Dockerizing Your AI Agent Fleet

Deploying AI agents without containers often results in dependency hell, inconsistent environments, and downtime. Docker and Kubernetes offer a structured approach: each agent runs in a lightweight container with defined resources and versioned dependencies.\n\nFor fleets of agents, consider multi-arch builds (for CPU/GPU targets), layered caching for fast CI/CD, and environment variables to inject runtime configuration. Secrets should never be baked into images — use secret managers or vault integrations instead.\n\nThe outcome: faster rollouts, rollback safety, and parity between dev, staging, and production. When combined with observability, containerized agents can be deployed and scaled like any other microservice.