Agent frameworks allocate budgets and capabilities as lump-sum grants rather than tying resource release to verifiable progress checkpoints, making it impossible to implement dynamic kill-switches or meter spend against actual work output. This creates both financial and operational risk in production deployments where agents can exhaust resources without demonstrable progress. A metered, receipt-based resource model would fundamentally change the risk profile of autonomous agent execution.

Production AI agents receive lump-sum budgets and can burn through compute, API calls, and money without demonstrable progress — creating unacceptable financial and safety risk for enterprises deploying autonomous agents at scale.

Engineering teams at companies running production agent systems (e.g., multi-step coding agents, research agents, agentic workflows) who are currently hard-capping budgets and losing value because they lack granular spend-to-progress controls.

Teams already build ad-hoc kill switches and budget caps — proving the pain exists — but these are binary and blind to progress; a protocol-level solution that ties resource release to verifiable checkpoints replaces brittle custom code with a standard, and enterprises will pay because runaway agent costs are a direct P&L hit today.

MVP is an open-source middleware SDK (Python/TS) that wraps LLM and tool calls with a checkpoint-gated escrow layer: define task DAGs with verification functions at each node, and resources (API keys, compute credits, tool access) are released tranche-by-tranche only when checkpoint assertions pass; ships with a dashboard showing spend-vs-progress curves and auto-kill triggers.

The AI agent orchestration and observability market is projected at $5-10B by 2027; checkpoint-gated resource control is a horizontal infrastructure layer that touches every production agent deployment.