Why does my agent keep spending after any sane budget would have cut it off?
Opportunity
AI agents running multi-step workflows consume tokens at 20 to 100 times the rate of a single query, and there is no infrastructure-level primitive to enforce a spending or compute ceiling at runtime. When two agents enter a recursive clarification loop or a retrieval agent over-fetches context, the only signal is the invoice at the end of the month. Frameworks like LangGraph and AutoGen each handle retries and checkpoints but none enforce a resource contract that halts execution when a declared budget is breached. A January 2026 arXiv paper formalizes what such a contract would look like, but the gap between that formalism and a deployable primitive that works across model providers and tool calls remains wide open.
Why it matters
A runtime resource contract is the missing safety layer that makes agent deployments safe to hand to non-engineers.
How I score the opportunity
The Opportunity Score is my own read, not a measurement: how much it hurts, how often it bites, and how little exists to solve it today. Higher means I think it is more worth building.
How much pain it causes when it shows up.
How often people actually run into it.
How little good tooling exists for it today.
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