Preparing your store for AI shopping agents is a seven-step audit: complete product schema, current feeds, citation presence in assistants, operationally true inventory and pricing, parseable policies, participation in agent interfaces as your platform ships them, and a measurement loop so you know it’s working. This is the practical half of agentic commerce — the buyer’s agent is coming either way; this checklist decides whether it can choose you.
Step 1: Product schema — the non-negotiable
Every product page needs complete schema.org Product markup: name, price with currency, availability, ratings, brand, GTIN where you have it. Agents parse this before they read your prose, and a missing or contradictory field doesn’t earn a follow-up question — it earns a skip. Audit the top 20 sellers first with a schema validator; wrong price or stale availability in markup is worse than none, because it teaches agents your data lies.
Step 2: Feeds that don’t drift
If you run shopping ads you already have a product feed — the same infrastructure agents increasingly read. The failure mode isn’t absence, it’s drift: feed says in stock, store says sold out; feed price is last month’s. Set feed sync to the fastest cadence your platform allows and treat feed errors as revenue bugs, not marketing chores.
Step 3: Be in the answers (the citation layer)
Before any agent buys, an assistant recommends — and that’s measurable today. Put your 20 top buyer questions (“best X for Y”) to ChatGPT and Perplexity and record who’s named. If it’s not you, that’s an AEO gap with a specific fix: answer-first product content, comparison pages that deserve citations, and third-party corroboration (the get-cited playbook). Recommendation is the top of the agentic funnel; everything below it inherits.
Step 4: Operational truth
Agents remember what humans forgive. A stale price honored grudgingly, a “2-day shipping” that’s really five, a sold-out product still purchasable — each teaches the systems ranking you that your store’s claims need discounting. The fixes are unglamorous: inventory sync across every surface, shipping promises set to what you actually hit, and prices consistent between page, feed, and schema. This is where autopilot-style operations quietly become an agentic-commerce advantage — machine-speed housekeeping keeps machine-readable truth true.
Step 5: Policies a parser can use
Returns, warranties, shipping zones — written for humans, they’re marketing; written clearly, they’re decision inputs. An agent comparing two stores can weigh “free 30-day returns” only if it can extract it. Short declarative sentences, consistent placement, no PDF-only policies.
Step 6: Interfaces, as they arrive
The transactable layer is arriving platform by platform: catalog programs feeding assistant partners, agent-checkout pilots, MCP surfaces for tool-using agents. You don’t have to chase every pilot — but know your platform’s roadmap, opt into the catalog programs (they’re mostly free distribution), and make checkout-program decisions deliberately when they reach you (what agentic checkout changes).
Step 7: Measure, monthly
Two numbers tell you if any of this works: citation share on your buyer-query set (are assistants naming you more?) and assistant-referred sessions in analytics (traffic arriving from AI surfaces — small today, compounding). Track both monthly; they’re your agentic-commerce scoreboard long before checkout volume shows up.
The 90-minute version (do this week)
If seven steps reads like a quarter’s roadmap, here’s the triage that fits in one sitting: run your top 20 products through a schema validator (30 min — fix the top-seller errors first); ask ChatGPT and Perplexity your five biggest buyer questions and write down every brand named (20 min — that list is your competitive reality); spot-check feed price and availability against five live product pages (15 min); and read your returns policy as if you were a parser — if the answer to “how many days?” isn’t extractable in one sentence, rewrite that sentence (25 min). Ninety minutes, and you’ll know which of the seven steps is your bottleneck instead of guessing.
The order matters
Schema before citations (unreadable stores don’t get recommended), citations before checkout programs (nobody buys what isn’t suggested), truth throughout (one burned agent outweighs ten good impressions). If you do exactly one thing this week: validate schema on your top 20 products and run the 20-question citation check. Ninety minutes, and you’ll know precisely where you stand.
The bottom line
Agent-readiness isn’t a technology project — it’s data hygiene, honest operations, and visibility work with a new referee. The stores that treat machine-readable truth as a product feature will be the ones AI buyers can actually choose.
The citation half runs itself — weekly scans of what AI recommends, gaps turned into fixes: datavessel AEO scanning →
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