# Multi-agent systems

Canonical URL: https://agentlearn.dev/learn/agents/multi-agent
Author: [Hemanth HM](https://h3manth.com)
Track: agents
Reading time: 10 minutes

Multiple agents introduce coordination, not automatic correctness. Use them where specialization or independent work has a measurable benefit.

## Divide responsibilities before adding workers

A support workflow may separate policy research from order lookup. These workers should return typed artifacts to a coordinator rather than repeatedly paraphrasing each other's messages. A final coordinator needs evidence and provenance, not just a confident consensus.

## How it works

Specify who owns each task, which tools each role may access, and how conflicting results are resolved. Bound fan-out, total tokens, and elapsed time. Avoid circular delegation. Independent reviewers can still share model biases or training data; agreement is not equivalent to independent experimental evidence.

## A concrete example

Two workers disagree about return eligibility because one used an old policy. Majority voting cannot fix the missing version check. The coordinator should compare document IDs and effective dates, then resolve the evidence conflict or escalate.

## Apply it to your assistant

Add a worker with a different policy version and surface the conflict. Before running the exercise, predict the result. Afterward, explain which assumption changed and add one case where the system should refuse, ask for clarification, or escalate.

All exercise inputs and outputs are deterministic teaching examples. No language model is called. Run the same idea against a versioned dataset before making a production claim.


## Key takeaway

Coordinate typed evidence under a shared budget, and test whether extra agents improve the whole system.

## JavaScript exercise: Multi-agent systems · code experiment

Add a worker with a different policy version and surface the conflict.

```javascript
const artifacts = [
  { worker: 'policy', policyVersion: 3, eligible: true },
  { worker: 'reviewer', policyVersion: 2, eligible: true },
];
const versions = new Set(artifacts.map(a => a.policyVersion));
console.log({ evidenceConflict: versions.size > 1, votesAgree: artifacts.every(a => a.eligible) });
```

## Knowledge check

Two agents agree on an unsupported claim. What does their agreement prove?

1. The claim is true
2. The agents are independent
3. Only that these runs agreed; evidence still needs checking

Answer: Only that these runs agreed; evidence still needs checking

Correlated errors are common when systems share models, prompts, or evidence. Agreement does not establish truth.

## Sources

- [LangGraph overview](https://docs.langchain.com/oss/javascript/langgraph/overview) — LangChain, Living documentation. Graph-based orchestration, state, persistence, and long-running workflows.