Structured Data for AI Agents — an AXD Institute resource on agentic experience design, agentic commerce, trust architecture, and human agent interaction. Founded by Tony Wood..
| Dimension | Traditional UX | Agentic Experience Design (AXD) |
|---|---|---|
| Primary material | Attention and affordance | Trust and delegation |
| User state | Present, navigating | Absent, delegating |
| Design output | Screens and interfaces | Outcomes and constraints |
| Temporal model | Session-based | Relationship-based |
| Success metric | Task completion | Trust calibration |
AI agents need structured data that supports autonomous action, not just search result display. This includes machine-readable pricing with validity periods, verifiable merchant credentials, programmatic API specifications, trust boundary declarations, and delegation contract schemas. Traditional SEO markup (title tags, meta descriptions) tells search engines what a page is about. Agent-oriented structured data tells AI systems what they can do with the information and under what constraints.
Product schema with complete Offer data is the most critical structured data for machine customers. An AI shopping agent making an autonomous purchase decision needs: exact price, currency, availability status, shipping cost, return policy, and merchant identity - all in machine-readable format. Without complete Offer schema, the agent cannot evaluate whether a purchase meets the constraints its human principal specified. Product schema is the foundation; everything else (trust credentials, API
Implement comprehensive Schema.org Product markup with every machine-critical attribute: name, description, SKU, GTIN, brand, colour, material, dimensions, weight, and condition. Embed Offer schema with real-time pricing data: price, priceCurrency, availability (InStock/OutOfStock/PreOrder), priceValidUntil, and eligibleRegion. AI agents performing Deploy Organization schema with verifiable identity attributes: legalName, taxID, duns, iso6523Code, and foundingDate. Validate all structured data with Google's Rich Results Test and Schema.org's validator, then test with at least three AI shopping agents (Perplexity Shopping, Google AI Shopping, Amazon Rufus) to verify that agents extract the data you intend. The gap between what you publish and what agents consume is the Implement hasCredential and hasCertification properties on your Organization schema to declare verifiable trust credentials - ISO certifications, industry accreditations, payment processor verifications, and regulatory compliance status. Agents performing Publish machine-readable return and refund policies using Schema.org MerchantReturnPolicy: returnPolicyCategory, merchantReturnDays, returnMethod, and returnFees. Agents operating under Declare your shipping policies using OfferShippingDetails with shippingRate, deliveryTime, and shippingDestination. Structure these as machine-parseable constraints rather than prose descriptions - an agent evaluating whether a merchant meets its principal's delivery requirements needs exact data, not 'fast shipping available'. Publish an OpenAPI 3.1 specification at a well-known endpoint (/.well-known/openapi.json) that describes your commerce API surface. Agents performing Design your API responses to include structured provenance metadata: data freshness timestamps, source system identifiers, and confidence indicators. Publish a comprehensive JSON-LD SiteNavigationElement that declares your site's information architecture - every major section, its purpose, and its