As AI agents become part of production platforms, their interaction with Kubernetes is increasingly ad hoc. Each agent embeds its own assumptions, permissions, and execution logic, making behaviour difficult to reason about, audit, or operate safely at scale.

KMCP introduces a missing control plane interface, a standardized contract for how agents exchange intent, context, and actions with Kubernetes systems.

This session explores how KMCP can be used as an infrastructure primitive rather than an AI feature. We’ll walk through practical platform scenarios where KMCP sits between agent logic and Kubernetes APIs, defining clear boundaries for execution, validation, and observability.

In this session, you’ll see how KMCP enables platform teams to :

Standardize agent-to-cluster interactions across tools, controllers, and workflows

Separate agent intent from execution logic, reducing tight coupling with Kubernetes internals

Enforce permission and policy boundaries before actions reach the control plane

Make agent actions observable and auditable using existing cloud-native telemetry

Create interoperable agent ecosystems aligned with CNCF principles

The result is a shift from opaque, agent-driven automation to predictable, contract-based operations. By treating KMCP as a control plane interface, platform teams gain the same guarantees they expect from APIs, controllers, and CRDs - applied to AI-driven systems.

This session is intended for platform engineers, Kubernetes practitioners, and architects designing the next generation of AI-native infrastructure.