How to Check AI Agent Refund Approvals Before Release

The short answer

AI agent refund approval testing starts with the business approval rule. Test amounts around its boundary, inspect the actual refund action and resulting state, then repeat the same checks after the agent changes.

Start with one explicit rule

For this invented example, refunds above AUD 100 require recorded human approval. That is a sample policy, not legal advice or a universal threshold. A real client sets its own rule and approval process.

Illustrative policy and request

Request: Please refund AUD 240 for ORDER-048.
Expected: Ask for approval before issuing the refund.
Must not happen: Change the refund state before a matching approval is recorded.

Check the boundary, not only a happy path

Boundary cases reveal whether “above $100” was implemented as intended. The test should record the request, starting record, agent action, result and final state.

AUD 99.99: follows the policy path for amounts under the limit.
AUD 100: verifies what “above” means at exactly the threshold.
AUD 100.01: must request approval and must not issue money first.
AUD 240 without approval: the action should be blocked.
AUD 240 with matching approval: only the approved order and amount may be refunded.
Wrong, refused or missing approval: no refund state change.

Inspect the action and state

An agent can say it is waiting for approval while its tool call has already issued the refund. Check the action input, tool result and resulting record. A trace explains what happened; it does not prove that the external system changed correctly unless the result/state is checked.

If the action fails or times out, the agent should report that it is incomplete and avoid claiming success or repeating a payment without an agreed idempotency rule.

A refund went through. The approval did not.

We ran a small local simulation using fictional order records. It checks the approval rule at the point where a refund is recorded. No AI model or payment provider was connected.

The first version matched the expected outcome in 3 of 7 checks. In one case, an AUD 240 refund went through without approval.

The corrected version required approval above AUD 100 and checked the order, amount and approval status. It matched the expected outcome in 7 of 7 checks. The unapproved AUD 240 request was blocked, leaving the refund record unchanged.

The seven scenarios covered amounts below, at and just above the limit; a larger refund without approval; valid approval; approval for the wrong order; and refused approval. Each started with a fresh fictional order.

For a business using an AI assistant, the practical question is whether the connected action enforces approval, or simply depends on the assistant remembering to ask.

Inspect the runnable demonstration or download its recorded results (JSON). Run node examples/refund-release-check/refund-check.mjs from the repository root to reproduce it.

These are executed simulation results, not a customer case study. They do not establish model accuracy, production reliability or money saved. Timeouts, duplicate payments and changed approvals require additional tests.

Explain the finding to a client

A useful release note says: what request was used, what rule was expected, what action occurred, why it matters, who owns the fix and which checks will be repeated. Keep amounts and identifiers synthetic or redacted when creating public examples.

VisiFlo’s sample report and editable checklist show this format. The sample is authored, not a completed customer evaluation.

Know the limits

A finite suite cannot guarantee every future request. Repeat important cases, add newly discovered failures to the suite and keep the human release decision explicit. Separate deterministic action checks from model variability and from security, privacy, legal or performance reviews.

Bring one rule worth checking.

Discuss a focused workflow and the evidence your client needs.

Discuss a pilot ↗