Three commerce agents—Shopper Agent, Buyer Agent, and Merchant Agent—are generally available, while ChatGPT catalog selling reaches GA in July 2026. For developers, the hard part is proving that synchronized product data produces recommendations people can trust.
Treat catalog synchronization as an interface
Retailers can synchronize catalogs from Business Manager to ChatGPT without extra software or third-party middleware. That is a meaningful simplification, but direct synchronization removes middleware, not the need for product-data governance.
The useful engineering model is an interface contract. Before exposing a catalog to an AI shopping channel, document where each product value originates, who owns it, and which review gate can stop publication. This mirrors Van Data Team's starting point: map product sources, field ownership, and review gates.
A successful transfer only proves that data moved between systems. It does not prove that an agent selected the right item, supported its answer with useful evidence, or respected the availability of a requested variant. Those are different failure modes and should be observable separately.
Test what the shopper will experience
The release includes Agentic Commerce Search, which uses inferred intent and is designed for both Salesforce and non-Salesforce storefronts. That makes test design more important than a simple exact-name lookup.
Build test cases around realistic shopping intent and compare the output with the product records your team considers authoritative. Keep the acceptance criteria concrete:
- Recommendation accuracy: Did the response select products that fit the stated intent, and can a reviewer trace the choice to current catalog data?
- Evidence quality: Does the answer use product information that supports the recommendation instead of merely naming an item?
- Variant availability: Is the recommended size, color, or other requested variant actually available in the catalog at review time?
- Storefront coverage: If your implementation spans Salesforce and non-Salesforce storefronts, does the same review method work across both?
The first three checks come directly from the operational risk: AI visibility is not a win when recommendations are inaccurate, poorly supported, or attached to unavailable variants. The fourth keeps the validation approach aligned with the product's stated storefront scope.
For each failed test, capture the source record, the responsible field owner, and the review decision. That turns an incorrect answer into a fixable data or workflow problem instead of a vague "AI quality" issue.
Use the release stages as rollout boundaries
ChatGPT catalog selling is GA in July 2026. Google Search, including AI Mode, and the Gemini app are scheduled for later this summer. Treat that sequence as a reason to stage readiness work, not as permission to assume one channel's behavior will predict another's.
Start with shared catalog controls: source mapping, ownership, validation, publishing, monitoring, and failure recovery. Then keep channel-specific tests separate. The reusable layer should establish whether a claim and variant are dependable; the channel layer should show how each destination represents them.
This is also why a single "catalog synced" status is too coarse. A useful dashboard should distinguish transfer success from review status and observed representation. Vanaxity follows that separation across SEO, GEO, and AEO: create structured content, validate claims, publish across channels, and monitor how search and answer engines represent it.
Be explicit about the tradeoffs
Direct synchronization reduces the integration path, which can make distribution simpler. The same shorter path can also expose weak catalog controls sooner. More review gates add operational work; fewer gates leave less opportunity to catch a questionable claim or unavailable variant before an agent presents it.
Inferred-intent search also changes what a useful test looks like. Exact product names are still valid inputs, but they do not cover a mechanism designed to infer intent. A broader test set takes more effort to maintain, yet it gives teams a clearer view of recommendation behavior.
The stated design for non-Salesforce storefronts widens the intended scope. It also makes source ownership worth documenting explicitly, because that reach does not remove differences in how a retailer manages its own product data.
Define readiness before visibility
A practical readiness gate can be simple: every exposed product source is known, every critical field has an owner, every publish path has a review decision, and the three shopper-facing checks have passed. Monitoring should then verify how the product is represented after publication, with a route for correcting failures.
That definition keeps the milestone honest. Distribution is complete when a catalog reaches a channel. Readiness is demonstrated when the resulting recommendation remains accurate, evidenced, and available.
If you were implementing Salesforce Agentforce Commerce, which check would you automate first—recommendation accuracy, evidence quality, or variant availability—and what exact result would fail the build?
📖 Read the full guide → Salesforce Agentforce Commerce: The GEO Playbook for AI Shopping
