── ── AI & agents

Agent-Ready Product Data

An AI shopping agent does not respond to photography, mood or persuasive copy. It reads structured attributes, unambiguous variants, and machine-readable price, stock, shipping and returns. What it cannot parse, it skips; what it parses wrongly, it buys wrongly — and the merchant pays for the return and the complaint.

How it works

Output: SKU field audit + measured agent funnel + repair list

1. Field audit. For every SKU: title, brand, GTIN/MPN, category, material, dimensions, compatibility, intended user. Check against the target channel's published feed specification and schema.org Product. Gate: required-field missing rate is zero → otherwise fill fields first. 2. Disambiguate variants. Every color / size / bundle is its own identifiable variant with its own ID, price, stock status and image; attribute names and values are standardized, with units. Gate: any single variant can be located uniquely → otherwise split it. 3. Machine-readable transaction data. Price (with currency and promotion window), availability, shipping cost and time, and return policy are expressed in the feed and in structured data (Offer, OfferShippingDetails, MerchantReturnPolicy), and match the page. Gate: feed, schema and page agree → otherwise regenerate all three from one source of truth. 4. Policy and trust pages. Returns, warranty, privacy, terms and contact pages are public, crawlable, explicit, and need no login. 5. Cut checkout friction. Allow guest checkout and standard payment methods; remove forced registration and unnecessary verification; evaluate the agent checkout path the channel or protocol offers, and treat it as replaceable — channel checkout models change. Gate: never bypass fraud controls or payment security for speed. 6. Test with real agents. Use 3+ shopping agents or assistants, prompted the way buyers prompt, through the full flow. For each SKU record: Seen? Understood (attributes and price correct)? Bought? Gate: ≥3 runs per layer → otherwise no conclusion. 7. Repair and re-test. Fix the layer with the largest loss first; re-test in 4 weeks. 8. Stop-rule: data is complete and the product is still not Seen → the problem is no longer catalog data; move to brand and third-party evidence work (geo-generative-engine-optimization).

Output template: SKU · missing fields · variant issues · price/stock/shipping/returns consistent? · Seen rate · Understood rate · Bought rate · blocker · fix · re-test date

When to use it

• user says 'make our products buyable by AI shopping agents', 'is our product data agent-ready?', 'agentic commerce', 'the agent failed to place the order', asks 'why doesn't ChatGPT / a shopping agent recommend or buy our products?', is preparing a product feed for an AI assistant or an agent checkout protocol, or wants a catalog that AI agents can read, compare and purchase from

Worked example

ChatGPT Shopping and the Agentic Commerce Protocol (2025–2026)

This is not a single merchant's story. It is the public record of one large AI shopping channel — what OpenAI said its agent reads, what it requires from merchants, and how its checkout layer changed within six months. Walking that record through the Process shows which parts of "agent-ready" are durable and which are volatile. Every fact below comes from OpenAI's own announcements and its published feed specification.

Related mental models