# How to Run AI Agents on Kubernetes with Pulumi

DevFeed: [How to Run AI Agents on Kubernetes with Pulumi](<https://devfeed.tech/articles/how-to-run-ai-agents-on-kubernetes-with-pulumi-18986.md>)

Original publisher: [Read original article](<https://www.pulumi.com/blog/ai-agents-on-kubernetes/>)

Author: Joe Duffy

Published: 2026-07-28T00:00:00Z

Content type: tutorial

Language: en

Sources: [Pulumi](<https://devfeed.tech/sources/pulumi.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Python](<https://devfeed.tech/topics/python.md>), [TypeScript](<https://devfeed.tech/topics/typescript.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [general](<https://devfeed.tech/tags/general.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [infrastructure-as-code](<https://devfeed.tech/tags/infrastructure-as-code.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [platform-engineering](<https://devfeed.tech/tags/platform-engineering.md>), [pulumi](<https://devfeed.tech/tags/pulumi.md>), [pulumi-neo](<https://devfeed.tech/tags/pulumi-neo.md>), [python](<https://devfeed.tech/tags/python.md>), [typescript](<https://devfeed.tech/tags/typescript.md>)

## AI overview

This tutorial examines how Kubernetes infrastructure must be designed differently for AI agents, focusing on GPU scheduling, long-lived state, secrets, external tool access, and governance. It covers the agentic-Kubernetes ecosystem and provisioning with Pulumi using TypeScript and Python, including the role of Pulumi Neo.

## Source excerpt

Kubernetes has become the default place teams run agentic AI workloads: CNCF's 2026 annual survey found that 66% of organizations hosting generative AI models use Kubernetes to manage some or all of their inference workloads.1 An entire ecosystem has grown up around that fact -- agent runtimes, model servers, GPU schedulers -- and most of it assumes the infrastructure underneath is already handled. It usually isn't. An AI agent is not a stateless web service, and provisioning for one takes more than copying a Deployment YAML and swapping the image. I spend a lot of my time these days thinking about what changes when the thing consuming your infrastructure isn't a person or a fixed pipeline, but an agent making its own calls about what to do next -- and Kubernetes is where I keep seeing that question show up first, because it's already where most teams run everything else. This post is about that gap: what changes about Kubernetes infrastructure when the workload is an agent, what the current agentic-Kubernetes ecosystem actually looks like, and how to provision and govern that infrastructure with Pulumi using TypeScript and Python -- plus where Pulumi Neo fits once the infrastructure itself has to answer to an agent. If you haven't read our take on the broader shift toward agentic infrastructure, What Is Agentic Infrastructure? is a good companion piece; this post stays specific to the Kubernetes layer. What makes agentic AI workloads different from ordinary Kubernetes workloads? An agent's resource and governance profile looks nothing like a typical web service, largely because it schedules accelerators, holds state across long sessions, and reaches out to systems a normal Deployment never touches. Dimension Typical web service Agent workload Compute CPU, predictable Often GPU/accelerator, bursty Session shape Short-lived, stateless requests Long-lived sessions, conversational state Egress Internal services, one database Model provider APIs, arbitrary tools, other agen