We All have been building AI Agents with MCP since it launched in 2024, but there's one thing no one is talking about - "What happens when MCP Fails? (and they fail often). MCP is just a process and it can crash, and so will the AI agent progress, it all can vanish with a crash.

Well, the new Tasks primitive in MCP (SEP-1686) helps a lot, it gives your AI agents a way to hand off long-running tools, but it doesn't solve the real problem. They don't maintain the state when the server crashes.

In this session, I'll do a live demo of a Kubernetes Auto Healing AI Agent with MCP server and walk through how to wrap MCP tool logic in workflows that survive crashes, restarts, and network failures.
I'll cover how to handle human-in-the-loop approvals inside long-running tools, how to retries and state-management, and how to observe what your MCP tools are doing in production.

I'll be breaking a running server on stage and show you the agent recovering without losing a step. Hence "The Invincible MCP Server"