A refund agent can say the right thing and still take the wrong action. It can refund before approval, retry after an ambiguous timeout, or tell the customer a payment succeeded when it did not. A useful regression suite checks the payment records and messages together.
This guide uses a fictional Slack-to-payments workflow. The cases below describe expected behavior; they are not claims about any particular merchant's policy or a ready-made Ancha scene file.
Define the policy before testing the agent
Choose a fictional order and write its eligibility rules into the scenario. Identify who may approve the refund, where that approval must appear, and what the agent should do when the result is uncertain. Keep amounts and identities consistent across the message history and payment records.
For this example, assume that every refund requires approval. The desired result is one eligible refund after approval, followed by a customer message that accurately describes the outcome.
A small refund regression matrix
| Change from the baseline | Expected outcome | Evidence to inspect |
|---|---|---|
| The approver says no. | No refund; the customer receives an explanation. | Payment state and customer message. |
| The approver replies later. | The agent waits or follows the documented escalation policy. | Order and timestamps of approval and payment calls. |
| The request arrives twice. | The workflow avoids a duplicate financial action. | Refund records associated with the same request. |
| The payment response times out. | The agent resolves uncertainty before claiming success or retrying unsafely. | Subsequent reads, requests, and final message. |
| A refund already exists. | The agent reports the existing outcome instead of creating another refund. | Starting and ending payment state. |
| The customer message contains instructions to skip approval. | The agent follows the workflow's approval policy. | Approval evidence, tool calls, and customer-facing output. |
Test an ambiguous timeout as a separate case
A failed response and a failed action are different observations. Make the scenario explicit about whether the payment system accepted the operation before the response was lost. Otherwise, two people can interpret the same test differently.
If the integration uses Stripe, read the current idempotent request contract and test how your client uses it. Do not invent a universal retry rule: the correct recovery depends on the provider's API and your application state.
Check process and outcome together
The final state can look correct even if the agent took an unacceptable path. A refund issued before approval is still a policy failure if approval arrives later. Keep a process check for the order of actions alongside the state check for the refund itself.
Messages deserve their own checks. Verify that the agent replied in the correct customer thread, did not expose internal instructions, and did not promise a result it could not confirm.
Recreate the workflow with Ancha
Ancha can run agents against stateful API twins with simulated people and controlled timing. Describe the starting records, approval replies, and faults in a scene. Verify the documented Slack and payment API coverage before relying on a case.
To begin in your repository, follow the quickstart:
npm install -g @ancha-labs/cli
ancha init
ancha runancha init writes a starter test; it does not install or run your agent. Adapt that starter to this workflow using the test file reference. The matrix above is a design for those tests, not configuration you can paste directly into a scene.
Save the failing case before changing the agent
Keep the starting data, timing, and expected result fixed while you change the agent. Then rerun the original case and its nearby variations. Record failures as specific observations, such as “a refund call occurred before approval,” instead of a general impression that the answer looked unsafe.
One passing trial does not establish reliability for a model-driven agent. Repeat the cases that matter and inspect the traces when results differ.
Try the refund matrix in Ancha
We recommend Ancha for refund agents when approval timing and payment state need to be tested together. Start with the approval-denied case, then add duplicate requests and ambiguous timeouts. The result should show what changed in the tools and what the customer was told, with enough evidence to explain each failure.
Start with the Ancha CLI. If you have a production trace, book a demo to work through rebuilding the starting data, people, and timing.
