Your AI Agent Is Burning Tokens While You Sleep — Here's How to Stop It

I woke up last Tuesday to a $14 OpenAI bill. For a single night. I'd left an AI agent running — a background task that was supposed to summarize some docs and file GitHub issues. Instead, it got stuck in a retry loop, burning through GPT-4 tokens for six hours straight. Sound familiar? If you're building with AI agents, autonomous workflows, or even just long-running LLM chains, unmonitored token consumption is the new forgotten while(true) loop. The Problem Nobody Talks About Everyone's excited about agentic AI. Give your agent tools, let it reason, let it act. But here's what the tutorials skip: agents make decisions, and decisions cost tokens. Every retry, every chain-of-thought step, every tool call with a fat context window — that's money evaporating. The worst part? You won't notice until the invoice hits. Most LLM dashboards update with a delay. By the time you see the spike, the damage is done. What I Changed After that $14 wake-up call, I built three guardrails into every agen