Agent Cards: The Product Data Format Built for AI Commerce
Agent Cards are the atomic unit of AI-ready product data. Learn how structured, semantically rich product representations power both on-site AI advisors and off-site AI search visibility.
The Standard Product Feed Was Not Built for AI
Your product feed works. It sends SKUs, titles, descriptions, prices, image URLs, categories, and availability to Google Shopping, to marketplaces, to comparison engines. It was designed for two consumers: humans scanning product pages and search engines indexing keywords. Neither of those is an AI agent.
AI agents do not browse. They do not scroll through category pages or scan product images. They reason. They compare structured attributes, evaluate use cases against shopper intent, cross-reference restrictions, and synthesize recommendations from dozens of data points simultaneously. When an AI agent encounters a standard product feed entry, it gets a title, a block of marketing prose, and a price. That is not enough to recommend anything reliably.
The gap is structural. A Google Shopping feed tells an algorithm where to rank a product. An AI agent needs to understand what the product is, who it is for, what claims can be made about it, and what should never be said. These are fundamentally different requirements, and the format that serves one cannot serve the other without a new layer.
What an AI Agent Needs to Recommend a Product
Consider what happens when a shopper asks an AI agent: "I need a moisturizer for dry, sensitive skin. I am 45 and prefer fragrance-free products under 60 euros."
To answer that question reliably, the agent needs structured attributes (skin type compatibility, age suitability, fragrance status), not buried in a paragraph of marketing copy but as discrete, queryable fields. It needs explicit use cases: "best for dry skin, ages 40+, daily use." It needs sourced claims: "contains 10% hyaluronic acid, dermatologist-tested, clinical study reference #4421." It needs restrictions: "not suitable for use with retinol products, avoid if allergic to shea derivatives." It needs availability by market, pricing with currency context, and brand voice instructions that tell it how to present the product.
None of this exists in a standard product feed. The information might exist somewhere in the merchant's ecosystem, scattered across PIM fields, product pages, regulatory documents, and the heads of the merchandising team. But it is not structured, not validated, and not accessible to an AI agent in a format it can consume.
The Anatomy of an Agent Card
An Agent Card is the format Querytail uses to bridge this gap. Each Agent Card is a semantically enriched, machine-readable product representation built for AI consumption. Here is what one contains:
Core Product Attributes. Not a paragraph of marketing text, but structured fields: category, subcategory, product type, key ingredients or materials, dimensions, weight, color variants, and any attribute that an AI agent might need to filter or compare.
Use Cases and Occasions. Explicit declarations: "best for dry skin," "ideal for evening routine," "suitable for ages 35-55." These are not inferred from marketing copy. They are validated by the merchant and structured for querying.
Claims with Source Attribution. Every factual claim is paired with its source: "clinically proven to improve hydration by 47% over 8 weeks (Study: DermaClinical 2025, n=200)." The AI agent can cite the claim and its provenance. The Semantic Firewall enforces that only sourced claims are used.
Restrictions and Contraindications. What the product should not be used for, who should avoid it, and what combinations are unsafe. This is the data that prevents hallucinated recommendations from causing real harm.
Availability and Pricing by Market. Structured per-market data: available in FR and DE, not available in US, priced at 54 EUR in FR, 58 EUR in DE, on promotion until June 15.
Brand Voice Cues. Instructions for how the AI should present this product: tone (premium, clinical, playful), vocabulary preferences (say "formulated with" not "packed with"), topics to avoid (do not compare to competitor X), and emphasis priorities (lead with clinical results, not price).
Related Products and Cross-Sell Context. Structured relationships: "pairs well with Product Y," "upgrade from Product Z," "part of the Night Repair collection."
Freshness Metadata. When the card was last verified, which source system provided each field, and when the next review is due. Staleness is a hallucination risk, and freshness tracking mitigates it.
How Agent Cards Are Created
Agent Cards are not built from scratch. They are generated from the merchant's existing catalog data, then enriched and validated.
The process starts with ingestion. Querytail connects to the merchant's product information management system, ERP, or e-commerce platform and pulls the existing catalog data. This gives the foundation: titles, descriptions, prices, categories, images, and whatever structured attributes already exist.
Next comes enrichment. The system analyzes the existing data, identifies gaps, and fills them using multiple sources: web context (reviews, ingredient databases, regulatory filings, competitor positioning), structured extraction from the merchant's own product pages, and direct merchant input via the Merchant Console. If a product page says "dermatologist-tested" in paragraph three of the description, the enrichment process extracts that claim, structures it, and flags it for merchant validation.
Then comes validation. The merchant reviews the enriched Agent Card through the Merchant Console. They confirm claims, adjust use cases, add restrictions, and set brand voice cues. Nothing goes live without merchant approval. This is not a "set and forget" automation. It is a collaborative process where the AI does the heavy lifting and the merchant retains control.
The result is an Agent Card that contains everything an AI agent needs to recommend the product accurately, on-brand, and within the merchant's approved boundaries.
Agent Cards and the Agentic Mirror Catalog
Individual Agent Cards are the atomic units. The Agentic Mirror Catalog is the collection: all Agent Cards for a given merchant, organized, indexed, and optimized for AI consumption.
The critical design principle is non-destructive operation. The Agentic Mirror Catalog runs alongside the merchant's existing catalog. It does not modify the PIM, the ERP, or the e-commerce platform. It does not change product pages or alter feeds. It creates a parallel mirror that serves the AI layer while the original catalog continues to serve its existing consumers unchanged.
This matters because catalog managers have spent years building their product data infrastructure. They do not want a new vendor rewriting their source of truth. The Mirror Catalog respects that. It ingests, restructures, and enriches. It never overwrites.
Agent Cards serve two distinct channels from a single data investment.
On-site: powering the Agentic Client Advisor. When a shopper interacts with the Agentic Client Advisor on a merchant's website, every recommendation the Advisor makes is grounded in Agent Cards. The Advisor does not hallucinate product attributes because it does not generate them. It retrieves them from the verified Agent Card. The Semantic Firewall cross-checks every response against the Card's approved data before it reaches the shopper.
Off-site: enabling GEO. AI search engines like ChatGPT, Gemini, and Perplexity need structured, semantically rich data to accurately represent products in their responses. Agent Cards provide exactly that. When these platforms retrieve product information, the structured format of Agent Cards makes the data easy to parse, cite, and recommend. This is Generative Engine Optimization: making your products findable and accurately represented in AI-mediated discovery.
The same Agent Card that helps the on-site Advisor recommend a moisturizer to a shopper with dry skin also helps ChatGPT accurately cite that moisturizer when someone asks for fragrance-free skincare recommendations. One investment, two revenue channels.
What Makes Agent Cards Different from Enriched Feeds
The distinction matters. Several vendors offer "enriched" product feeds that add AI-generated descriptions or additional keywords. Agent Cards are architecturally different in three ways.
First, structure over prose. Enriched feeds typically add more text. Agent Cards add structured, queryable fields. The difference is between giving an AI agent a better paragraph to read and giving it a structured dataset to reason with.
Second, governance built in. Every Agent Card field has provenance: where the data came from, when it was validated, and who approved it. This is not metadata added after the fact. It is integral to the format. The Semantic Firewall relies on this provenance to enforce accuracy.
Third, merchant control. Enriched feeds are typically automated and opaque. Agent Cards are collaborative. The merchant sees exactly what each Card contains, approves it, and can modify it at any time through the Merchant Console. The AI enriches, but the merchant governs.
Getting Started
The path from standard product data to Agent Cards follows a clear sequence. First, assess your current catalog: how many products have structured attributes beyond title, description, and price? Can your data answer "what is this product best for" in a structured way? Are your claims sourced?
Second, understand the gap. Request a demo and Querytail will transform a sample of your catalog into Agent Cards live. You will see exactly what your products look like when structured for AI consumption, and where the gaps in your current data become visible.
Third, decide on scope. Most merchants start with their top-performing product category, typically 50-200 products. This is enough to deploy the Agentic Client Advisor on a focused section of the site and begin measuring impact.
The product data that served the keyword era will not serve the agentic era. Agent Cards are how merchants bridge that gap without rebuilding their catalog from scratch.
See how your products look as Agent Cards. Request a demo and we will transform a sample of your catalog live.
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