I ran 53 failure scenarios before letting an AI agent spend $1. These are the 6 controls that mattered.

Disclosure: I built sipi.bot. This comes from implementing and testing the controls, not from neutral market research. Prompts can guide behavior, but they are not enforcement. If an agent can call a paid API, provision cloud resources, buy data, or move money, the spending decision needs deterministic policy outside the model. These are the six controls I would install before letting an agent spend $1: A per-transaction cap. A rolling daily or session budget. A velocity rule that stops retry storms. Merchant and category allowlists or blocks. Human approval above a defined threshold. Idempotency plus a queryable audit trail. The decision path should be simple: the agent proposes a transaction, the policy layer returns APPROVED, BLOCKED, or FLAGGED, and the payment integration honors that result. No model call should decide whether a rule was violated. The current public evaluation set passes 53 out of 53 scenarios, including boundary values, retry loops, merchant rules, categories, and time windows. That proves the rules behave as specified. It does not pretend we already have customer results. I am looking for one team already running an agent that can trigger a paid action. For the first team, I will include a founder setup review at the $99 Team price and help test the initial rules against your failure cases within 48 hours. If sipi.bot green-lights a transaction that breaks an active rule, that month is free. If this matches a problem you have now, comment with your stack and the paid action your agent can trigger. I will tell you whether it is a fit before asking you to buy.