Why AI agents burn through budget overnight

Anatomy of the failure

Runaway cost is the failure mode that turns the appealing economics of pay-per-token agents into a liability. There are two distinct mechanisms. The first is the runaway loop: the agent tries an action, it fails, the agent retries, it fails again, it tries a variation, that fails, and without a hard iteration ceiling the loop can consume an enormous amount of budget — sometimes a month's worth in a few hours, frequently overnight or over a weekend when no one's watching. The second is silent token bloat: the agent's context window grows as observations, tool outputs, and reasoning accumulate, and since every token is paid for on every model call, a context that quietly grows from 5,000 to 50,000 tokens multiplies the per-call cost 10x while the work being done looks the same. Both are invisible until the bill arrives, because dashboards rarely surface per-run cost in real time. The prevention is multiple overlapping limits: bounded retries with backoff, a hard maximum iteration count per run, per-run token caps, aggregate daily/per-task budgets that stop the agent when hit, and cost telemetry that alerts on spikes. The teams that get burned are the ones who shipped an agent without these controls because it worked fine in testing — testing rarely triggers the stuck-loop or context-bloat conditions that only emerge at production scale and volume. The economics of agents only work with the cost architecture in place.