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Table of Contents
- What is agentic commerce and how does it work?
- Why does product data quality determine agentic commerce outcomes?
- What data requirements do AI agents have that traditional search does not?
- How does taxonomy structure affect agent-mediated product discovery?
- What does agentic commerce readiness look like in practice?
- Q&A
Key Takeaways
Agentic commerce, in which AI agents execute product discovery, comparison, and purchase decisions on behalf of consumers, is not a future scenario. It is a present reality, and it is accelerating. The products that AI agents recommend are those whose data is most complete, most structured, and most consistently formatted. Brands and distributors that invest in product data infrastructure now are building the foundation for agentic commerce readiness. Those that do not are building a discoverability gap that will compound as agent-mediated commerce grows.
What Is Agentic Commerce and How Does It Work?
Agentic commerce is the emerging model in which AI agents, operating on behalf of consumers, procurement managers, or automated purchasing systems, conduct product discovery, comparison, and in some cases purchase execution autonomously. Rather than a human buyer navigating a catalog, entering search queries, and evaluating results, an AI agent receives a task (“find the best available option for X within these parameters”), queries multiple data sources, evaluates the results against the specified criteria, and returns a recommendation, or executes a purchase directly.
Deloitte’s analysis of retail and agentic commerce identifies this as a structural shift in how products are discovered and purchased, not an incremental improvement to existing search. The agent does not browse. It queries structured data sources, evaluates attribute completeness and consistency, and recommends the products whose data most reliably answers the question it has been asked. Products that cannot be evaluated, because their data is incomplete, inconsistently structured, or not accessible in a machine-readable format, are not recommended.
Why Does Product Data Quality Determine Agentic Commerce Outcomes?
Product data quality determines agentic commerce outcomes because AI agents make recommendations based on the data they can access and evaluate, not on the data that exists somewhere in a catalog but cannot be parsed. An agent asked to find a stainless steel mixing bowl with a capacity of at least 5 litres, dishwasher-safe, available for next-day delivery, and priced under $50 will evaluate every product in its accessible data set against those five criteria. A product that meets all five criteria but has its capacity stored as “large” rather than “5L,” its material stored in an unstructured description field rather than a structured attribute, and its delivery availability not represented in its schema markup will not be recommended — not because it is the wrong product, but because the agent cannot confirm that it meets the criteria.
This is the commercial consequence of attribute sparsity, uncontrolled vocabulary, and absent schema markup in an agentic commerce environment. It is not a marginal discoverability issue. It is a binary outcome: the product is either in the recommendation set or it is not.
What Data Requirements Do AI Agents Have That Traditional Search Does Not?
AI agents have three data requirements that traditional keyword search does not impose with the same strictness. First, attribute completeness: an agent evaluating products against a set of criteria requires that every relevant attribute be present and populated for every product in the evaluation set. A missing attribute is not a partial match; it is an exclusion. Second, semantic consistency: an agent must be able to compare attribute values across products in the evaluation set. This requires that the same attribute be stored in the same format, using the same controlled vocabulary, across all products. An agent cannot reliably compare “stainless steel,” “SS,” “304 stainless,” and “stainless” as equivalent values unless the data has been normalized to a single representation. Third, machine-readable structure: the product data must be accessible to the agent in a structured, parse able format, either through schema markup on the product page, through a structured data feed, or through an API that returns normalized attribute data.
Traditional keyword search can partially compensate for missing or inconsistent data through fuzzy matching, synonym expansion, and relevance scoring. An AI agent operating on structured criteria cannot make the same compensations. It requires the data to be right.
How Does Taxonomy Structure Affect Agent-Mediated Product Discovery?
Taxonomy structure affects agent-mediated product discovery by determining the precision with which an agent can scope its evaluation set. An agent asked to find products in a specific category, “industrial cleaning chemicals,” “replacement HVAC filters,” “food-grade lubricants” uses the taxonomy hierarchy to identify the relevant product nodes and retrieve the products classified within them. When the taxonomy is shallow or inconsistently applied, the agent either retrieves too broad a set (because the category node is too high in the hierarchy) or misses relevant products (because they are classified in the wrong node).
A four-level taxonomy hierarchy: Department, Category, Subcategory, Product Type, provides the structural precision that agent-mediated discovery requires. At the Product Type level, the agent can scope its evaluation set to the specific product type that matches the buyer’s intent, retrieve only the products classified within that node, and evaluate them against the specified criteria using the attributes assigned at that level of the hierarchy. This is the structural foundation of agentic commerce readiness.
What Does Agentic Commerce Readiness Look Like in Practice?
Agentic commerce readiness is not a single certification or a one-time project. It is a state of data infrastructure that enables AI agents to discover, evaluate, and recommend your products reliably. It has five components. First, a well-governed taxonomy hierarchy with at least four levels of depth and Product Type nodes granular enough to reflect distinct buyer intent signals. Second, a complete, normalized attribute framework with controlled vocabulary, consistent data types, and mandatory attribute coverage for all products at each taxonomy level. Third, complete schema markup implementation: Product, Offer, AggregateRating, and BreadcrumbList at minimum, on all product pages, generated programmatically from the structured catalog data. Fourth, structured data feeds that deliver normalized, attribute-complete product data to the retail media networks, data pools, and API endpoints that AI agents query. Fifth, a governance framework that maintains data quality as the catalog grows, because agentic commerce readiness is not a state you achieve once; it is a standard you maintain continuously.
Q&A
- How do we know if our products are currently being surfaced by AI agents? Direct measurement of AI agent-driven discovery is still an emerging capability, most analytics platforms do not yet attribute traffic or conversion to AI agent sources with the same precision as organic search or paid media. However, proxy indicators include: the volume of traffic arriving from AI-powered search surfaces (Google AI Overviews, Bing Copilot, Perplexity); the completeness of your schema markup as measured by Google’s Rich Results Test; and the completeness and consistency of your product data as measured against the attribute requirements of the retail media networks and data pools you work with.
- Is agentic commerce readiness relevant for B2B catalogs, or is it primarily a consumer commerce concern? Agentic commerce readiness is highly relevant for B2B catalogs, and in some respects more urgent. B2B procurement is already heavily automated: procurement platforms, ERP integrations, and automated reorder systems are all forms of agent-mediated purchasing that have been operating for years. The new generation of AI agents extends this automation to the discovery and evaluation phase, not just the reorder phase. For B2B distributors and manufacturers, the buyers most likely to use AI agents for product discovery are the same procurement managers and materials engineers who already use digital catalogs, and who, as the Coveo field guide documents, abandon those catalogs when the data cannot answer their questions quickly and accurately.
Ready to assess your agentic commerce readiness? Our product data specialists evaluate your taxonomy structure, attribute completeness, schema markup coverage, and data feed quality against the requirements of AI-mediated discovery. Explore our Agent Discovery Readiness services →
Isaac Wanzama is Founder and Chief Strategist at geekspeak Commerce and RetailTaxonomy.com. With over two decades of experience in ecommerce strategy and product data management, Isaac works with brands and distributors across North America to build the data infrastructure that powers discoverability, retail media performance, and omnichannel growth.
