Running one AI agent? Cute. Running ten? Now we're talking. Running fifty agents in production with no gateway, no governance, and a Slack channel called #agents-prod that nobody reads? That's how you end up on a Monday morning call explaining to your CFO why the LLM bill went from $4K to $61K over the weekend, and why nobody noticed until accounting flagged it. I've watched this movie too many times. The plot is always the same. Someone reads about agentic AI on a Tuesday, ships a proof of concept by Friday, and a quarter later there are agents scattered across seven repos, talking to each other through MCP servers nobody documented, with API keys sitting in .env files on three engineers' laptops. Then something breaks. It's never small. Here are the five most common ways this goes sideways, and what actually fixes each one. Failure #1: The Infinite Agent Loop That Ate Your Budget You build Agent A. It's helpful. It can ask Agent B for help when stuck. Agent B is also helpful.…