Your AI Agent Looks Healthy — But Your API Bill Says Otherwise

You wake up to a $200 API bill. Your agent ran all night. It looked healthy — heartbeat green, no errors, process running. But token usage went from 200/min to 40,000/min because it was stuck re-parsing a malformed response in a loop. This is the most expensive failure mode in AI agent operations, and traditional monitoring won't catch it. Why cost tracking matters for AI agents Traditional services have relatively predictable costs. A web server handles N requests per second, each costing roughly the same in compute. AI agents are different. A single LLM call can cost anywhere from $0.001 to $2.00 depending on the model, context size, and output length. A logic loop that retries the same failing operation can burn through hundreds of dollars in minutes. The key insight: for LLM-backed agents, cost is a health metric, not just a billing metric. The pattern: cost per heartbeat cycle Instead of tracking total spend, track cost per work cycle : while True : start_tokens = get_token_count