AI Agent Runaway Spend Index
A recurring benchmark for autonomous agent cost failures: retry loops, MCP tool storms, delegated sub-agent fanout, paid data API polling, and the spend avoided by request-path controls.
April 2026 modeled incidents
| Failure mode | Uncontrolled | Controlled | Avoided |
|---|---|---|---|
| OpenAI retry loop | $7,200 | $250 | 96.5% |
| MCP browser automation loop | $1,840 | $120 | 93.5% |
| Sub-agent research fanout | $18,480 | $900 | 95.1% |
| Paid data API polling loop | $9,600 | $600 | 93.8% |
| Multi-tenant agent swarm | $134,400 | $6,000 | 95.5% |
Runaway spend index FAQ
What is the AI Agent Runaway Spend Index?
A recurring benchmark of modeled autonomous agent cost failures, including retry loops, MCP tool storms, delegated fanout, paid API polling, and avoided spend from request-path controls.
Why do runaway AI agents create cost risk?
Agents can loop, retry, delegate, and call paid tools or APIs much faster than humans. Without request-path budgets and kill switches, small mistakes can become expensive incidents before dashboards report the damage.
How does SatGate reduce runaway agent spend?
SatGate enforces per-request budgets, MCP tool cost policy, revocable capabilities, delegation caps, audit requirements, and kill switches before upstream calls execute.
Use the index as a control-plan checklist
The pattern is consistent: agent spend incidents are not solved by better dashboards. They are solved by request-path budget enforcement, MCP tool cost policy, revocable capabilities, delegation caps, and kill switches before upstream calls execute.