Guide · 6 min read · For CS and support leads
Your AI support agent is now a financial actor. Govern it accordingly.
Once an AI agent has refund permissions, the policy layer that used to sit in a CS supervisor's head needs to live somewhere the agent can be evaluated against. Practical guide to wrapping autonomous refund decisions in policy.
Every CS team that hands an AI agent the refund button rediscovers the same fact: the implicit policy that lived in a supervisor's judgment does not survive contact with a model. The model will refund on edge cases the supervisor would have escalated, will issue goodwill credit on calls that did not require it, and will, occasionally, cascade into a small refund storm when given an ambiguous instruction at scale.
The fix is not to take the button away. The fix is to put a real policy layer between the agent and Stripe and to log every decision the agent makes against that policy.
What an agent-aware policy looks like
Identity-based rules: the AI agent is a distinct identity in the policy engine. Tighter thresholds for the agent than for a human supervisor are reasonable on day one and easy to relax later.
Volume rules: cap total refunds per agent per hour or per day. Refund storms are the most common AI failure mode and a cheap rule to write.
Reason-code rules: certain reason codes (fraudulent, duplicate) auto-approve up to a threshold; others (goodwill, requested_by_customer) always route to a human above a small amount.
Customer-lifetime rules: a customer with three refunds in the last 30 days is in a different bucket than a first-time refunder. The agent should not need to discover this itself.
What the decision ledger gives you
Every decision the agent made, with the policy version that decided it. When the agent does something you didn't expect, you can answer two questions in seconds: was the policy as written wrong, or did the agent escape policy? Both answers are actionable.
Auditors care about the same record for a different reason. SOX/ICFR controls on outbound payments require evidence of who decided what, under which control, and with whose approval. An LLM transcript is not that evidence. A signed decision receipt with the policy version is.
Where to start
Shadow mode. Connect Stripe read-only, replay the last 90 days of refund activity against a proposed agent-aware policy, see exactly what would have been blocked, escalated, or auto-approved. Move to live enforcement when the simulation matches the team's intuition. /start-free is the starting point.