Direct answer: do not start with autonomous checkout, a chatbot, or another isolated feed. Start with one canonical product record that keeps variant identity, price, currency, availability, delivery promise, returns, and destination URL consistent across the PIM or ERP, commerce platform, product page, structured data, feeds, and checkout. AI can enrich descriptions and detect conflicts; source systems and rules must still decide what exists, what it costs, where it can ship, and what the customer can buy.
OpenAI begins its agentic commerce guidance with a structured product feed, but direct feeds in ChatGPT are currently limited to approved partners. Its shopping help also warns that product details and prices can be delayed and tells shoppers to confirm final price, availability, variants, returns, and warranty with the merchant. Google Merchant Center and Search Central impose the same operating discipline: price and availability should match the landing page and checkout. Being “in the feed” is not a commercial outcome. It is an entry condition that fails when the catalog diverges.
The https://makinai.co/insights/en/agentic-commerce-business-case-framework helps determine whether agentic commerce has a business case. This article covers the implementation layer before that bet: make one category trustworthy for agent discovery and purchase without rebuilding the stack. The https://makinai.co/insights/en/ai-catalog-enrichment-scope-cost-product-attributes addresses creating factual attributes from authorized sources; this distinct project propagates approved offer, price, inventory, delivery, and policy without channel divergence. The https://makinai.co/insights/en/ai-onsite-search-missing-products-ecommerce-conversion shows how catalog data and filters break onsite search, while https://makinai.co/insights/en/inventory-aware-ai-marketing-paid-media-crm connects inventory to media and CRM. One product truth must support the workflows.
Use an Agent-Ready Product Contract
- Identity — persistent product and variant IDs, GTIN or MPN where appropriate, brand, category, and parent-child relationships without duplicate offers.
- Offer — factual title, required attributes, compatibility, size, color, material, and unit; AI must not invent a product claim.
- Price — amount, currency, tax treatment, promotion terms, effective dates, and installment logic interpreted consistently in feed, page, and checkout.
- Availability — sellable status, authorized quantity or range, location, backorder, and update interval; “in stock” cannot have channel-specific meanings.
- Fulfillment — served destination, method, cost, ETA, cutoff, and item, region, or carrier exceptions.
- Policy — returns, warranty, restrictions, subscriptions, bundles, and regulated-item conditions in machine-readable fields.
- Visual proof — correct primary and variant images with usage rights, resolution, quality, and SKU association.
- Destination — canonical URL, preselected variant where required, and a checkout that preserves offer, currency, and availability.
- Freshness — source, timestamp, SLA, update mode, and the action taken when a fact expires.
- Diagnostics — error, severity, owner, and reconciliation queue; a model never guesses a replacement price or stock status.
This is not another marketing spreadsheet. It is a testable agreement among merchandising, commerce, data, logistics, service, legal, and finance. Every field needs a source of record, transformation rule, frequency, and decision authority. OpenAI's Merchant Feed Terms keep the merchant responsible for the content and do not guarantee that a product will be surfaced. Fund data quality and operations without assuming ranking, traffic, or channel approval.
Choose the smallest integration layer that solves the problem
- Level 1: page and structured data — fix Product and Offer data, variants, price, availability, shipping, returns, canonical URL, and sitemap.
- Level 2: existing feed — strengthen the export already used for Merchant Center, a marketplace, or a catalog partner; do not create another truth source.
- Level 3: channel delivery — map the contract to the channel schema, using full snapshots and incremental updates appropriate to need and access.
- Level 4: action and checkout — proceed only after identity, policy, inventory, fraud, payment, confirmation, cancellation, and support work in shadow mode.
OpenAI says Shopify merchants can already have products integrated through Shopify's catalog. That can remove the need for an individual direct feed, but it does not fix variant, description, availability, or policy defects at the source. Eligibility and delivery differ for other platforms. Do not build a critical dependency around access that still requires approval. Improve assets that also serve search, paid media, recommendations, and marketplaces.
Illustrative example: one category, not 12,000 SKUs
A retailer with 12,000 SKUs chooses 800 items in a high-intent, healthy-margin category. The audit finds 11% of variants missing a required attribute, daily inventory updates despite volatile stock, expired promotions still present in one feed, and a generic return policy that does not fit bulky items. These figures are illustrative—not a benchmark or MAKINAI client result.
The pilot fixes identity and price first, then adds incremental availability updates, shipping and returns by product class, and a divergence queue. AI proposes attribute mappings, detects conflicting titles, and clusters repeated errors. Rules stop publication when currency, price, variant, destination, or policy fails a test. Merchandising approves facts, operations approves promises, legal approves policy, and commerce owns the release.
Run a six-week pilot
- Week 1 — select category, market, channels, and baseline; inventory the PIM, ERP, commerce platform, OMS, feeds, structured data, and checkout.
- Week 2 — publish the field contract, systems of record, keys, owners, latency, and blocking criteria.
- Week 3 — reconcile identity and variants; repair URLs, taxonomy, attributes, and images in a manual sample before automation.
- Week 4 — connect price, availability, delivery, and returns; test consistency across source, page, feed, and checkout.
- Week 5 — run in shadow mode, simulate expiry and failure, review rejections, and test rollback without exposing a wrong offer.
- Week 6 — release the eligible category and document runbook, operating cost, exception queue, and expand-adjust-stop criteria.
Cost rises with variant count, countries and currencies, price and inventory frequency, store-level granularity, bundles, subscriptions, promotions, tax rules, shipping, returns, images, channels, and review. A daily update may suit a stable catalog but is dangerous for scarce stock or volatile prices. Near-real-time updates reduce divergence and increase integration, observability, and on-call cost. A central truth source limits conflict; channel overrides may improve fit but create debt and need an owner.
Measure operational trust before attributing revenue
In the first month, monitor variant coverage, required-field validity, eligible, rejected, and limited products, price and availability mismatches, record age, valid URLs and images, repair time, and hours per exception. Then connect qualified sessions, product views, add-to-cart, checkout starts, orders, cancellations, returns, and contribution margin by category and source when observable.
Inclusion, an impression, or an AI reference does not prove incremental revenue. Platforms may not expose every event, while mix, seasonality, price, and media also change outcomes. Use a comparable category or phased rollout and disclose attribution limits. The https://makinai.co/insights/en/ai-product-recommendations-ecommerce-conversion-margin shows how to protect margin in product recommendations; apply the same discipline to agent-assisted shopping.
If your team already operates a PIM, ERP, commerce platform, and feeds but cannot tell which price, availability, or policy an agent receives now, bring MAKINAI at https://makinai.co/services/en/crm-ecommerce-commerce-transformation one category, the current schema, channel diagnostics, product pages, and 30 days of price and inventory changes. We can scope the Product Contract and pilot without turning the discussion into a replatforming project.