As AI agents evolve from answering questions to executing actions, Kubernetes becomes a powerful—but risky—automation target.
This session demonstrates how to build a safe AI-powered Kubernetes operator using kubectl-ai. We follow the complete workflow from a natural language request to Kubernetes API calls, evidence collection, diagnosis, and controlled execution.
You'll discover the engineering patterns required for production: least-privilege RBAC, read-only access, approval before changes, command validation, audit logs, and grounding responses with real cluster data to reduce hallucinations.
A live demo will show an AI agent investigating a failing workload using pods, events, logs, and resource configuration.
Rather than replacing operators, AI becomes a secure Kubernetes copilot while humans retain control of critical actions.