An agent-ready product catalog is a catalog that an AI agent can retrieve, parse, reason about and act on — without scraping, guessing or inferring. This page defines the target shape, the minimum viable attribute set, and the validation steps that separate a feed that "works" from a catalog that interoperates.
Working definition
Definition
A product catalog is agent-ready when each SKU has: a canonical identifier, an addressable URL, typed attributes covering the category's query surface, explicit policy fields, and a state (price + availability) that is fresh within a well-defined freshness SLA.
Five properties of an agent-ready catalog
- Identifiable. Every SKU resolves to a canonical identifier (GTIN, or MPN+Brand, or an internal SKU that is stable).
- Addressable. Every SKU has a permanent URL that returns structured data without login or consent walls.
- Typed. Attributes have names, units, enumerated values — not just marketing prose.
- Policy-aware. Returns, warranty, shipping, age/geo restrictions are expressed as structured data, not only as legal text.
- Fresh. Price and availability have a documented freshness window, and feeds match that window.
Minimum viable attribute set
The exact list depends on category. The following is the baseline any agent will expect for a physical consumer good.
| Field | Example | Why agents need it |
|---|---|---|
| id | SKU-39281 | Internal reference, deduplication |
| gtin | 5012345678900 | Cross-merchant identity |
| brand | Patagonia | Filter, trust, match |
| title | Women's Torrentshell 3L Rain Jacket | Human-facing, query match |
| description | (semantic, attribute-rich) | Embedding / retrieval |
| category | Apparel > Outerwear > Rain Jackets | Taxonomy placement |
| price | 129.00 EUR | Ranking, filtering |
| price_effective_until | 2026-04-30T23:59:59Z | Promotion honesty |
| availability | in_stock / pre_order / out_of_stock | Reliability |
| stock_qty | 14 | Scarcity signal |
| shipping | {region, cost, handling_time, delivery_time} | Fit to shopper constraints |
| returns | {window, cost, method} | Pre-sale confidence |
| warranty | 2 years manufacturer | Consideration |
| attributes | {material, waterproof_rating, weight_g, …} | Query filtering |
| variants | [{size, color, gtin, stock_qty, price}] | Resolve the right SKU |
| images | [{url, role, alt}] | Visual verification |
| url | https://merchant.com/p/torrentshell | Handoff / attribution |
| locale | en-GB, fr-FR, … | Regional routing |
| updated_at | ISO 8601 timestamp | Freshness |
Anti-patterns
Patterns we see regularly that silently degrade agent performance:
- Marketing-only titles. "Ultimate Rain Protection for Real Adventurers" — unsearchable. Prefer "Women's 3L Rain Jacket — Torrentshell — Patagonia".
- Attributes inside description prose. An agent has to extract them. Type them.
- Region-sensitive PDPs without structured locale. The agent sees a French page priced in EUR for a US shopper.
- Price mismatch between feed and PDP. Agents cross-check; mismatches kill trust.
- Stock set to "available" when the back-order window is 6 weeks. Use structured availability states.
- Returns policy as a single freeform paragraph. Structure it.
The three expressions of the same catalog
An agent-ready catalog should be expressible in three parallel forms. Each form serves a different retrieval channel; all three should reconcile.
- Structured data on the PDP — JSON-LD using schema.org Product, Offer, AggregateRating, MerchantReturnPolicy, ShippingRateSettings.
- Feed — Google Merchant Center XML/CSV (or equivalent), enriched to cover policy fields.
- API / MCP tool — programmatic endpoint for agent orchestrators. Read-only endpoints for catalog, offers, inventory, policy.
The three forms must agree on identifiers and prices. Reconciliation is an operational discipline, not a one-time export.
Validation and telemetry
How you know you are agent-ready:
- Google Rich Results Test — JSON-LD parses as Product and Offer.
- Merchant Center diagnostics — no high-severity errors.
- Feed coverage — >95% of SKUs have brand + GTIN (or MPN+Brand).
- Price parity — feed and PDP match at >99% of sampled SKUs.
- Stock parity — availability states match checkout behaviour at >98%.
- Structured returns / shipping — JSON-LD present on >90% of PDPs.
- Agent crawl volume — measurable traffic from OAI, Anthropic, Perplexity, Amazon, GoogleBot agents; you track via server logs.
This set is the core of our audit methodology.
Shopify, WooCommerce, Salesforce, custom stacks
Platform-specific notes:
- Shopify — structured data is partial out-of-the-box; metafields are the leverage. Expose rich attributes via Shop / Shopify Shopping APIs as they evolve agent endpoints.
- WooCommerce — plugins like RankMath / Yoast cover schema basics; returns/shipping structured data typically requires custom snippets.
- BigCommerce / Adobe Commerce / Salesforce Commerce Cloud — agent-side APIs appearing in product roadmaps; track vendor release notes.
- Custom — the opportunity is biggest and the discipline required is highest. Invest in schema, feed and API parity from day one.
Where to go next
- See the full operator play in best practices.
- Use the readiness checklist to assess yours.
- Read catalog as API for the deeper mental model.