This page is a dated log of the changes that matter for agent selection: shifts in how AI shopping agents choose products and stores, updates to the protocols merchants need to track, and corrections we make to our own research pages when a primary source changes. Each entry is dated by when the change became true or was caught here, not by when the underlying source itself was published. Subscribe to the RSS feed to get each entry as it lands.
Ads arrive in ChatGPT shopping, OpenAI publishes its ranking factors, and the research lab closes
Protocol change · September 18, 2026
Two things landed on the ChatGPT shopping surface at once, and they pull in opposite directions. OpenAI began testing Sponsored Agents on September 16, a limited alpha with selected US advertisers that lets a shopper open a labelled conversation with a business after clicking an ad. At the same time, OpenAI's shopping help article states plainly that product results are selected independently, are not ads, and are not influenced by any OpenAI partnership. The ChatGPT playbook now carries both, scoped as the alpha it is rather than a rollout anyone can buy into.
The same help article turned out to be the most valuable source of this refresh. It publishes what ChatGPT weighs when choosing products, and separately what it weighs when ranking merchants: availability, price, quality, and whether you are the maker or primary seller of the item. That last factor is one a reseller cannot edit its way out of, and it is now a signal in its own right. The article also settles something the titles guidance had to assume: ChatGPT rewrites merchant titles and descriptions into simplified versions, so on that surface your title is an input to a rewrite rather than the string a shopper reads.
There is a new page on sponsored placement and agent selection, because the research and the product change deserve to be read together. A 2026 choice experiment finds that agents penalise sponsored listings, and that the penalty shrinks when the agent is told it works for the platform rather than the shopper. Disclosure labels written for human consumers now reach a model instead, and how that model weighs them depends on whose interest it thinks it serves.
The infrastructure change is the one most likely to be silently costing someone traffic today. On September 15 Cloudflare set new defaults: for domains newly onboarding, its Agent and Training classes are blocked by default on pages that display ads, and multi-purpose crawlers are judged by every behaviour they carry, so blocking training can take Googlebot, Applebot and Bingbot with it. Verified status no longer means allowed by default. The serving agent traffic chapter now walks through checking what your own CDN blocks, and the crawler reference carries Cloudflare's behaviour classes, including a Transact class for checkout actions taken on a shopper's behalf, plus a full row for OpenAI's ad-safety crawler.
On the protocol side, the tidy picture of one rail per engine has finished collapsing. UCP shipped a dated release on August 25 with breaking schema changes to fulfillment, consent and signing keys, and it now runs under a consortium whose technical committee includes Amazon, Meta and Microsoft. Google still runs the AI Mode and Gemini implementation, so both things are true at once, and the comparison says so. Google also made its Merchant Center AI performance insights generally available, which gives merchants a share-of-voice number against named competitors. It measures being shown rather than being chosen, and the Gemini playbook draws that line explicitly.
Two more surfaces joined the map. Shopify turned on WebMCP by default across Liquid storefronts, so an in-browser agent can call a store's tools instead of parsing its pages, which is the subject of a new chapter on storefronts that answer tool calls. Anthropic published an open reference implementation of a shopping agent that hands off to the merchant's own checkout. And for anyone running a WooCommerce store, there is now an honest status page on what actually ships today, including the distinction between WooCommerce's store-management MCP integration and a shopper-facing rail, which are not the same thing.
Finally, a subtraction. The research lab is gone. It published a methodology for a quarterly benchmark and never published a number, and a results page that waits indefinitely is a promise, not a publication. The measurement protocol survives as a chapter you can run yourself, how to test agent selection, and the register of what remains unknown moved to open questions. Every old address still works. This site now runs no experiments of its own and publishes no measured claims, which is what was already true.
Copilot's dual rails, llms.txt v2, new payment rails, and fresh selection research
Protocol change · August 20, 2026
The agentic-commerce map moved over the summer, and the site's coverage moved with it. The timeline now records the biggest shift: Microsoft Copilot became the second major surface on both rails, launching Copilot Checkout on the Agentic Commerce Protocol with Stripe in January and taking UCP-ready Merchant Center feeds to US general availability in April. UCP itself grew Cart, Catalog, and Identity Linking capabilities, and Google announced Universal Cart plus checkout expansion beyond the US. The clean one-rail-per-engine picture in the UCP vs ACP comparison no longer holds, and the coverage now says so.
The single biggest correction is on the ChatGPT side. OpenAI moved away from its standalone in-chat Instant Checkout around March 2026, and its merchant documentation now describes shopping in ChatGPT as discovery, with checkout completed on the merchant's own site or app. The ChatGPT playbook and its troubleshooting sibling now carry that dated status, which makes the product feed, not an in-chat buy button, the surface to win.
The payment layer thickened too: American Express debuted Agentic Commerce Experiences with a consumer protection commitment for registered-agent purchases, and Adyen announced an API suite, in limited availability at launch, that spans UCP, AP2, and ACP in one integration, both now dated on the timeline alongside Visa's pilot progress.
Two machine-surface updates matter for anyone who followed our implementation guidance. The llms.txt specification moved to v2, which removes the old Optional-section semantics and blesses Markdown link relations for discovery. And the AI shopping crawler reference picked up Google changes: Google-Extended's scope is now stated as Gemini model training, Vertex AI agent work moved to the new Google-CloudVertexBot token, and Google is experimenting with cryptographic agent verification through Web Bot Auth.
The evidence base also grew beyond ACES: a peer-reviewed Marketing Science study of ChatGPT referral traffic to e-commerce sites, a Microsoft Research marketplace simulation that found large first-proposal and response-speed advantages, and the ABxLab testbed, from a team including MIT Media Lab researchers, showing agent choices shift predictably under price, rating, and nudge manipulation.
Site update · July 9, 2026
Most of this site is written to be read. Now the parts meant to be acted on can be handed to a coding agent instead. Both merchant checklists, every blueprint, the AI shopping crawler reference, and the implementation-oriented handbook chapters carry a "Run this with your coding agent" block: a short, copyable prompt you paste into any coding agent that can fetch a URL.
The prompt does the setup for you. It tells the agent to fetch the page's Markdown mirror first, detect whether your store is Shopify, WooCommerce, Magento, BigCommerce, or custom, and work through the page item by item. The checklists give you two prompts: an audit-only one that reports against every item and changes nothing (run this first), and an audit-and-fix one that implements the fixes you approve. Every prompt carries the same hard rule: the agent never invents your product data, no made-up GTINs, prices, or delivery windows, and it lists exactly what you need to supply where something is missing.
There is a short walkthrough of the whole workflow, including what a good audit report looks like. It works with any coding agent that can fetch a URL; review everything your agent changes before you ship it.
Article table of contents on every long-form page, and a redesigned footer
Site update · July 9, 2026
Long-form pages (the handbook, platform playbooks, research pages, reference, blueprints, comparisons, and both merchant checklists) now carry an "On this page" table of contents generated from each page's headings. On wide screens it sits beside the article and highlights the section you are reading as you scroll; on phones it is a collapsed block above the content; with JavaScript off it is a plain list of anchor links. The two merchant checklists use their category sections as entries, so the long self-assessments gained section navigation too.
The footer is redesigned around the newsletter signup (new research and model-update notes; no spam; unsubscribe anytime), keeps every section link, and now also links signals.json, the machine-readable export of the Signals Database, alongside llms.txt and the RSS feeds.
Navigation rebuilt: section dropdowns, three new hubs, and an all-pages index
Site update · July 9, 2026
The header menu now carries dropdown panels for each section, so the newer surfaces (the handbook chapters, the blueprints, the Agent Legibility Analyzer, and this updates log) are reachable directly from any page. Four new pages ship with it:
- The Agent Selection Handbook: the handbook's table of contents in reading order.
- Merchant Tools: both checklists and the analyzer in one place.
- Reference: the glossary, the AI shopping crawler reference, protocol comparisons, and the blueprints.
- All Pages: every published page, grouped by section.
The menu works without JavaScript, and every published page stays within two clicks of the home header or footer.
v1.1: two handbook chapters, the Agent Legibility Analyzer, blueprints, and the Agent Selection Index methodology
Site update · July 8, 2026
This release ships the v1.1 surfaces. Each item below links the live page:
- Two handbook chapters: serving agent traffic, on how to afford being open to AI crawlers through caching and edge config rather than blocking them, and information density for agents, on the token economy of machine surfaces plus an anti-cloaking doctrine: the same facts everywhere, machine surfaces at their own linked URLs, never content that varies by user agent at one URL.
- The Agent Legibility Analyzer: a client-side tool that reads a pasted product payload the way a language-model agent would, showing token cost, which decision fields are present, marketing-noise dilution, and a lean view that only removes and reorders. Nothing you paste is transmitted or stored.
- Blueprints: a versioned blueprints collection of copy-paste templates for machine surfaces, an llms.txt for catalogs, a product markdown-mirror, token-efficient product JSON-LD, and an edge-worker config, mirrored in a standalone MIT-licensed examples repo.
- The Agent Selection Index methodology: the full methodology for a planned quarterly benchmark of how AI shopping agents choose under controlled conditions. It ships methodology-complete and numbers-empty on purpose: measured values appear only after a run is generated and author-verified.
ACES description-rewrite figures revised upward in v3
Research · July 8, 2026
The ACES paper's current version (v3, December 2025) reports that a seller-side description rewrite raised average market share by +3.66 percentage points for Claude Sonnet 4, +8.37pp for GPT-4.1, and +14.79pp for Gemini 2.5 Flash, substantially larger than the +2.69pp, +5.64pp, and +4.83pp the same experiment reported in the superseded v2 (October 2025). About 33% of category-model pairs now show a large, statistically significant gain, up from roughly 25% in v2.ReportedACES, Allouah et al., arXiv:2508.02630 (2025-12-17)v3 also tests three newer model generations not covered in v2 (Claude Opus 4.5, GPT-5.1, and Gemini 3.0 Pro Preview). "Office lamp" is the only product category where every one of the six models tested showed a positive, statistically significant gain, ranging from +7.1pp to +80.4pp.ReportedACES, Allouah et al., arXiv:2508.02630 (2025-12-17) A live re-verification caught that three AgentMint.net pages, the description-rewrite experiment, how AI agents choose products, and product titles for AI agents, were still citing the superseded v2 figures. All three were corrected today to the current v3 numbers.