1. Attribute traffic
Give each project or workload a distinct governance key. Shared provider credentials alone cannot explain which agent created the spend.
Practical guide
How to control AI agent LLM spend
The goal is not simply a lower bill. It is attributable model execution with enforceable limits and evidence of what happened.
2. Observe before enforcing
Collect request, token, cache, latency, retry, model, and route telemetry without mutating live traffic.
3. Find amplification
Look for retry loops, long outputs, repeated stable context, background tasks, and expensive models used outside policy.
4. Set budgets and policy
Apply output ceilings, model and tool rules, and project policy at a shared proxy instead of duplicating controls in every agent.
5. Require receipts
Capture the applied policy version, output budget, mode, mutations, and hashes with every response.
6. Compare and expand
Measure governed traffic against its direct baseline, then expand only when cost, latency, and operational evidence support it.