Multi-Agent Systems

What Is a Multi-Agent System?

Imagine you're building a house. You wouldn't ask one person to be the architect, plumber, electrician, and builder all at once. That person would get overwhelmed, make mistakes, and the house would suffer. The same thing happens with AI.

A multi-agent system is when two or more AI agents work together to solve a problem. Instead of asking one AI to do everything, you give each agent a specific job and let them collaborate. Think of it like a team — one person researches, another writes, a third checks the work.

Each agent has its own role. One might be great at finding information. Another might be a specialist at writing code. A third might be good at spotting mistakes. Together, they can handle much bigger and harder tasks than any single agent could manage alone.

Why Use More Than One Agent?

A single agent can get stuck. It might miss details, make mistakes, or run in circles when the task gets too big. Have you ever asked an AI to do something complex and gotten a messy result? That's usually because one agent is trying to juggle too many things at once.

When you use multiple agents, each one can focus deeply on its own job. They don't get distracted. They don't drop the ball on their specialty. The result is usually much better quality.

For complex projects — like building a full app, writing a book, or running a business — no single agent can handle every part well. Multi-agent systems let you scale your AI work without sacrificing quality.

A Team of Specialists

Here's how a multi-agent system typically works:

Step 1: Assign Roles
Each agent gets a specific job title and instructions. One might be the "Researcher," another the "Writer," and another the "Editor." Clear roles mean no confusion.

Step 2: Give Each Agent the Right Tools
Researchers get access to search. Writers get access to a document editor. Editors get nothing but a critical eye. Each agent only gets what it needs.

Step 3: Set Up Communication
The agents need to share their work with each other. The Researcher shares findings with the Writer. The Writer shares a draft with the Editor. This is usually handled by a main "orchestrator" agent that directs the flow.

Step 4: The Orchestrator Pulls It Together
A supervisor agent watches the whole process, decides which agent goes next, and makes sure the final output is complete. Think of it like a movie director — the actors all do their parts, but the director makes sure the scene works.

Multi-Agent Code Review

Here's a simple example of a multi-agent setup for reviewing code. One agent reads the code, another checks for bugs, and a third rewrites it with fixes applied.

# Three agents: Reader, Reviewer, and Fixer

def multi_agent_review(code):
    # Agent 1: Read and summarize the code
    reader = {
        "role": "reader",
        "task": "Understand what this code does"
    }
    summary = ask_agent(reader, code)

    # Agent 2: Review the code for bugs
    reviewer = {
        "role": "reviewer",
        "task": "Find bugs and problems"
    }
    bugs = ask_agent(reviewer, code)

    # Agent 3: Fix the bugs found
    fixer = {
        "role": "fixer",
        "task": "Rewrite the code without bugs"
    }
    fixed_code = ask_agent(fixer, code + "\n\nBugs found:" + bugs)

    return {"summary": summary, "fixed": fixed_code}

result = multi_agent_review(user_code)
print(result["summary"])
print(result["fixed"])

Notice how each agent does one thing well. The Reader focuses on understanding. The Reviewer focuses on finding flaws. The Fixer focuses on solutions. They pass their results to the next agent, and the final output is much better than what any single agent would produce alone.

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