How to Structure Data for Machine Customers

Adopt schema.org Product vocabulary as your foundation and extend it with category-specific attributes - machine customers expect standardised fields that map to their evaluation criteria.

Define a strict attribute taxonomy for your product category: mandatory fields (name, SKU, price, availability), recommended fields (dimensions, materials, certifications), and optional fields (sustainability data, origin information).

Publish all quantitative attributes in standardised units with explicit unit declarations - '500g' is parseable, 'about half a kilo' is not. Agents require precision, not approximation.

Implement intent-aligned attribute groupings that map to the decision criteria agents use: performance attributes, compatibility attributes, trust attributes, and cost attributes.

Version your schema and publish a machine-readable changelog - agents that have cached your product data need to know when attribute definitions change to avoid misinterpretation.

Implement real-time inventory APIs with sub-second latency for stock levels, pricing changes, and availability updates - agents making purchasing decisions cannot tolerate stale data that leads to failed transactions.

Design your data feeds to support zero-click commerce workflows: agents need to query availability, confirm pricing, and reserve inventory in a single atomic transaction.

Publish event-driven feeds (webhooks, Server-Sent Events) for price changes, stock alerts, and new product launches - proactive notification is more efficient than polling for agents monitoring thousands of products.

Include data freshness metadata in every API response: timestamp of last update, cache validity period, and confidence level for estimated fields (e.g., delivery date estimates vs. confirmed dates).

Build bulk query endpoints for agents that need to evaluate your entire catalog or category - individual product queries are inefficient for comparison shopping across hundreds of options.

Implement delegation-aware authentication that verifies both the agent's identity and the human principal's authorisation - a machine customer must prove it has legitimate authority to act on behalf of a specific person.

Design tiered API access levels: anonymous discovery (catalog browsing), authenticated comparison (detailed attributes and pricing), and authorised transaction (checkout and purchase) with increasing verification requirements.

Build trust-based rate limiting that rewards well-behaved agents with higher access levels over time - agents with a history of legitimate transactions should receive priority access during high-demand periods.

Implement agent identity standards that allow you to distinguish between different agent platforms, verify their provenance, and track their behaviour patterns for trust calibration.

Publish your authentication requirements and onboarding process in machine-readable format (OpenAPI specification) so agent developers can integrate programmatically without human-to-human coordination.

Implement automated schema validation that runs on every product data update - check for required fields, valid data types, consistent units, and cross-field logical consistency before publishing.

Monitor your data quality against entity resolution standards: can agents correctly identify your products across different contexts, match them to competitor products, and resolve ambiguities in your catalog?

Build automated consistency checks between your structured data and your visual product pages - discrepancies between what humans see and what agents parse will trigger trust penalties from agent evaluation systems.

Implement trust calibration monitoring: track how agents rate your data quality over time by monitoring return rates, complaint patterns, and agent feedback signals that indicate data accuracy issues.

Establish a data freshness SLA and monitor compliance: define maximum acceptable staleness for each data type (pricing: real-time, inventory: 5 minutes, specifications: 24 hours) and alert when thresholds are breached.