Running AI agents on Kubernetes requires more deliberate infrastructure design than typical web services due to GPU scheduling, long-lived sessions, broad credential surfaces, and complex RBAC needs. The post surveys the 2026 agentic Kubernetes ecosystem (kagent, KServe, Kueue, KubeRay, NVIDIA GPU Operator) and shows how to provision a GPU-enabled EKS cluster, install kagent via Helm, manage model provider secrets with Pulumi ESC, and declare agents as versioned custom resources — all in both TypeScript and Python. It argues for general-purpose languages over templated YAML for composing, testing, and governing agent infrastructure, and explains how Pulumi Policies enforce least-privilege and cost guardrails. Pulumi Neo is positioned as an infrastructure-layer agent that proposes and reviews changes to the cluster itself, operating above workload-level agent frameworks like kagent.
