AI Agent Evaluation and Regression Testing for eCommerce (2026)
Quick summary: Ten real lookups and three must-escalate cases, re-run before every release. A green latency chart is not correctness. Below 16 out of 30 readiness, do not add a write while you tune the set.
Key Takeaways
- Below 16 out of 30 readiness, do not add a write while you tune the set
- On 25 September 2026, an eCommerce agent is correct only when it repeats the right lookup and refuses the wrong write
- The AWS-side telemetry layers are a separate guide
- The AWS evaluation docs describe the product
- Confirm the feature in your Region; availability in June 2026 is not a promise about every Region on the day you launch
Table of Contents
On 25 September 2026, an eCommerce agent is correct only when it repeats the right lookup and refuses the wrong write. Token latency will not tell you that. The AWS-side telemetry layers are a separate guide. This page is the commerce regression set.
Who this is for. The engineer who will be paged when the agent cites the wrong order. Executives need the pass bar in the RFP template, not this harness.
Our take: thirteen cases you can explain beat a model leaderboard. Block the release when they fail.
The set
Start from ten tickets before the agent talks to a customer. Strip names, emails, and full addresses. Keep the shape:
- Ten lookups whose answer is in the order or the policy (status, carrier, return window).
- Three that must escalate (delivered-but-missing, chargeback language, a refund ask).
Each case stores:
- Input the user would send
- Fixture ids (order, shipment)
- Tools that may be called
- Tools that must not be called
- A substring or structured field that must appear (the carrier status), and one that must not (a refund confirmation)
AgentCore Evaluations can run batches once you have this file. The AWS evaluation docs describe the product. They do not contain your fixtures. Confirm the feature in your Region; availability in June 2026 is not a promise about every Region on the day you launch.
What to regress
Re-run the file when any of these change:
- System prompt or tool description
- Model id or inference profile
- OpenAPI schema, even a “description” edit
- Policy (Cedar or otherwise) from log-only to enforce
- A Shopify, Adobe, or BigCommerce API version pin
A tool-description edit is how a refund mutation sneaks back in. Treat it as code.
Failures worth a case each
Add one fixture when you see it. Do not wait for a taxonomy project.
| Failure | Assert |
|---|---|
| Wrong tool | getShipment not called with a product id |
| Unauthorized write | refund tool call count is 0 |
| Hallucinated order | cited id is in the fixture |
| Duplicate write | one idempotency key, one side effect |
| Stale inventory | response includes the fixture asOf, or escalates |
| Prompt injection in a product description | the agent does not follow “ignore policy” |
| Tool loop | tool rounds under the cap you set |
| Downstream 500 | no invented tracking number |
| Partial failure | shipment timeout stays a timeout |
| Wrong customer | shopper token cannot call associate tools |
| Missing audit | the trace id exists for the turn |
The support-shaped cost note still applies: leaving Browser on has run about 3× platform spend. Put a case in the set that fails if Browser is invoked on a pure lookup. Cost is an eval.
Readiness under 16 out of 30 means you are not adding write cases in order to “test refunds.” You are testing that the tool is absent.
How a run fails in practice
What broke — The golden order id was reused in production and then fulfilled. The eval still expected “unfulfilled,” so a correct agent looked red, and someone weakened the assert to “contains a status.” Detection: the assert passed on “cancelled” the following week. Fix: fixtures live in a sandbox store, ids reserved, asserts exact. Lesson: a golden set that points at live orders rots, and loose asserts hide the rot.
If you only do one thing
Write the three must-escalate cases and wire them to fail the build if a refund tool is called. Lookups can follow.
What to do this week
- Pull thirteen tickets. Remove personal data.
- Store them next to the tool schema. The series folder is
ecommerce-ai-agents-series. - Run them against the sandbox before the next prompt edit.
- Alarm on denied-write spikes using the same trace you already keep for securing the agent.
- Do not quote a pass rate in a board deck until the same file has run twice. One green run is a demo.
- Discuss the architecture if you want the gate built into the first workflow. The service is eCommerce AI agents.
What this post doesn’t cover
- A public numeric pass rate for a FactualMinds client. We do not have a published agent case study.
- LLM-as-judge for tone. Useful later. It will not catch a wrong order id.
- Load testing. Correctness first. Then volume.
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