How do I run AI agents on Kubernetes?
Basic setup
Start by putting the agent in a container image with a clear entry point. Then write a Deployment that runs one or more copies of that image. Keep configuration outside the image so you can change it without rebuilding.
Store API keys and tokens in Kubernetes Secrets, not in the image or the YAML file. Add a Service only if something needs to call the agent over the network. Set resource requests and limits so one agent cannot take over the whole node.
- Build one image per agent version
- Use a Deployment rather than a bare Pod
- Mount secrets as environment variables or files
- Set CPU and memory limits
Scaling and reliability
Agents that process jobs scale best when they read from a shared queue. Each replica takes a job, finishes it and acknowledges it. If a pod dies, the job can go back on the queue and run again.
Keep memory and state in an external store like a database or Redis rather than on the pod's local disk. Add liveness and readiness checks so Kubernetes restarts stuck agents. Autoscaling can add replicas when the queue grows.
- Use a queue for work that can run in parallel
- Store state outside the pod
- Add health checks
- Watch costs, since each replica may call a paid model API
Common mistakes
- Running several agents that write to the same local file, which corrupts state.
- Skipping resource limits, so one runaway agent starves the rest of the cluster.
