# Make commerce journeys reliable for AI agents.

> Available today: AI agent task tests for product discovery, price, stock, delivery and returns, plus the automatic site check. Cart and checkout journeys are coming later.

- Canonical URL: https://aiscovery.io/use-cases/ecommerce
- Markdown version: https://aiscovery.io/use-cases/ecommerce.md
- Last updated: 2026-10-21

Available today: verify product facts, prices, stock and delivery with step-by-step evidence. Agents stop before cart; cart and checkout on a staging copy are coming later.

- [Apply as an ecommerce partner](https://aiscovery.io/partners)
- [Request a demo](https://aiscovery.io/demo)

For retailers, marketplaces, DTC teams, commerce platforms and agencies.

## Test the decisions that shape a commerce journey.

Tasks cover discovery, selection, conversion and recovery without making a production payment.

- **Find:** Find products that match explicit price, feature and delivery constraints.
- **Compare:** Compare variants and specifications without mixing facts between products.
- **Verify:** Confirm current price, discount context, availability and delivery promise.
- **Select:** Choose the intended size, color or configuration and preserve the selection.
- **Stop before cart:** Reach the add-to-cart step with the intended item and price, then stop. Cart tests on an approved staging site are coming later.
- **Recover:** Handle unavailable inventory, validation errors and replacement options.

## Follow the facts from product page to safe stop gate.

The run stays linked to assertions at every consequential transition.

1. **Product match.** Visible facts and structured data identify the same item and variant.
2. **Delivery choice.** The selector remains named, stable and actionable after inventory loads.
3. **Stop point.** The intended item, variant and price are visible at the add-to-cart step, where the agent stops.

## Typical findings connect technical behavior to commerce risk.

- **Conflicting price:** Visible, structured and cart prices describe different states.
- **Ambiguous variant:** Custom controls hide selection state from the accessibility tree.
- **Moving target:** Inventory hydration shifts the delivery or cart control during action.
- **Lost selection:** A variant choice resets after the page updates, so the agent reads the wrong price.

## Measure reliable completion, not a lucky click.

A successful outcome can still carry friction that makes it fragile across agents.

- **Step screenshots:** Follow discovery, selection and the final check step by step, with masked screenshots.
- **Completion time:** See how long each page, search and filter step took.
- **Layout shifts:** Measure layout shifts on each page the agent visits.
- **Result consistency:** Repeat each key task up to three times per agent.

## Production payment remains outside the public audit.

AI agents stop before cart, checkout and payment on your live site. Full checkout, refund or order tests on an approved staging site are coming later.

## Technical signals are inventoried without becoming arbitrary requirements.

The audit checks visible and machine-readable commerce facts, interaction semantics and optional agent-native surfaces.

- Product and Offer structured data
- Canonical URLs, sitemaps and merchant signals
- Variant, quantity, cart and delivery semantics
- Accessibility tree and keyboard interaction
- Network errors and layout shifts during task runs
- Declared WebMCP forms when present: detected, not run

## Your team gets a traceable remediation package.

- Results per key task for each agent that ran
- Step logs and masked step screenshots
- Product fact and structured-data conflicts
- Prioritized findings with acceptance criteria
- Baseline and retest comparison

## E-commerce audit questions

**Q: Do public tests place real orders?**

A: No. They stop before payment or another irreversible transaction.

**Q: Is missing WebMCP a failure?**

A: No. It is reported as capability state. Browser and search readiness are reported independently.

**Q: Do you estimate revenue impact?**

A: Only when a partner supplies suitable data. The baseline audit reports observed task and conversion friction.

## Turn this commerce journey into a repeatable test.

Use the canonical task, fixture, stop-gate and evidence contracts for product discovery, variants, delivery and cart review.

[Read the e-commerce testing guide](https://aiscovery.io/resources/test-ecommerce-ai-agents) (Practical testing guide)

## Test a commerce journey before agents meet it at scale.

Apply with one product discovery, delivery or cart workflow for a private partner review.

- [Apply as an ecommerce partner](https://aiscovery.io/partners)
- [Request a demo](https://aiscovery.io/demo)

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