So I just learned a very expensive lesson about AI agents.
I've been working on justcopy.ai - basically a tool where you can copy any website, customize it, and deploy it. To make this work, I built 7 AI agents that do the heavy lifting, following standard dev workflows.
What happened
Last week I started up the agents to test something, then stepped out to grab coffee. Came back maybe 2 hours later and checked my OpenRouter dashboard.
My bill had jumped $100.
First reaction: "Oh shit, are people actually using this?" Got excited for like 30 seconds thinking I had real users hammering the API.
Nope.
Added some logging and realized the agent I kicked off earlier was STILL running. Just churning through API calls. Completely autonomous. By the time I caught it, I'd burned through $200.
The fix
I had to add interrupt checks everywhere. Now before an agent makes any API call, it checks if it's been told to stop. Also added:
- Hard timeouts on everything
- Budget caps per session
- Better logging so I can see WTF is happening
Seems obvious in hindsight but yeah... autonomous doesn't mean it knows when to quit.
Anyway
If you're building stuff with AI agents and running into similar issues, hit me up. Would be happy to compare notes on what works (and what drains your bank account).
Top comments (1)
This is exactly why the "circuit breaker pattern" is so critical for autonomous systems! Your $200 lesson mirrors what we learned in distributed systems - any autonomous process needs multiple kill switches.
Beyond your fixes (which are solid), consider adding:
The scariest part? This scales non-linearly. 7 agents can easily become 70 API calls/sec if one agent spawns sub-tasks. Seen production systems go from $50/day to $5K/day in under an hour.
Your interrupt checks are the right move - treat autonomous agents like they're adversarial actors trying to drain your wallet. Because in a way, they are! 😅
Props for sharing the lesson publicly. More people need to hear this before they get their own $200 surprise.