Your AI Agent Spent $500 Overnight and Nobody Noticed

Friday 5 PM. You deploy a research agent that processes customer tickets. It calls GPT-4 for each one. Expected load: 200 tickets a day, about $8 in API costs. Friday 11 PM. A bug in ticket deduplication. The agent reprocesses the same tickets in a loop. Each iteration makes 4 LLM calls at $0.03 each. The loop runs 50 times per hour. Saturday 3 AM. The agent has made 12,000 LLM calls. Cost so far: $360. Nobody is watching. Monday 9 AM. OpenAI billing alert fires at the $500 threshold you set months ago. Total damage: $487. No logs showing which agent caused it, which task triggered the loop, or when it started. This is not hypothetical. Every team running AI agents in production has a version of this story. Why Standard Monitoring Doesn't Help OpenAI gives you total organization spend. Not per-agent. Not per-task. Not in real time. If you have 5 agents calling GPT-4, and one goes haywire, your OpenAI dashboard shows a line going up. Which agent? You don't know. Which task caused the sp