It’s that software does what its developers told it to do. AI Agents complicate that assumption. An agent can reason about a problem, choose a course of action, call tools, write code, interact with other systems, and keep working toward a goal. That is precisely what makes agents useful. But it is also what makes running them in production different from running a conventional application.

And increasingly, enterprises want to run them on Kubernetes. A recent survey by the Cloud Native Computing Foundation (CNCF) found that two-thirds of organizations running AI inference workloads run them on Kubernetes. This makes sense, as Kubernetes has become the standard way enterprises deploy and operate distributed applications over the past decade. It already knows how to schedule workloads, allocate resources, recover from failures, and scale applications up and down.  

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Inference demand can arrive in bursts, requiring customers to rapidly scale up underlying hardware and software resources to meet surging demand. In that regard, Kubernetes is an excellent fit. Similarly, as AI workloads wind down, Kubernetes can gracefully scale down the underlying infrastructure, ensuring economical operation.

As good as the fit between AI inference and Kubernetes is, the combination still has gaps, starting with security. Recent incidents at frontier AI labs, in which AI agents escaped test environments and launched malicious cyberattacks across the internet, highlight the need for strong guardrails to keep AI workloads from misbehaving. 

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