Running AI Agents on Kubernetes: Architecture and Best Practices

Kubernetes offers the right primitives for production AI agent infrastructure: pod isolation for agent sandboxing, horizontal pod autoscaling for traffic spikes, namespaces for multi-tenant deployments, and robust secret management. The recommended architecture: each agent type runs as a Deployment with a minimum of 3 replicas for availability. Agent tasks queue via a message broker (Redis or Kafka) so spikes in demand do not overwhelm running pods. A sidecar container handles logging, tracing, and metrics without touching agent code. Resource limits are critical — unconstrained agents can exhaust cluster resources and cause cascading failures. Set memory limits at 2x the expected working set, CPU limits at 4x average usage to handle burst. Use Kubernetes Jobs for finite agent tasks and Deployments for persistent agent services. Monitor with Prometheus and Grafana — the key metrics are pod restart rate, job completion rate, queue depth, and P99 latency.