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Agentic Commerce Score · open standard · rubric v0.1 · 2026-07-23

Can AI agents actually buy from your store?

The Agentic Commerce Score (ACS) is a deterministic 0–100 measure of whether AI shopping agents — ChatGPT, Google AI Mode / Gemini, Perplexity, Claude — can find, evaluate, and buy from an online store. It is computed exclusively from public, externally verifiable signals, so any store can be scored by anyone, and the same store scores the same number on the same day.

# score any store in ~10 seconds (free, open source, MIT)
npx agentic-commerce-score your-store.com

Reference implementation: github.com/arenza-ai/agentic-commerce-score — zero dependencies, ~8 GET requests per scan, no sign-up, nothing leaves your machine.

Why a score, and why now

Shopping is moving into AI assistants. ChatGPT completes checkouts inside chat through ACP — the OpenAI/Stripe Agentic Commerce Protocol; Google AI Mode and Gemini surface product catalogs through UCP; Perplexity, Claude, and Amazon's agents fetch and compare products on a buyer's behalf. This is agent-led growth: external AI agents discovering, recommending, and transacting your products for a shopper who may never open your website.

Most stores were built for human eyeballs and Googlebot. They block AI crawlers in robots.txt, render their catalog only through JavaScript, ship Product schema without price or availability, and hide the policy pages an agent needs before it completes a checkout. Every one of those silently drops the store out of AI shopping answers — and nothing in a classic SEO audit surfaces it. ACS scores the store the way a buying agent experiences it.

Three pillars

PillarWeightQuestion
Discover35Can agents fetch and read the store at all?
Evaluate40Can agents parse, trust, and compare the products?
Transact25Can an agent complete a purchase?

A check passes, warns (half credit), or fails. Checks that cannot run (e.g. no open feed to sample) are marked n/a and their weight is redistributed within the pillar — the unverifiable is never punished, only the verifiably missing.

The twelve checks (v0.1)

CheckWtPassing bar
AI crawlers allowed in robots.txt (GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended…)
robots_ai_access
12No AI shopping crawler root-blocked
llms.txt present
llms_txt
5/llms.txt exists (absence = soft gap, never a blocker)
Sitemap discoverable
sitemap
6Declared in robots.txt or at /sitemap.xml
Homepage readable without JavaScript
homepage_renders
12≥300 visible words of real HTML, HTTPS, no noindex, no empty JS shell
Machine-readable product catalog
open_feed
10Open product feed parseable (e.g. /products.json)
Required fields: title · image · price · description
catalog_required_fields
12≥90% average coverage over sampled products
Titles literal + matchable, not slogans
title_quality
6<10% marketing-fluff titles, ≥15% carry a literal spec/descriptor
Descriptions an agent can quote
description_depth
4≥70% of products at ≥120 characters
Product JSON-LD with complete Offer
product_schema
8Offer carries price + priceCurrency + availability
Agentic-checkout rail (platform prerequisite)
checkout_rail
10Platform ships a native rail (v0.1: Shopify → ChatGPT checkout + Google catalog)
Machine-readable price + availability
machine_price_availability
7≥90% of products expose both
Shipping + returns policies discoverable
policies
8Both found at stable URLs

Exact thresholds, the AI user-agent list, and the changelog live in the versioned spec: SCORE.md. Scoring changes only ship with a rubric version bump; comparing scores across rubric versions is invalid.

“Agent-buyable” — the headline bar

A store counts as agent-buyable when all five hold:

  • AI shopping crawlers may fetch the store (robots.txt does not root-block them);
  • an open, parseable product feed exists;
  • required-field coverage (title / image / price / description) averages ≥ 80%;
  • the platform has a native or partial agentic-checkout rail (ACP / UCP path);
  • shipping and returns policies are both discoverable at stable URLs.

This is deliberately strict: it approximates the minimum a shopping agent needs to find a product, trust its data, and hand off a compliant checkout. Grades: A ≥ 85 · B 70–84 · C 55–69 · D 40–54 · F < 40.

What ACS is not

  • Not an enrollment checker. Whether a merchant has switched on ChatGPT checkout or a Google catalog surface is private state. ACS scores the externally verifiable prerequisites, and says so.
  • Not a visibility tracker. ACS measures whether agents can buy from you. Whether AI assistants actually recommend you is the outcome side — that requires probing the assistants themselves, at scale, market by market. That is what Arenza measures.
  • Not SEO advice. Some checks overlap with SEO hygiene, but the lens is strictly “what does a buying agent need”.

Get the outcome side

ACS tells you whether agents can buy from you. The next question is whether they recommend you — against your competitors, in every market that matters. Run the free AI visibility score (10 real buyer questions, live), or read how Arenza measures AI visibility and accuracy.