MCP Dev Summit Seoul 2026

Giving an AI agent access to a Kubernetes cluster sounds powerful — until it runs the wrong command. The real challenge is not connecting an LLM to a cluster, but constraining what it can do, enforcing correctness, and keeping a human meaningfully in the loop.
This session presents a blueprint for safe, auditable MCP-powered operations through a real use case: an AI-assisted failover system for Kubernetes. Instead of letting an agent call cluster APIs directly, we place a Failover MCP Server in front of the cluster and expose only discrete, permission-scoped tools for observation and controlled action.
Beneath that reasoning layer, a Kubernetes Operator serves as the enforcement point. CRDs such as FailoverPolicy and DisasterRecoveryPlan encode failover semantics, validate requested actions, and ensure execution remains deterministic and policy-compliant. Sensitive actions are held behind a requireApproval setting in the Agent manifest until an operator explicitly approves the next step.
The talk closes with lessons on where LLM reasoning helps during incidents and why Operator-controlled execution still matters in high-risk workflows.