AI workloads, including those using agentic AI, will only add to and change the questions Kubernetes teams have to answer. It’s no longer merely enough to ask if the pods are running. Or how long it takes a model to produce its first token. Or what happens when inference traffic spikes and no additional GPU capacity is available.

Increasingly, questions like those are becoming platform questions that sit next to the already familiar work of upgrades, recovery, and security.

That’s why The New Stack has just published Kubernetes in 2027: Building Platforms for AI, Autonomy, and Scale, a new ebook by Chris J. Preimesberger, in partnership with HPE.

This ebook starts with the work before launch — before the launch. We ask teams to stress-test model-serving systems under realistic loads, rehearse failures, and measure more than GPU utilization.

It also takes a clear-eyed look at agents. Giving an agent access to a cluster is a consequential decision, even if its task sounds routine.

The guide calls for identifiable owners, narrow permissions, approval gates, and audit trails that connect tool calls to the task that prompted them. It asks teams to evaluate AI output quality. A fast answer offers little comfort when it’s wrong.

And the ebook doesn’t treat AI as a reason to forget the rest of the platform. The checkpoints cover policy across clusters, visibility into services outside Kubernetes, upgrade and restoration drills, operational ownership, and developer self-service.

The checklist gets specific about the work that can fall between teams.

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