Free tool ยท Real policy, not a mockup
Why it is data
The long version is in how to give an agent a spending limit it cannot raise, and the use case most teams start with isrefund and payout authority, which needs no AI at all.
Questions
Real. The rule kinds, field names and effects are the ones the API accepts, so the output can be pasted straight into a sandbox workspace. If a rule kind or setting were wrong, the API would reject it on write rather than at three in the morning.
Because that is the failure people do not see coming. A per-provider cap does not sum across providers, so a runaway loop on tool calls can consume the headroom a payment was going to need. One envelope across every action type is what stops that.
In allowlist mode anything not explicitly permitted is refused. Blocklist mode is the opposite and is the friendlier default while you are still discovering what your agent does. Start on blocklist in test mode, move to allowlist before live.
It holds it. The action gets a requires-approval answer and the reservation is kept while a person decides, so the agent waits rather than failing and nothing is spent in the meantime.
At any path that does not ask. A control layer only binds the calls you route through it, so an agent holding a credential that reaches a rail directly is outside all of this. That is why an allowed decision comes back signed: make the executor demand the signature.