Your AI agent is burning money. It hasn't crashed, it's running fine, and the only way you find out is when the bill shows up.
Here's the pattern: an agent gets confused and calls the same tool over and over. Or a model upgrade quietly triples the cost per token. Or one bad execution fires a hundred times in parallel. From the SDK's point of view, every one of those is a success. Status 200, output returned, nothing to flag.
We think about cost governance as three layers, and they build on each other.
Detection
You need per-execution visibility, not a monthly total. Token count, cost in dollars, latency, per run. That's how you find out which agent burned $500 yesterday instead of which month burned $10k.
Diagnosis
An agent that costs $5 one day and $500 the next isn't random. Either the cost per call spiked (something's asking for way more tokens than usual) or the call count spiked (it's looping). Knowing which one happened tells you what to actually fix.
Enforcement
This is the part most teams skip. Once you know an agent's normal cost profile, you can pause it automatically if it breaks that pattern hard enough, or if it crosses a budget you set. That check runs before the next API call goes out, so it stops the spend rather than just reporting it after the fact. It fails open too, so a network hiccup on the check itself never breaks your agent's normal operation. And restarting it once you've found the root cause takes seconds.
Detection and diagnosis should always be on. Enforcement is opt-in, because the policy is yours to set. One team wants a hard stop. Another wants an alert and a human in the loop.
None of this works without seeing every execution, though. Sampled metrics will miss the loop that cost you a thousand dollars in five minutes.
If you're running agents in production, the cost story shouldn't be a surprise invoice weeks later. It should be a dial you can turn today.
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