Score0/8Start

Readiness Playbook · 2026

The AI / Agentic Storefront Readiness Playbook

Shopify turned agentic storefronts on by default. AI agents now read your clients' pricing, stock and SKUs straight off the store and answer buyers with them — right or wrong. This is the 8-point check we run before anyone talks about "AI visibility." Built for the agencies and operators who run Shopify Plus & Magento 2 stores, B2B especially.

8 checks · 5 run in-browser~10 min for the front-end fiveNo sign-up · nothing leaves this page

The failure is almost never crawlability — the agent reaches the page fine. It's context. When the ERP, the PIM and the storefront disagree on a single field, nobody decided which system wins, so the agent states the contested value as fact. Fix the source of truth first. Then carry it off-site — in that order. Checks 1–7 fix the record; check 8 is the only one you do afterward.

How to score a store

Take one real, live store — ideally a B2B or wholesale client — and one representative product. Five checks run from any browser right now (Front-end); three need ERP/admin access (Needs backend). This is a spot-check on one product, not a full catalog grade — treat the score as a directional signal. Be honest: a crawler thinks a broken store looks perfect.

1 · Pick a store + one SKUReal and live. B2B/gated catalog is where the gaps hurt most.

2 · Run the 5 in-browser checksNo login needed. Add the 3 backend checks when you have admin access.

3 · Read the band, start at #1Score updates live. Fix in order — source of truth first.

The 8 checks

Source-of-truth first (1–7), off-site last (8). Tick a box only if the store genuinely passes the test.

Source-of-truth alignment

Needs backend

For one product, do the ERP, the PIM and the live storefront state the same price, stock and spec? When they disagree, the agent quotes whichever system won the last sync — not the value the client would choose.

Test it — 60 sec

Pick one SKU. Compare its price and stock in the ERP/admin, the PIM, and the live page. Do all three match to the cent and the unit?

Real example

ERP holds a £42 trade price; the storefront renders £58 retail because the sync ran storefront-first. An agent quotes £58 to a wholesale buyer who was promised £42.

Engineer-grade fix

Name one system as the owner of each field and reconcile the others to it before exposure. On Shopify Plus, render from a single metafield source; on Magento 2, settle whether MSI, the ERP, or the catalog wins on price/qty.

Passes — all three systems agree per field.

B2B / gated-catalog exposureB2B

Front-end

Is any tier price, contract price or restricted SKU readable by an unauthenticated agent? Default-on agentic storefronts can quietly publish pricing and SKUs a B2B client never agreed to make public.

Test it — 60 sec

In a logged-out / incognito window, open a B2B product and ask an agent "what's the price of [SKU] at [store]?". If it returns tier or contract pricing without a login, it's exposed.

Real example

A wholesale catalog renders list price + MOQ publicly for SKUs the client only sells under NDA — now quotable by any agent, and by competitors.

Engineer-grade fix

Gate pricing behind auth and serve agents only public fields. Shopify Plus B2B customer context; Magento 2 shared catalog + customer groups. Decide deliberately what an unauthenticated agent may see.

Passes — only intended-public data is visible pre-auth.

Agent answerability

Front-end

Can an agent answer a real buyer question from the product data alone? If the answer lives in prose, a PDF, or a rep's head, the agent hedges or drops the SKU.

Test it — 60 sec

Ask ChatGPT or Perplexity a genuine buyer question about one SKU — "does [product] fit [use case]? lead time? MOQ?". Did it answer from the store, hedge, or move to a competitor?

Real example

Fit and compatibility live in a spec PDF, so the agent says "check with the seller" and recommends a rival that stated the same fact in plain attributes.

Engineer-grade fix

Lift the top five buyer questions per category into structured, retrievable attributes — not description copy. If a human rep answers it daily, an agent should read it.

Passes — an agent answers real questions from the data.

Schema legibility

Front-end

Is the product data machine-parseable, or free-text and variant soup? Agents parse raw attributes, not marketing copy. Messy options get dropped from filtered recommendations.

Test it — 60 sec

Open one product's structured data / attributes. Are options clean typed values, or free text like "see notes", "call for size", "red-ish / large"?

Real example

Variant options entered as free text can't be mapped to a size or colour facet, so the SKU never surfaces when an agent filters "medium, blue, in stock."

Engineer-grade fix

Normalize variants and custom options into typed attributes and valid product schema. Clean entity models in, parseable answers out.

Passes — attributes are typed and parseable.

Stock & lead-time truthfulness

Needs backend

Is the stock and lead-time the agent reads actually current, or a stale 3PL/ERP sync? "Ships in 2 days" when it's really 9 doesn't just lose the order — it burns trust the client can't see happening.

Test it — 60 sec

For one in-stock and one backordered SKU, compare what an agent states about availability against the real ERP/3PL number right now.

Real example

The 3PL feed syncs nightly; a sold-out item still reads "in stock, ships in 2 days" for eighteen hours, and the agent keeps recommending it.

Engineer-grade fix

Move to real-time availability, or expose an honest fallback range. Never let a stale value be stated as a confident fact.

Passes — availability is current or honestly hedged.

Cross-engine consistency

Front-end

Do ChatGPT, Perplexity and Gemini describe the product consistently and correctly — or contradict each other and the store? Divergence is the tell that the underlying record is contested.

Test it — 60 sec

Ask all three engines the same factual question about one product. Do they agree with each other and with the store's own page?

Real example

Two engines cite an old spec from a cached marketplace listing, one cites the store — the buyer gets three different answers and trusts none.

Engineer-grade fix

You can't patch this engine-by-engine. Strengthen the on-site record and its off-site echoes so the answers converge on the truth.

Passes — the engines agree with the store and each other.

Entity / identity correctness

Front-end

When an agent talks about the product, does it name the client's store as the source — or a directory scrape, a marketplace listing, or a competitor? If the agent doesn't know who owns the answer, the client doesn't own the sale.

Test it — 60 sec

Ask "who sells [product] and where should I buy it?". Does the agent name the client's own store, or send the buyer to a reseller?

Real example

The agent recommends the product but links a marketplace reseller, because the brand's own entity signals are weaker than the reseller's.

Engineer-grade fix

Reinforce entity and sameAs signals plus the on-site source of truth, so the store — not a directory — is the cited origin.

Passes — the store is named as the authoritative source.

Off-site carry — do this last

Needs backend

Only after 1–7: is the correct record being carried into the surfaces the store doesn't own — feeds, marketplaces, agent catalogs? Enriching and syndicating before the source is fixed just amplifies the wrong value at scale.

Test it — 60 sec

Only if 1–7 pass: spot-check that a live feed / marketplace / agent-catalog entry matches the reconciled store record for one SKU.

Real example

A product feed still carries last quarter's price because enrichment ran before the source of truth was fixed — so wider reach means wider wrong.

Engineer-grade fix

Pause syndication until 1–7 pass, then distribute off the clean record. Fix first, broadcast second — in that order.

Passes — off-site data matches the reconciled record.

Your score

0/8

Tick the checks above

Your score updates as you go. Nothing here is stored or sent — it's yours.

0–3

At risk

Agents are likely stating wrong things about these products now. Nothing off-site helps until the record is fixed.

4–6

Contested

Visible in AI answers, but contested values leak — a price, a stock count, a B2B tier the client never meant to publish.

7–8

Ready

The record is coherent. Off-site and visibility work will compound instead of amplifying errors.

Where to start: fix in order — checks 1–2 first (source of truth + B2B exposure). Everything downstream just amplifies whatever those two say, so a mid score usually means "reconcile the record, then re-test."

4 of 4 broken → 0 of 4

A live US home-furniture store: four AI agents were stating four core product facts wrong. After running this playbook, zero. It's a DTC store — the method is identical for B2B, where a wrong tier price or an exposed contract SKU costs more than a wrong spec.
See the case →

Want it scored for you?

Send one client store URL— ideally a B2B one — and we'll run these 8 checks and send back the punch-list. Worst case it's a list your build team closes. Best case it's a wedge you put in front of your own clients.

We only look at public storefront pages, with your go-ahead — no access to your client's admin. No strings, no pitch, no price. Async — reply wherever you found this, or email leo@luma-e.com with the URL.