# Agent Selection Updates > AgentMint.net Updates is a dated log of changes to AI agent shopping selection, research findings, and merchant-facing protocols, with its own RSS feed. 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](/updates/rss.xml) to get each entry as it lands.

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](/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](/ucp-vs-acp/) 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](/chatgpt-shopping-optimization/) and its [troubleshooting sibling](/store-not-in-chatgpt-shopping/) 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](/reference/ai-shopping-crawlers/) 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](/tools/), every [blueprint](/blueprints/), the [AI shopping crawler reference](/reference/ai-shopping-crawlers/), 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](/tools/coding-agent-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](/tools/) 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](/signals.json), the machine-readable export of the [Signals Database](/signals/), alongside [llms.txt](/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](/blueprints/), the [Agent Legibility Analyzer](/tools/agent-legibility-analyzer/), and this updates log) are reachable directly from any page. Four new pages ship with it: - **[The Agent Selection Handbook](/handbook/)**: the handbook's table of contents in reading order. - **[Merchant Tools](/tools/)**: both checklists and the analyzer in one place. - **[Reference](/reference/)**: the glossary, the AI shopping crawler reference, protocol comparisons, and the blueprints. - **[All Pages](/sitemap/)**: 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](/handbook/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](/handbook/information-density/), 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](/tools/agent-legibility-analyzer/) 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](/blueprints/) 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](/research/agent-selection-index/) 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. 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. A live re-verification caught that three AgentMint.net pages, the [description-rewrite experiment](/research/description-rewrite-experiment/), [how AI agents choose products](/how-ai-agents-choose-products/), and [product titles for AI agents](/product-titles-for-ai-agents/), were still citing the superseded v2 figures. All three were corrected today to the current v3 numbers.