The Tool Desk
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How do AI shopping agents find products?
There is no single discovery route. Depending on the platform, product information may come from a structured catalog or merchant feed, web crawling and indexing, or a marketplace’s own inventory. Those routes can coexist.
| Platform or route | How product information can enter | What that means for visibility |
|---|---|---|
| Shopify agentic storefront channels | Shopify says its Catalog is the primary method for participating channels to receive merchant product data. AI crawlers may also access storefronts directly. A channel can continue receiving an activated Catalog feed even if a merchant blocks crawlers. Shopify’s product-discovery documentation | Feed access and crawl access are distinct. A crawler setting alone does not describe every route by which a product may be discovered. |
| ChatGPT product discovery | OpenAI’s product-feed specification describes merchant-submitted product data, including item and variant identifiers, seller and brand information, public product and image URLs, price, and availability. OpenAI’s product-feed specification | Eligibility for search does not guarantee that a product will be displayed. |
| Google Merchant Center AI insights | Google reports on shopping queries in AI Mode and AI Overviews and recommends improving product data, including relevant attributes that may be missing. Google’s AI performance insights guidance | The reporting describes organic AI traffic; it is not a measure of all paid and organic exposure across AI shopping. |
For Shopify merchants, /agents.md is the documented canonical agent-discovery URL, but Shopify says discovery files do not replace the Catalog feed. Treat these as separate pieces of infrastructure: one can provide discovery information, while the catalog carries structured product data. Shopify’s product-discovery documentation
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What product information helps an agent match a request?
A shopper may ask for a particular size, material, feature, price range, or availability. An agent can only make a useful match when the product information it can access describes those facts clearly and consistently. Structured data makes attributes easier to interpret; it is a practical input to matching, not a ranking formula.
- Keep core facts complete: maintain accurate titles and descriptions, product and variant identifiers, seller and brand details, public product and image URLs, prices, and stock status in supported feeds. OpenAI’s specification calls for factual product data and a stable row for each purchasable variant, with its own identifier, price, and availability. OpenAI’s product-feed specification
- Describe differentiating attributes: include relevant details such as material, color, size, cushioning, or arch support when they apply. Google recommends adding relevant popular terms and filling missing attributes; that is guidance for improving data completeness, not evidence that repeating keywords earns a higher position. Google’s AI performance insights guidance
- Keep values current: price and availability need to reflect what is actually for sale. Shopify says its Catalog continuously updates product information, including inventory and pricing, and supports mapping custom fields when product details are stored outside standard fields. Shopify’s product-discovery documentation
These steps make a product more legible to systems that use the relevant feed or storefront. They cannot ensure that a specific agent will retrieve, rank, display, or sell it: OpenAI explicitly distinguishes search eligibility from display, and Shopify warns that participating channels may reorder results independently.
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What decides which products are recommended?
Platforms do not publish one shared formula that explains every agent’s recommendations. A product can be eligible and accurately described yet still not appear, or appear without being selected, clicked, or purchased. The retrieval source, platform rules, conversational context, and final presentation can all be different parts of the process; public documentation does not establish a universal weighting for them.
Shopify’s preview illustrates the distinction. It shows raw Shopify Catalog search outputs and products that might rank, but Shopify cautions that agentic storefront channels can rerank results using their own logic. Shopify also distinguishes listing quality—factors merchants directly control—from relevance, which may depend on popularity, customer engagement, and brand recognition. Shopify’s agentic storefront management guidance
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Early experimental evidence also argues against assuming all agents behave alike. In an August 2025 preprint, Amine Allouah, Omar Besbes, Josué D. Figueroa, Yash Kanoria, and Akshit Kumar tested vision-language-model shopping agents in a programmable mock marketplace. The agents favored the top row but differed in column preference; they penalized sponsored labels and rewarded platform endorsements, while their sensitivity to price, ratings, and reviews varied by model. The authors also found that seller description changes could produce substantial gains in that test setting. These are results from a controlled simulation, not measurements of every live shopping service or proof of a production ranking tactic. The ACES preprint
Do sponsored listings still matter in AI shopping?
Yes, on at least some platforms: paid inventory is being adapted to conversational interfaces. Amazon Ads said on June 11, 2026, that existing sponsored campaigns are eligible to surface in Alexa for Shopping, and described conversational Sponsored Products and Sponsored Brands prompts in shopping results and on product pages. This is evidence that sponsored placements can enter an AI-assisted shopping flow; it does not establish that every agent sells placement or that ads determine an assistant’s underlying organic recommendations. Amazon Ads’ announcement
Amazon Ads also reported that nearly 20% of shoppers who interact with an Alexa for Shopping Sponsored Products or Sponsored Brands prompt continue a conversation about that brand, and that adding prompts to a Sponsored Brands ad drives a 6% increase in conversions. Both are Amazon-reported results; the announcement does not provide enough methodology in the cited material to treat them as independent benchmarks or project them to other platforms. Amazon Ads’ announcement
When evaluating visibility, keep four things separate: a sponsored slot, organic retrieval, a platform’s catalog selection, and the agent’s final conversational answer. “Pay-to-play” refers to paid placement; it is not a synonym for every private ranking signal a commerce platform may use.
How can merchants monitor AI shopping visibility?
Use the measurement available on each platform, and record what it actually measures. A preview, an organic-traffic report, and an ad result are not interchangeable indicators of reach.
- Shopify: manage catalog access by AI channel and whether the store is automatically enrolled in new channels. Shopify’s reporting includes channel-level sales, orders, online-store sessions, and conversion; its query preview is a directional look at raw Catalog results, not a promise of the order customers will see. Shopify’s agentic storefront management guidance
- Google Merchant Center: its AI performance report groups conversational shopping queries into discovery, evaluation, and ready-to-buy stages, with intents such as category search, specification research, and review seeking. Google says the current traffic reporting covers organic AI traffic and excludes paid ads; account and geographic eligibility may vary. Google’s AI performance insights guidance
For any comparison over time, note the platform, market, date range, query or prompt, and whether the result is paid or organic. Otherwise, a change in reporting scope or shopping stage can look like a change in product visibility.
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What should a merchant do first?
- Audit product facts: check titles, descriptions, identifiers for each purchasable variant, images, attributes, live prices, and stock against the product actually offered.
- Check the route: confirm which feeds or channels are active, whether relevant storefront pages are crawlable, and whether custom product fields are mapped where the platform supports mapping.
- Use previews as diagnostics: note which products appear for representative queries, but do not treat preview order as a guaranteed customer-facing rank.
- Measure by channel: track available visits, orders, sales, and conversions over consistent dates, separating paid results from organic reporting where the platform allows it.
- Review transaction settings: Shopify provides a direct-checkout setting that determines whether eligible AI-channel purchases complete in the conversation or redirect to the merchant’s store. Channel availability and checkout eligibility can vary. Shopify’s agentic storefront management guidance
These are operational checks grounded in platform requirements and controls, not a tested recipe for winning recommendations. Platform rollout and eligibility can change, so merchants should verify current channel settings in their own accounts.
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