Practical, step-by-step playbooks for controlling what your autonomous AI agents spend. Each guide includes concrete rules, code examples, and benchmarks.
5-step playbook: velocity limit → per-transaction cap → merchant allowlist → time windows → human approval. The velocity rule kills retry loops (44% of incidents) on the Nth attempt.
5-layer budget model: daily total, per-transaction cap (10-25% of daily), velocity limit, category limits, approval threshold. Example procurement agent budget table included.
Two levers: pay less per call (right-size models, cache, compress) and make fewer calls (kill retry loops, block unnecessary spend). Combined: 65-90% typical cost reduction.
Approval workflows are how agents stay fast without spending unreviewed. Here's the setup in four steps.
A spend policy is a small document that maps to real rules. Five steps, one afternoon.
One config block gives any MCP client a spending policy. Here's the integration in three steps.
MCP servers are dependencies with tool access. Vetting them is the security review your agents deserve.
A flagged transaction is the firewall asking a question. Here's how to answer it well.
Cutting the AI API bill is rate shopping plus behavior change. The rate is one lever; volume, context, and retries are the bigger ones.
A runaway agent is spend compounding at machine speed. Here's how to stop the bleeding now, contain it, and prevent the next one.
Tracking AI spend means attribution: which agent, which merchant, which category — and a cadence that turns the log into decisions.
Rate cards are the start, not the decision. Providers matter on quality, latency, and how easy they make governance.