How do I run multiple AI agents at once?
Choose an isolation pattern
Each agent should run in its own process, container, or worker so that a crash or runaway loop in one agent does not stop the others. Containers are a common choice because each one can have its own environment variables and resource limits. Smaller setups can use separate worker processes managed by a process manager.
Give every agent a clear name, role, and tool list. Agents that share one large set of tools tend to make more mistakes and cost more to run, because they have more options to consider at each step.
Coordinate the work
A task queue lets you add jobs, retry failed ones, and limit how many run at the same time. Redis-backed workers and managed cloud queue services are common options, and the right choice depends on your existing stack. Set a concurrency limit so your agents do not hit provider rate limits all at once.
If one agent needs another agent's output, pass that output through the queue or a shared store. Avoid letting agents call each other directly in unlimited loops, since that can be hard to debug and expensive to run.
- One process or container per agent
- A queue for jobs and retries
- A concurrency limit for each model provider
- Separate logs for each agent
- A spending cap for each agent
Common mistakes
- Running all agents in one process, so one failure takes every agent down.
- Ignoring provider rate limits until several agents start failing together.
