How to Build a Multi-Agent System Where AI Agents Talk to Each Other

A single AI agent can do a lot. But some problems need more than one.

You need a researcher that gathers information. A coder that writes solutions. A reviewer that checks the work. A deployer that ships it. Each one has a different prompt, different tools, different context - and they need to collaborate without stepping on each other.

This is multi-agent architecture, and it's one of the most exciting patterns in AI right now. It's also one of the hardest to set up safely, because every agent is executing arbitrary code and you need to make sure a rogue agent can't compromise the others.

This guide covers practical patterns for building multi-agent systems that actually work in production.

Why not just use one agent?

The single-agent approach hits a wall when tasks get complex:

• Context window limits. A single agent trying to research, code, test, and deploy will blow through its context window. Specialist agents can focus on one thing with full context.
• Different tools, different risks. A coding agent needs filesystem access. A web researcher needs internet access. A database agent needs database credentials. Putting all these capabilities in one agent means a vulnerability in any one of them compromises everything.
• Parallelism. One agent works sequentially. Five agents work in parallel. Research, coding, and testing can happen simultaneously.
• Reliability. If your monolithic agent crashes mid-task, you lose everything. With specialized agents, the orchestrator can retry just the failed step.

The core pattern: Orchestrator + Specialists

The most reliable multi-agent architecture looks like this:

┌─────────────────────────────────────────────┐
│              Orchestrator Agent               │
│                                               │
│  Receives tasks · Plans · Delegates · Reports │
│  Has API token to create/manage workspaces    │
└───────┬───────────┬──────────────┬───────────┘
        │           │              │
   ┌────▼───┐  ┌───▼────┐  ┌─────▼─────┐
   │Researcher│  │ Coder  │  │ Reviewer  │
   │   Agent  │  │ Agent  │  │  Agent    │
   │          │  │        │  │           │
   │ Internet │  │ Files  │  │ Read-only │
   │ access   │  │ & exec │  │ access    │
   └─────────┘  └────────┘  └───────────┘

Orchestrator - The lead agent. It receives the high-level task, breaks it down into subtasks, delegates to specialists, collects results, and assembles the final output. It has the broadest permissions but never executes untrusted code itself.

Specialists - Each specialist runs in its own isolated environment with exactly the tools it needs. The researcher can access the internet but not the filesystem. The coder can write files but can't access the database. The reviewer can read the coder's output but can't modify it.

This separation means each agent has minimum necessary privileges. A compromised researcher can't modify code. A buggy coder can't leak data to the internet.

Making agents talk to each other

The key challenge is communication. How does the orchestrator send tasks to specialists? How do specialists return results?

Option 1: Direct workspace communication

Each agent runs in its own workspace (microVM). You set up private links between them so they can communicate directly over an internal network.

The orchestrator creates a workspace for each specialist, enables the internal API, and creates a private link. Now the orchestrator can send HTTP requests directly to each specialist's workspace API - no internet, no public endpoints, no external routing.

The communication flows over a private 10.x.x.x network. Each workspace's firewall only allows connections from explicitly whitelisted sources.

Option 2: Shared workspace for handoffs

Instead of direct calls, agents share a workspace as a "drop zone." The orchestrator writes task specifications to the shared workspace. Specialists pick them up, do the work, and write results back.

This is simpler but less real-time. Good for batch workflows where agents don't need instant responses from each other.

Option 3: The orchestrator manages everything

The orchestrator doesn't just delegate - it directly executes commands on specialist workspaces using the SDK. It creates a workspace, runs a command, reads the output, then decides what to do next.

This is the simplest pattern. The specialists don't even need to be "agents" - they're just execution environments that the orchestrator uses as tools.

Practical example: Code review pipeline

Here's a concrete multi-agent pipeline that reviews pull requests:

Step 1: Orchestrator receives a webhook when a PR is opened.

Step 2: Orchestrator creates a "coder" workspace - clones the repo, checks out the PR branch.

Step 3: Orchestrator creates a "reviewer" workspace - also clones the repo, but with read-only context. The reviewer runs static analysis, looks for common issues, and generates a review.

Step 4: Orchestrator creates a "test runner" workspace - runs the test suite against the PR branch. Completely isolated - if the tests are malicious (happens with open-source PRs), they can't affect anything.

Step 5: Orchestrator collects results from all three workspaces, synthesizes them into a review comment, and posts it back to the PR.

Step 6: All specialist workspaces are destroyed. Clean slate for the next PR.

Each workspace is a Firecracker microVM that boots in ~130ms. The whole pipeline runs in seconds, not minutes. And because each workspace is hardware-isolated, a malicious PR can't compromise the review pipeline.

Security patterns for multi-agent systems

Multi-agent systems have a larger attack surface than single agents. Here's how to keep things secure:

Minimum privilege per agent

Each specialist gets exactly the capabilities it needs:

One-way private links

Private links in Oblien are directed. If workspace A can reach workspace B, that doesn't mean B can reach A. The orchestrator can call specialists, but specialists can't call the orchestrator (unless you explicitly allow it).

Ephemeral specialists

Don't reuse specialist workspaces across tasks. Create them fresh for each task and destroy them when done. This prevents state leakage between tasks and eliminates the risk of a compromised workspace persisting.

Scoped tokens

Each specialist gets a scoped token that limits what it can do. The coder's token lets it read/write files in its workspace. It can't create new workspaces, access the network configuration, or see other workspaces.

Framework-specific patterns

OpenClaw + Claude Code

Deploy OpenClaw as the orchestrator in a permanent workspace. It manages the high-level reasoning and task planning. When it needs coding done, it creates a temporary workspace with Claude Code installed, sends the task, and collects the output.

The two agents have fundamentally different strengths: OpenClaw is great at planning and decomposition; Claude Code is great at writing and editing code. Together, they're more capable than either alone.

LangChain / LangGraph agents

LangChain's agent tools map directly to workspace operations. Define custom tools that create workspaces, execute commands, read files, and destroy environments. LangGraph's state machine can model the orchestrator's decision flow.

CrewAI teams

Each CrewAI "agent" in a crew can be backed by its own workspace. The crew's task delegation maps naturally to workspace creation and management. Each crew member is hardware-isolated from the others.

When NOT to use multi-agent systems

Not every problem needs multiple agents. Avoid the complexity if:

• The task is straightforward. A single agent with the right tools can handle most coding, research, and data analysis tasks.
• You don't need isolation between steps. If all steps trust each other and share the same context, multiple agents just add overhead.
• Latency matters more than reliability. Each workspace handoff adds ~200ms. For real-time chat, stick with a single agent.
• You're not handling untrusted input. If all the data is trusted and the tasks are predictable, the security benefits of isolation are less important.

Start with one agent. Add more agents when you hit clear limits.

Getting started

Here's the simplest path to a working multi-agent system:

1. Start with the orchestrator. Create a permanent workspace, deploy your agent framework, and get it working end-to-end as a single agent.
2. Identify the bottleneck. Which tasks would benefit from isolation or parallelism? Code execution? Research? Testing?
3. Extract one specialist. Move that task to a separate workspace. Have the orchestrator create the workspace, send the task, and collect the result.
4. Add more specialists as needed. Each new specialist is just another workspace with a specific purpose and scoped permissions.
5. Connect them securely. Use private links for workspace-to-workspace communication. Destroy ephemeral specialists after each task.

The infrastructure (workspace creation, networking, isolation) is handled by Oblien. You focus on the agent logic.

Learn about workspace networking →