The Real Stack Behind AI Agents in Production — MCP, Kubernetes, and What Nobody Tells You

Every team I talk to is building AI agents. They've got LangChain running, a vector database humming, and a demo that impresses the CEO. Then someone asks: "Cool. How do we run this for 10,000 users?" That's where things get quiet. I've spent the last year deploying agentic AI systems on Kubernetes — the kind that actually serve real traffic, not just notebook demos. And I've learned that the gap between "it works on my laptop" and "it runs in production" is enormous. This post is about the real stack behind production AI agents in 2026. Not the LLM part — everyone covers that. I'm talking about the infrastructure layer that nobody writes about but everyone desperately needs. The Problem Nobody Talks About Here's a typical conversation I have at least once a week: Engineer: "We built an AI agent that can query our database, search documents, and draft reports." Me: "How does it connect to those systems?" Engineer: "Custom Python scripts. Different ones for each tool." Me: "How do you d