How we stopped multi-agent context leaks from bloating cloud bills in production
Every time an autonomous agent loop fails to close cleanly, your backend processing buffer absorbs the cost through duplicated context reloads and excessive API queries.
Most solo founders and lean engineering teams don't track this burn rate until the monthly cloud bill arrives. A pending agent state isn't neutral—it's an ongoing operational cost that drains your runway.
To solve this, we isolated the core failure mode and open-sourced a production-grade backend guard that halts execution buffers when loop thresholds are breached.
You can inspect the complete repository here: https://github.com/koniJang/max-token-saver-light
If you want a complete structural audit of your landing funnel friction and LLM token leaks within 24 hours, you can access our async micro-offer ($39): https://contra.com/s/Xs7WYN7c-llm-token-leak-and-checkout-friction-audit-24h-async?r=trs_research
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Useful framing. The part I’d measure first is not just token spend, but “retry depth per completed task” — it catches both prompt bloat and loops that look successful from the outside. A small kill-switch plus a run ledger usually pays for itself before you need more complicated evals.
Spot on. Tracking retry depth exposes the silent loops that standard token counters miss. A lightweight run ledger combined with a hard loop cutoff is usually all it takes to stop the bleeding before scaling up complexity.