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.
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.