Your product catalog may look complete to a human shopper. It may include polished lifestyle imagery, persuasive copy, product videos, comparison charts, and links to detailed policies.
That does not mean it is ready for an AI agent.
As agentic commerce develops, software agents will increasingly help shoppers discover products, compare options, validate availability, and initiate purchases. These agents are not browsing your site the way a person does. They are interpreting structured records, querying APIs, reconciling product identifiers, and looking for reliable answers to specific questions.
If key information is buried in JavaScript, hidden behind a visual configurator, or inconsistent across your PIM, ERP, storefront, and marketplace feeds, your products may be difficult for agents to understand or trust.
That spells trouble for brands competing in an environment where product discovery is increasingly mediated by AI.
The solution is not to remove the brand experience that human shoppers value. It is to build a second layer beneath that experience: machine-readable commerce data that makes your catalog clear, consistent, and actionable for software.
Machine-readable product data is information organized so that software systems can parse, compare, and use it without relying on visual interpretation or manual intervention.
This includes more than a product name and price. A useful product record should communicate:
Common standards and delivery methods include Schema.org structured data, JSON-LD, product feeds, and real-time REST or GraphQL APIs. Schema.org’s Product vocabulary provides a common way to describe attributes such as brand, SKU, GTIN, material, dimensions, variants, reviews, and offers.
This does not guarantee that an AI system will recommend a product. It does make the product easier for systems to identify and evaluate accurately.
That distinction is important. AI-ready commerce is not a ranking shortcut. It is an accessibility and reliability strategy.
Most commerce experiences were designed around a human journey:
AI agents compress and rearrange that process. An agent may need to answer a question such as:
> “Find a waterproof hiking jacket under $250, available in men’s medium, made with recycled materials, and eligible for delivery this week.”
To answer well, it needs structured and current data across several dimensions. A lifestyle image may show that a jacket is being worn outdoors, but it does not reliably communicate waterproof ratings, material composition, sizing, or delivery availability.
Likewise, a return policy buried several clicks away may be perfectly accessible to a determined shopper but difficult for an agent to retrieve and apply to a recommendation.
From a technical perspective, the challenge usually comes down to four issues:
When these conditions exist, agents may skip a product, misunderstand it, or present outdated information.
A practical product data strategy should cover five core layers.
Agents need to know whether two records describe the same product or two different products.
At a minimum, review the consistency of:
A product identifier is especially important for brands selling across multiple channels. Consistent identifiers help systems connect your product page with reviews, marketplace records, feeds, and inventory data.
Descriptions should use clear, literal language alongside brand storytelling.
For example, “Built for the next adventure” may support your brand voice, but it does not tell an agent whether a product is a 20-liter waterproof backpack with a padded laptop sleeve.
Important attributes vary by category, but may include:
Numeric specifications should include explicit units. “Capacity: 20” is ambiguous. “Capacity: 20 liters” is significantly more useful.
Price and inventory must be accurate when an agent evaluates the product, not merely accurate when a crawler last visited the page.
That means connecting machine-readable records to live or frequently synchronized systems for:
For enterprise brands, this is often where product data work becomes integration work. The storefront may not be the source of truth for price or availability. Those values may originate in an ERP, PIM, OMS, warehouse system, or regional pricing service.
A product is not truly purchasable if an agent cannot determine whether it can be shipped to the customer or returned under reasonable conditions.
Where relevant, expose:
Google’s Product structured data documentation distinguishes between product snippets and merchant listings, with merchant listings supporting details such as pricing, availability, shipping, sizing, and return policies.
These standards were developed for search, but the same principle applies to agentic commerce: the more clearly a system can understand the offer, the less it has to infer.
Agents need context, not isolated records.
A catalog should make relationships explicit, including:
This is particularly important for B2B commerce, where buyers may search for a component compatible with a specific machine, model, or purchasing program.
A common mistake is treating JSON-LD, APIs, and product feeds as interchangeable. They are related, but they solve different problems.
Structured data on the storefront helps systems interpret a product page. It can identify the product, offer, reviews, availability, and other attributes in a standard format.
Product feeds provide a larger catalog view for a particular channel or partner. They are useful when an AI platform, marketplace, or advertising system requires a defined file structure.
APIs support real-time interaction. An agent may need to check inventory, calculate a price, create a cart, validate shipping, or retrieve order status. Those actions require more than static page markup.
A mature API readiness plan should therefore ask:
The correct architecture will vary by business. A Shopify Plus brand may use native platform capabilities, structured metafields, and apps or integrations to expose additional catalog information. A Salesforce Commerce Cloud brand may need to coordinate catalog, commerce, CRM, OMS, and middleware data through APIs and event workflows.
Red Van’s Shopify partnership and Autobahn for Salesforce reflect the same broader principle: commerce performance depends on how effectively the systems behind the storefront work together.
Becoming agent-ready does not require replatforming on day one. Start with a focused audit.
Review products across different categories, price points, variants, and fulfillment rules. Include products with known data challenges rather than auditing only the cleanest records.
Document where each critical field originates:
This often exposes duplication and ownership gaps quickly.
Create a shared data model for the fields agents and other channels need. Do not begin by trying to standardize every field in the business. Prioritize information that affects discovery, comparison, trust, and purchase.
Add appropriate Product, Offer, review, shipping, and return information to product pages. Validate the output using tools such as Google’s Rich Results Test and Schema Markup Validator.
Structured data should reflect what shoppers can actually see and buy. Marking up information that is inaccurate, hidden, or no longer available creates trust and compliance problems.
Determine which prices, inventory statuses, and fulfillment promises require real-time access. Build or improve API endpoints and feeds where static markup is not sufficient.
Traditional analytics will remain useful, but agentic commerce introduces new questions:
In agentic commerce, product data becomes part of the customer experience.
Brands with clean, complete, and current data will be easier for agents to discover, compare, and transact with. Brands with fragmented or ambiguous data may still have strong products and a compelling brand identity, but they will be harder for automated systems to represent correctly.
The goal is not to replace human-oriented content. Images, storytelling, merchandising, and distinctive brand language remain essential. The goal is to give those experiences a reliable technical foundation.
For commerce and IT leaders, the most useful next step is a joint assessment of catalog quality, platform architecture, and API readiness. Red Van’s commerce services team can help identify where product data is limiting discoverability or creating unnecessary integration complexity: and prioritize improvements that support both today’s channels and tomorrow’s AI-driven ones.