Key takeaways
- One catalog serves every agent: the same structured signals (grid position, price, rating, endorsement, and description text) drive selection across ChatGPT, Gemini, and Perplexity.
- The weights are provider-specific. The strongest public evidence, the academic ACES simulation, shows the same signal moving each model by a different amount.
- Protocol rails carry you to the engines: ACP feeds carry ChatGPT shopping (the in-chat checkout was scaled back in March 2026, so purchases now complete on merchant sites), and Google's own developer documentation confirms UCP is the rail enabling agentic actions on Gemini and AI Mode.
- The rails no longer map one-to-one onto engines: Microsoft Copilot consumes both, running Copilot Checkout on ACP through Stripe while reading UCP-ready Merchant Center feeds.
- There is no universal 'top rank' to chase. Optimize the shared inputs first, then break ties engine by engine.
- Agent behavior is a moving target (a model update can reshuffle which products win), so every claim on this page is dated.
The shared signal set (one catalog, many agents)
You do not maintain a separate product catalog for each AI shopping engine, and you should not optimize for one as if the others do not exist. The selection chapter of this handbook lays out the signals agents actually read (where a product sits in the results grid, its price, its star rating, whether it carries a platform endorsement, and how its description is written), and those same categories of signal recur across ChatGPT, Gemini, and Perplexity. That is what lets a single well-structured catalog compete everywhere at once, and it is the practical core of agent selection: the inputs are shared even when the scoring is not.
The evidence that the categories are shared but the weights are not comes from one controlled study. In the ACES simulation (vision-language-model agents choosing from a mock storefront in randomized trials), every selection signal (price, rating, endorsement, and description text) carried a different weight on each model tested, so no single "winning" configuration held across Claude, GPT-4.1, and GeminiReportedACES, Allouah et al., arXiv:2508.02630 (2025-12-17). Read that as evidence of how engines diverge, not as conversion math for a live store: it is a simulation across a fixed model set, not a record of real purchases. This hub is the platform overview inside the broader agentic commerce optimization guide; the per-engine playbooks below carry the specifics.
Where engines differ (the weights)
If the signals are shared, the divergence lives in how hard each engine leans on them, and that is where per-engine work earns its keep. The selection chapter reports the full ACES breakdown model by model; the short version is that the gaps are large enough to matter. One clear example: platform endorsement is a lever on every model, but not equally. A platform endorsement such as an "Overall Pick" badge lifted a baseline 10% selection probability to 24.3% for a Claude agent, 19.9% for GPT-4.1, and 42.6% for Gemini, with Gemini rewarding endorsement most stronglyReportedACES, Allouah et al., arXiv:2508.02630 (2025-12-17).
The takeaway is directional, not a dial you set: the same honest signal buys more selection lift on some engines than others, which is exactly why there is no universal "top rank" to aim for and why a per-engine agent win rate is the number to watch rather than a single blended one. Get the shared inputs right first (feed completeness, product schema, titles, crawler access), then use the playbooks to break ties on the engine that matters most to your catalog.
Which rail reaches which engine
Signals decide whether an agent prefers you; a protocol rail decides how that agent reaches your catalog and completes a purchase. The two rails to know launched behind different engines, and both original mappings are confirmed. The Agentic Commerce Protocol (ACP), announced by OpenAI and Stripe on 29 September 2025, launched as the rail powering ChatGPT's Instant CheckoutSpec-factAgentic Commerce Protocol: OpenAI + Stripe (2025-09-29). The shape of that rail changed within six months: OpenAI scaled the in-chat checkout back by March 2026, routing purchases through merchant sites and apps insteadReportedForbes, Jason Goldberg (2026-03-10), and OpenAI's merchant documentation now describes ChatGPT shopping as discovery-first: ACP product feeds decide how products appear in results, and customers complete their purchase on the merchant's own site or app, with no fees on purchases that start in ChatGPTSpec-factOpenAI, ChatGPT for merchants. So for ChatGPT, ACP remains the confirmed rail, now carrying discovery and product feeds rather than an in-chat purchase.
Google has put its side on the record too. Google announced the Universal Commerce Protocol (UCP) at NRF 2026 on 11 January 2026 as an open standard for agents to complete discovery, checkout, and post-purchase without leaving the AI surfaceSpec-factUniversal Commerce Protocol: Google, NRF 2026 (2026-01-11), and Google's own UCP developer guide states that adopting UCP enables agentic actions on AI Mode in Google Search and Gemini, starting with direct buyingSpec-factGoogle: Universal Commerce Protocol developer guide, naming Gemini and AI Mode as UCP's consuming surfaces directly.
The one-rail-per-engine picture then stopped holding in 2026, because Microsoft Copilot consumes both rails. Microsoft launched Copilot Checkout on 8 January 2026, letting shoppers complete a purchase inside Copilot, with merchants onboarding through PayPal or Stripe and launch brands including Urban Outfitters, Anthropologie, Ashley Furniture, and Etsy sellersReportedMicrosoft Source (2026-01-08), and Stripe states that Copilot Checkout runs on the Agentic Commerce Protocol, with a Shared Payment Token issued to the merchant, who remains the seller of recordReportedStripe newsroom (2026-01-08). On the discovery side, Microsoft announced on 21 April 2026 that UCP-ready feeds in Microsoft Merchant Center were generally available to everyone in the US, and that Copilot Checkout had expanded to more than 500,000 merchants and to mobileReportedMicrosoft Advertising blog (2026-04-21). Microsoft's agentic commerce documentation describes UCP as a complementary layer over Merchant Center feeds, says Copilot surfaces products from both the open web and a merchant's Merchant Center feed, and limits Copilot Checkout to English-language merchants shipping within the US as of that documentationSpec-factMicrosoft Advertising: Agentic Commerce. The practical reading for a merchant: the same two rails now cover three engines, so protocol work you do for one ecosystem increasingly pays out in another. The full protocol comparison (what each rail covers, and which one to implement) lives in UCP vs ACP: which gets you selected.
Per-engine playbooks
Once the shared inputs are in place, these four pages cover what each engine does differently.
- ChatGPT Shopping Optimization. The product-data signals ChatGPT reads over the ACP rails, and why there is no "enroll" button to flip.
- Why you're not in ChatGPT Shopping. The diagnostic order when your products are missing: crawler blocks, feed gaps, title mismatches.
- Gemini & Google AI Mode shopping ranking. How AI Mode ranks through your Merchant Center feed and the UCP rail, where feed quality is the lever.
- Perplexity Shopping Optimization. The signals and sources Perplexity leans on, and how it differs from the other two.
Behavior changes on updates
Optimizing to a single engine is fragile because the engine itself moves. ACES documented that a model update can drastically reshuffle market shares, as it did between Gemini 2.5 Flash Preview and Gemini 2.5 FlashReportedACES, Allouah et al., arXiv:2508.02630 (2025-12-17). What an agent rewards this quarter it may weight differently after the next release, which is why AgentMint.net types and dates every model-behavior claim rather than presenting it as a fixed rule.
Agent behavior changes with every model update. Last verified: 2026-08-20. Treat the per-engine playbooks as snapshots, re-check them against your own measured agent win rate on a cadence, and weight the shared inputs (which are stable across updates) over any single engine's current quirks.