The Agentic Commerce Readiness Checklist: 3 Buckets, 25 Checks
A practical checklist for assessing whether your catalog can be acted on by AI shopping agents. Each check maps to a specific failure mode at retrieval, filter, comparison, or transaction time.
AI shopping agents don't rank your products. They filter them. A product that fails a filter disappears from the consideration set — silently, with no error message, no ranking penalty, no suppression notice.
A buyer asks ChatGPT for "food-safe industrial degreasers." Your product matches the keyword. The hazmat flag is missing. The agent can't verify food safety and excludes it. A buyer asks Gemini for "6-inch duct fittings for residential installation." Your fitting matches. The sub-category field is blank. The agent can't place it correctly. Neither failure shows up in your analytics.
This checklist maps each failure mode to the specific check that catches it before the agent does.
How to use this checklist
Run it at the product level, not the catalog level. A catalog that's 95% ready still has 50,000 products failing if you have 1 million SKUs. The goal is to identify which specific products fail which specific checks — and route them to the right agent for remediation.
Bucket 1: Discovery
These checks determine whether an AI agent can find, identify, and place your product. Discovery failures are the most expensive — a product that can't be found or correctly categorized never makes it to the comparison step.
Brand name is canonical (not "Manufacturer", "OEM", "N/A")
Channel listing match fails; brand filter returns incomplete results
Brand Normalization AgentGlobal product identifier (GTIN/UPC/EAN) is present and valid
Channel matching breaks; duplicate listings appear
Channel Matching AgentPack size and geometry are explicit (not "assorted" or "varies")
Multipack compliance check can't run; pricing per unit is wrong
Attribute AgentModel number matches manufacturer's canonical format
Compatibility filter fails for exact-match queries
Attribute AgentProduct title contains key identifiers (brand, type, size, material)
Retrieval step misses queries using those identifiers
Attribute AgentProduct is in the correct category for each target channel
Listing suppressed or placed in wrong browse node
Taxonomy AgentSub-category and product type are filled, not just top-level category
Filter navigation fails; product buried below relevant results
Taxonomy AgentTaxonomy is current — channel taxonomies change quarterly
A category correct 6 months ago may now be wrong
Taxonomy AgentMaterial and composition are structured (not buried in description)
Fails material-based filter queries
Attribute AgentDimensions are explicit and in standard units
Fails size-based filter queries
Attribute AgentCompatible products or fitment data is present where applicable
Fails compatibility filter — product excluded from shortlist
Product Graph AgentKey specs for the category are filled (voltage, pressure, load rating, etc.)
AI agent can't compare against alternatives — product gets skipped
Attribute AgentProduct is discoverable
Bucket 2: Compliance
These checks determine whether your product can be published and sold without triggering a rejection or suspension. Compliance failures often go undetected until after a listing goes live — at which point the cost is a channel suspension, not just a failed submission.
Hazardous materials are correctly flagged (GHS, DOT, OSHA)
Post-listing rejection, channel suspension, liability exposure
Compliance AgentMultipack configuration is explicit and consistent with product identifier
Multipack listing rejected or priced incorrectly
Compliance AgentAge restrictions and safety warnings are present where required
Regulatory non-compliance, listing removal
Compliance AgentRestricted or controlled products are flagged before submission
Channel suspension if caught post-publication
Compliance AgentTax category is correct per jurisdiction
Pricing errors, tax liability exposure
Compliance AgentCertifications and standards are listed where required (UL, CE, food-safe, etc.)
Agent can't verify category-specific safety claims; product excluded from regulated queries
Compliance AgentCleared to publish & sell
Bucket 3: Content
These checks affect whether an AI agent can reason about your product and make a confident recommendation. A product that passes Discovery and Compliance but fails Content will be found — and then deprioritized in favor of products the agent can evaluate more clearly.
Product description is specific, not generic ("quality product" teaches the agent nothing)
AI agent can't generate a confident recommendation
Attribute AgentKey use cases or applications are stated explicitly
Fails use-case queries
Attribute AgentSetting, problem, and outcome are described for applicable products
Agent can't match buyer scenario to product
Attribute AgentNo placeholder values in required fields ("TBD", "See description", "N/A")
Channel validation fails; agent confidence drops
Attribute AgentAttributes are consistent across title, description, and structured fields
Conflicting signals lower agent confidence — product gets deprioritized
Attribute AgentLanguage and units are appropriate for each target market
Filter fails for region-specific queries; international channels reject the record
Attribute AgentComparative signals are present — what this product does vs. common alternatives
AI comparison step picks the product with more evaluable data
Attribute AgentReady to recommend
What to do with your results
Products that fail Bucket 1 or Bucket 2 are likely already costing you in suppressed listings, compliance flags, or silent exclusions from AI recommendations. Fix these first — they have an immediate, measurable impact on publish rate and discoverability.
Products that pass Buckets 1 and 2 but fail Bucket 3 are published and discoverable, but losing at the comparison step. When a buyer asks Perplexity or Gemini to compare options, these products get skipped in favor of ones the agent can evaluate more clearly. The fix is structured content enrichment — not copy polish.