Can Your Product Data Survive an AI Comparison? (2026)

AWS lifecycle notice (June 30, 2026) — Amazon Bedrock Agents Classic is in maintenance for new customers after July 30, 2026. If you host a comparison copilot, use Bedrock AgentCore. Full matrix: lifecycle roundup. Third-party comparison agents do not run on your Classic stack.

On June 17, 2026, AgentCore Harness reached general availability (What’s New). Useful when your agent compares SKUs with tools. Useless as a patch for empty attributes.

Can your product data survive an AI comparison? That is a merchant audit, not a strategy keynote. AI may compare price, features, attributes, reviews, availability, delivery, return policy, compatibility. This post is not when AI agents choose products (post 36 — strategic decision layer). It is whether your data can stand in that bake-off without collapsing into prose.

It is not an anonymized client engagement. Demo SKUs are fixtures. We do not invent comparison win-rate KPIs.

First-party signals we reuse (not eCommerce outcomes) — Gateway server-side tools cut median tool round-trip ~180 ms → ~95 ms on a B2B CRM assistant (12 tools, ~8k turns/day) — Gateway post. Platform TCO silhouette: support-style AgentCore at 50K sessions/mo ~$791/mo platform + model (decision guide). Those numbers size your host. They do not measure “won vs competitor.” Model remaining sessions on the AgentCore pricing calculator.

Reproduce this — Copy ai-comparison-audit-framework.md. Score one category. Passport: ai-product-passport-schema.md. Contract: ai-ready-catalog-contract.md. Decision-layer signals stay in agent-choice-optimization-signals.md. Series folder: ecommerce-ai-agents-series/.

Opinionated take: fail the category closed until required attributes share one taxonomy and ATP/price carry asOf. Trade-off: you delay comparison-table content. You stop losing to a worse product with cleaner fields.

FactualMinds helps merchants audit product information for agents — not a guaranteed ranking in someone else’s model.

The audit (one category, not the whole catalog)

Score your category. Do not submit a demo table as a business case.

AI may also weigh reviews, availability, delivery, return policy, compatibility — those belong on the passport and the decision-layer artifact. This audit asks whether the merchant file is even comparable.

How to run the audit (90 minutes, one category).

1. Export required attrs, units, facet definitions, and passport fields for 20–50 live SKUs (your count, not ours).
2. Mark missing required keys. One empty width on apparel is a fail for that SKU, not a 2% “data quality” average that hides it.
3. Diff units. Convert nothing in a spreadsheet as a fake fix — fix the taxonomy in PIM.
4. Diff PLP facets vs passport. Every disagree is a fail.
5. Pull three SKUs a human merchandiser would compare. If they do not share a schema, “missing comparisons” fails the category.
6. Read five descriptions. If a spec exists only in prose, “weak descriptions” fails those SKUs.
7. Do not score reviews or brand love here. That is post 36. Do not score GEO rank. That is not a thing we sell.

Goldens for your copilot, if you have one: given two SKUs where a required attr is empty, the agent returns incomparable, not a winner. Must-not-invent-claims. Must-not-invent-discount.

Baymard 70.22% still applies when the “winner” at comparison has a total or size that checkout rejects.

Adobe July 2026 AI-referral +62% YoY and 60% higher conversion (Digital Commerce 360) remain channel mix, not a comparison win rate. McKinsey / EuroCommerce June 2026 61% European AI discovery is discovery behavior, not your bake-off score.

SEO is not obsolete. This audit does not replace Shopping attribute feeds. It is whether an agent can compare in addition.

Worked fixture (replace with your SKUs)

Three running-shoe SKUs in one category. Worksheet only.

An agent that still names a winner among these three is guessing. Fail closed.

How this sits next to the passport and the storefront

flowchart TB
  PIM[PIM contract post 35]
  Pass[Passport post 62]
  Store[Storefront APIs post 61]
  Audit[This audit]
  Choice[Decision layer post 36]
  PIM --> Pass
  PIM --> Store
  Pass --> Audit
  Store --> Audit
  Audit --> Choice

If post 10’s shopping readiness scores below 8, do not invite a copilot to compare live. If post 61 APIs are scrape-only, the audit will fail on accuracy even if PIM looks pretty.

Do not default to multi-agent to “run comparisons.” One read harness with getProduct is enough until the split test says otherwise. Prefer tools over Memory for live specs.

How this sits on AWS

Your comparison copilot: Harness, Gateway getProduct / getInventory / getPrice, goldens that must-not-invent a winner when a required field is empty. Cedar on any write (do not attach publishProduct). Export to Strands only for hop caps — Agents-as-Tools / Graph, not Swarm on merchandising writes. No native Shopify connector. Skip Agents Classic after 30 July 2026. Browser off.

Gateway ~180 → ~95 ms is the CRM canary. Catalog p95 dominates. ~$791/mo at 50K sessions is a support-shaped host floor, not savings from “winning comparisons.”

Context: Python 3.12+, boto3 ≥ 1.38.0, supported region. Sketch — pin the model your account allows.

# Sketch — InvokeHarness comparison turn. Fail closed if getProduct lacks required attrs.
# runtimeSessionId ≥ 33 characters. Do not attach publish or discount-issue tools.
import boto3
import uuid

client = boto3.client("bedrock-agentcore", region_name="us-west-2")
response = client.invoke_harness(
    harnessArn="arn:aws:bedrock-agentcore:us-west-2:123456789012:harness/compare-read",
    runtimeSessionId=str(uuid.uuid4()),
    messages=[{
        "role": "user",
        "content": [{"text": "Compare SKU-TEE-BLU-M and SKU-TEE-BLU-L on width and ATP only"}],
    }],
)

Goldens must fail if the model fills width from the title.

What broke

What broke — A merchandiser asked a harness to “write comparison modules for the PLP” so agents and humans would see the same bake-off. Attributes were still empty; units mixed EU and US size. Detection: published copy claimed a waterproof rating the SKU was not certified for; goldens had no must-not-invent-claims case; Policy was off because “it was only content.” Fix: unpublish modules; run this audit; put specs in fields; claims gates in PIM; merchandiser publish; strip publishProduct. Lesson: comparison copy is not a substitute for comparable data. Weak descriptions plus auto-publish is how you lose the bake-off in court, not just in the model.

Inventing a discount so the agent “wins” is the post-36 failure mode. This audit should have already failed on missing promo-engine fields.

What to Do This Week

1. Copy ai-comparison-audit-framework.md.
2. Pick one category. Score the five checks. Do not average the catalog.
3. Align facets with passport fields. Lying navigation is an audit fail.
4. Confirm post 35 contract and post 62 passport before a comparison-content sprint.
5. Read post 36 for what you still cannot control (third-party rank).
6. If you host a copilot, add goldens that fail on invented specs. Browser off.
7. Model sessions on the AgentCore pricing calculator.
8. Run monday-checklist.md. Contact us if the first fail is units, not copy.

What This Post Doesn’t Cover

• Strategic decision layer — post 36
• Shopping-agent information object — post 62
• Catalog contract — post 35
• 13-check copilot invite score — post 10
• A measured “agent picked us X%” KPI from a named merchant — we are not inventing it

FAQ

When should you NOT run an AI comparison audit as a merchandising campaign?

Skip “win the agent” when required attrs are empty or units disagree. Fix the contract first. Do not invent discounts.

How is this different from when AI agents choose products?

Post 36 is what intermediaries weigh. This is whether your SKUs are comparable. Link 36; do not rewrite it.

What could go wrong if descriptions are strong but attributes are empty?

Invented winner tables. “M” vs “Medium.” Specs belong in fields.

When should you NOT let an agent publish comparison-table copy onto the PDP?

Auto-publish drifts and invents claims. Draft maybe; merchandiser publishes; PIM gates.

What could go wrong if facets on the PLP disagree with passport fields?

Filters lie; the agent reads another flag; checkout sells the wrong SKU. Facets must equal agent fields.

Is AgentCore required to survive third-party comparisons?

No. Structured objects are. Harness hosts YOUR loop. Empty width cannot be runtime-fixed.

Need a category audit before a comparison copilot? Contact FactualMinds or see Amazon Bedrock.