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.
Deep dives
- Schema.org structured data for AI agents: complete guide
- GEO optimization: appear in AI-generated product recommendations
- llms.txt: guide for e-commerce merchants
- Shopify catalog optimized for UCP: merchant guide
- Product data mistakes that block agentic discovery
- GEO, LLMO, AEO: the new AI visibility disciplines