Clustering AI agents: How to run distributed workloads across machines

What is clustering AI agents?

When your AI coding agents (like Claude Code or Codex) start handling multiple tasks at once, a single machine can become a bottleneck. Clustering AI agents means connecting multiple machines into a unified system where agent workloads can run across any node, migrate between machines, and continue without interruption if one machine goes down.

The idea is simple: instead of running all your agents on one laptop, you spread them across a group of servers. Each agent still behaves the same way, but the cluster handles routing, load distribution, and state persistence.

Why cluster agents instead of scaling vertically?

Vertical scaling (buying a bigger machine) has limits. Even the most powerful workstation has a finite number of CPU cores, memory slots, and GPU capacity. Clustering lets you add capacity horizontally, one machine at a time, with no architectural ceiling.

Here are the practical benefits:

• Fault tolerance: If a node fails, agents running on it can be resumed on another node. No lost state, no restarted tasks.
• Resource isolation: Heavy inference workloads run on GPU nodes, while lightweight orchestration runs on cheaper hardware.
• Geographic distribution: Run agents in different regions to reduce latency for local tools and APIs.
• Cost efficiency: Use spot instances, second-hand hardware, or cloud VPS instances without committing to a single large machine.

How a leaderless mesh cluster works

Traditional clustering often relies on a master node that coordinates everything. If the master goes down, the whole cluster stops. Hive uses a different approach: a leaderless mesh.

In a leaderless mesh, every node knows about every other node. When you add a machine, it joins the mesh and immediately becomes part of the cluster. If a node disconnects, the rest continue operating. The cluster converges on the current state without any central coordinator.

Setting up a cluster for AI agents

1. Install the agent runtime on each machine. Every node runs the same daemon and connects over a single WebSocket channel.
2. Join machines into the mesh. With Hive, you can join servers into a leaderless mesh by authenticating each node to your cluster profile.
3. Route agents to appropriate nodes. Agents can be assigned to specific hardware, or the cluster can distribute them automatically based on available resources.
4. Enable session mobility. Detach a session from one device and reattach from another, even if the agent moved to a different machine in the cluster.

Hive handles steps 2 through 4 automatically. You only need to install the server daemon via the one-liner installation script on each node.

Practical use cases for distributed agent workloads

Parallel code review

Run multiple Claude Code sessions across different parts of your codebase simultaneously. Each agent works on a separate module, and you monitor all of them from a single dashboard.

Batch processing pipelines

When you need to review hundreds of files or run automated refactoring tasks, distribute the work across nodes. Each machine processes a chunk, and results are aggregated by the orchestration layer.

Multi-environment testing

Run agents on different operating systems or hardware configurations in parallel, comparing outputs without serializing the work.

Collaborative agent farms

In a team setting, multiple developers can share a cluster. Each developer launches agents on the cluster, and the mesh ensures no two agents conflict on resources.

Managing state across nodes

The toughest problem in distributed agent workloads is state. If an agent runs for hours, accumulates context, and then you need to switch to another device or another node, the state must travel with the session.

Hive solves this with a synced notes vault that travels across every device. The vault is agent-aware, meaning it preserves conversation history, file diffs, and intermediate results. When you detach from one node and reattach from another, the vault reconstructs the agent’s context on the new node.

Licensing and cluster size

Hive licensing is straightforward. The Personal plan supports 3 devices with clustering. The Pro plan supports 10 devices. The Team plan supports unlimited devices for up to 5 users, ideal for small teams sharing infrastructure.

All plans include clustering and the synced notes vault. Pricing information is available here, with both one-time and subscription options.

Get started with your first cluster

1. Install the Hive daemon on your primary machine.
2. Install it on secondary machines (any Linux server, cloud VPS, or even a Raspberry Pi).
3. Connect your desktop or mobile app to the cluster.
4. Launch agents on any node and move between them freely.

The installation takes less than a minute per machine. Download Hive now and see how clustering AI agents simplifies your distributed workloads.

Frequently asked questions

What machines can I use in a Hive cluster?

Any machine that can run the Hive daemon, including Linux servers, cloud VPS instances, and ARM devices like Raspberry Pi. Nodes communicate over a single WebSocket connection, so they don’t need to be on the same network.

Is there a master node that can fail?

No. Hive uses a leaderless mesh, meaning every node is independent. If one node disconnects, the rest continue operating without interruption.

Can I run different agents on different nodes?

Yes. You can run Claude Code on one node, Codex on another, and a plain terminal session on a third. Hive manages them all over the same cluster profile.

How does session mobility work across machines?

Sessions live on the server, not the client. You can close the app on one device, open it on another, and reattach to the exact same session, including its full context, without losing state.

What are the licensing costs for clustering?

Clustering is included in all paid plans. The Personal plan covers 3 devices for EUR 39 one-time. The Pro plan covers 10 devices for EUR 79 one-time. The Team plan offers unlimited devices for 5 users.