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What is LLM agent cost control?
LLM agent cost control is the practice of capping how much money a single AI agent run can spend. Unlike monthly provider spend caps, which are account-wide and fire after the fact, per-run control stops an individual agent in real time once it crosses a cost or loop limit.
Autonomous agents make many chained LLM calls, so cost is driven by runtime behavior, not a fixed price. Effective cost control happens per run and in-process.
Levels of control
| Mechanism | When it fires |
|---|---|
| Provider monthly cap | After the spend, account-wide |
| Observability alert | After the spend, on a dashboard |
| Per-run brake (AgentBrake) | Before the next call, in real time |
AgentBrake enforces a live cost ceiling from real token usage and stops the run the moment it's crossed.
Related
Stop your next runaway agent
One line of code. Open source. Works on LangChain 1.x, CrewAI 1.x, and the classic AgentExecutor.
$
Star on GitHub
pip install agentbrake-sdk
Frequently asked questions
- How is per-run cost control different from a provider spend cap?
- A provider cap is monthly and account-wide and fires after the damage. Per-run control stops this specific agent the moment it crosses a limit, before the next call.
- Can you cap cost inside the agent process?
- Yes. AgentBrake tracks token spend live and raises a stop the instant a run exceeds your cost ceiling — no proxy required.