Multi-Agent Systems: When Multiple AI Work Together

One AI Does One Thing. Multi-Agent Does More.

Imagine a team where everyone has a different job — one person does research, another writes, a third double-checks the facts. That's how multi-agent AI systems work. Instead of asking a single AI to do everything, you set up multiple AI agents, and each one specializes in a specific task.

A supervisor agent acts like a team manager. It takes a big job, breaks it into smaller pieces, assigns each piece to the right specialist agent, then collects all the results and puts them together into one final answer.

Think of it like a kitchen. One cook trying to make a five-course meal would be overwhelmed. But a team where one chef handles appetizers, another handles the main course, and a third handles dessert — they work together and produce something much better.

Specialized Agents Beat a General-Purpose AI

A single AI can get tired and make mistakes when given a big, complicated job. It might lose track of what it was doing halfway through. Multi-agent systems solve this by giving each agent a clear, focused task — so each one can do its job really well.

Because agents work in parallel, things get done faster too. While one agent is looking up information, another can be writing the next section. It's like having multiple workers on an assembly line instead of just one.

This approach is also easier to fix. If one agent keeps making the same mistake, you can swap it out or fix it without rebuilding the whole system.

The Multi-Agent Workflow

Here's how a multi-agent system typically handles a task:

A Simple Multi-Agent Code Example

Here's how a multi-agent system might work in practice. A supervisor assigns tasks to a researcher agent and a writer agent. Each one does its job, then the supervisor pulls everything together:

// A simple multi-agent setup with a supervisor

import { settings } from "@langchain/core/language_models"
import { z } from "@langchain/core/output_parsers"

// Define the supervisor — it decides who does what
const supervisor = new SupervisorAgent({
  role: "Project Manager",
  goal: "Coordinate specialist agents to complete the user's request"
})

// Define specialist agents
const researcher = new Agent({ role: "Researcher", tools: [webSearch] })
const writer    = new Agent({ role: "Writer", tools: [writeFile] })

// Supervisor assigns tasks and collects results
const result = await supervisor.run([researcher, writer], {
  task: "Write a report about climate change"
})

console.log(result) // Final combined output

The supervisor delegates the research task to the Researcher agent and the writing task to the Writer agent. Each agent focuses only on its own job, and the supervisor pulls everything together at the end.

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