Runaway LLM-agent spend — per-agent budgets, alerts, auto-stop

Executive Summary

Long-running autonomous LLM agents are creating unpredictable, persistent API and infrastructure costs for engineering and AI teams, especially at companies that run production workflows or experimentation platforms; Finance, DevOps, and AI platform teams commonly report surprise invoices and difficulty attributing spend to individual agents or projects. This problem is acute for teams that use multiple LLM providers and orchestration layers, where a single runaway agent can burn thousands of dollars in hours and a single misconfiguration compounds cost across providers. You could build a developer-focused FinOps product that assigns per-agent budgets, issues tiered alerts, and enforces safe auto-stop policies, with cross-provider attribution and a lightweight SDK/runtime hook to tag requests and measure effective token/compute usage. The timing is compelling: a $36.0B addressable market (roughly 1.8M organizations × $20K ACV), rising adoption of autonomous agents, and the extension of cloud FinOps practices to LLM spend mean buyers are actively looking for tooling that prevents surprise bills and provides governance. This can stand out by combining deterministic per-agent accounting, policy-driven auto-stop that supports “soft” and “hard” halting, and native integrations with major LLM APIs and internal observability systems to deliver measurable ROI within weeks. Real strengths are the clear pain, high potential ACV, and multi-provider attribution value; real challenges include engineering integration complexity, avoiding disruptive false positives on mission-critical agents, and competing in a medium-competition landscape where incumbents may offer partial solutions.