How to Run AI Agents on Kubernetes with Pulumi

Kubernetes has become the primary platform for hosting generative AI models, with 66% of organizations using it to manage AI inference workloads, as revealed by the CNCF's 2026 survey. The infrastructure supporting agentic AI workloads on Kubernetes requires deliberate design, differing significantly from typical stateless web services, as it involves long-lived sessions, GPU scheduling, and complex governance profiles. While Kubernetes is suitable for agent workloads due to its scheduler, autoscaling, and RBAC capabilities, provisioning involves more than merely reusing deployment templates. Pulumi is a tool that helps provision and govern Kubernetes infrastructure for AI agents, using TypeScript and Python, by creating a stack that integrates agent runtimes, secrets management, and policy enforcement. Pulumi's Neo further enhances management by proposing infrastructure changes and handling maintenance tasks. The ecosystem supporting agentic workloads includes several CNCF projects like kagent, KServe, and Kueue, which provide frameworks and tools for managing AI agents on Kubernetes.

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