}
)

# Define Code Executor Agent
code_executor = UserProxyAgent(
    name="CodeExecutor",
    system_message="You execute Python code and report results.",
    human_input_mode="NEVER",
    code_execution_config={"work_dir": "./code_output"}
)

# Initiate conversation
code_writer.initiate_chat(
    code_reviewer,
    message="Write a Python function to calculate fibonacci numbers"
)

Agent Workflow Configurationjsonconfig

{
  "agents": [
    {
      "name": "CodeWriter",
      "role": "Generate Python code based on requirements",
      "model": "gpt-4",
      "temperature": 0.7
    },
    {
      "name": "CodeReviewer",
      "role": "Review code quality, logic, and best practices",
      "model": "gpt-4",
      "temperature": 0.3
    },
    {
      "name": "CodeExecutor",
      "role": "Execute code and validate functionality",
      "model": "gpt-4",
      "code_execution": true,
      "work_directory": "./code_output"
    }
  ],
  "workflow": "sequential",
  "max_iterations": 5,
  "timeout": 300
}