# Agentic Commerce Glossary

> This glossary gives plain-English, sourced definitions for the agentic-commerce stack, each term labeled by evidence type: spec-fact, reported, or our framing.

<!-- Plain-English, sourced definitions for the agentic-commerce stack (agent selection, win rate, AEO, UCP, ACP, product feed), each typed by evidence. -->
<!-- Canonical: https://agentmint.net/glossary/ · By Abdalsalaam Halawa · Published 2026-07-07 -->
<!-- Evidence tags below are load-bearing: <Claim type="spec-fact|reported|hypothesis"> marks how each factual claim is sourced. -->

Agentic commerce has produced a thicket of overlapping terms: some are open protocol names with fixed, checkable meanings, some are vendor coinages, and some (including a few of ours) are framings for things no official spec has named yet. This page keeps them straight and honest: every entry carries its evidence type inline, links its primary source where one exists, and points to the chapter of this handbook that goes deep. Definitions marked *our analysis* are inferences we are accountable for, not established fact, so you can weigh them accordingly.

## Terms (A-Z)

**ACP: Agentic Commerce Protocol**

An open standard for agent-driven checkout and product feeds, from OpenAI and Stripe. <Claim type="spec-fact" source="Stripe + OpenAI, Instant Checkout announcement (2025-09-29)" href="https://stripe.com/newsroom/news/stripe-openai-instant-checkout">OpenAI and Stripe announced ACP on 29 September 2025 as the rail behind ChatGPT's in-chat Instant Checkout</Claim>; <Claim type="spec-fact" source="OpenAI, ChatGPT for merchants" href="https://chatgpt.com/merchants" date="2026-08-20">OpenAI has since moved away from a standalone Instant Checkout experience, so ACP feeds now power discovery in ChatGPT while purchases complete on merchant sites</Claim>, and <Claim type="reported" source="Stripe newsroom" date="2026-01-08" href="https://stripe.com/newsroom/news/microsoft-copilot-and-stripe">ACP also carries Microsoft's Copilot Checkout, launched January 2026 with Stripe</Claim>. It governs *eligibility to transact*, not who gets recommended. The selection layer is where this handbook lives. We link the spec rather than re-document it: [UCP vs ACP](/ucp-vs-acp/).


**Agent selection**

<Claim type="hypothesis">The choice an AI shopping agent makes when it picks one store, product, or offer over the alternatives it could have returned, the outcome this entire handbook is about influencing</Claim>. No official protocol defines or governs it; the protocols handle checkout and payment, while each AI platform runs its own, undocumented selection process. That gap is why we treat selection as its own discipline. The mechanics: [how AI agents choose products](/how-ai-agents-choose-products/).


**Agent selection maturity model**

<Claim type="hypothesis">Our five-level editorial framework for sorting how ready a store is to be chosen by AI shopping agents, ascending from level 1 (unreachable: blocked crawlers, no feed) through eligible, legible, and preferable to level 5 (measured: running the win-rate protocol and iterating)</Claim>. It is a way to prioritize work, not a measured or validated scale, and it carries no outcome numbers. Each level maps to specific Agent Selection Checklist categories and selection signals, so a level reading becomes a concrete next task. The full model: [the agent selection maturity model](/handbook/agent-selection-maturity-model/).


**[Agent win rate](/glossary/agent-win-rate/)**

<Claim type="hypothesis">Our proposed metric for agentic commerce: the share of relevant agent queries in which your product or store is the one selected, the agentic-era analogue of an ecommerce conversion rate</Claim>. Because agents weight signals differently and reshuffle on model updates, we argue it is best tracked engine by engine rather than as one blended number. Full definition and how to measure it: [Agent win rate](/glossary/agent-win-rate/).


**Agentic commerce optimization (ACO / AEO)**

<Claim type="hypothesis">The practice of structuring a catalog, feed, and product pages so AI agents can find, parse, and prefer your products, the agentic-commerce counterpart to SEO; "answer engine optimization (AEO)" and "agentic commerce optimization (ACO)" are used near-interchangeably as industry coinages, several of them vendor-defined, with no standards body behind them</Claim>. <Claim type="reported" source="Scot Wingo / ReFiBuy, Retailgentic" date="2025-10-21" href="https://www.retailgentic.com/p/part-iiiaiii-aco-for-retailers-brands">The specific term "agentic commerce optimization (ACO)" was introduced in Scot Wingo / ReFiBuy's Retailgentic series over September-October 2025</Claim>, but attribution is not settled: <Claim type="reported" source="Search Engine Journal, Alex Moss" date="2026-02-23" href="https://www.searchenginejournal.com/agentic-commerce-optimization-a-technical-guide-to-prepare-for-googles-ucp/566969/">a Search Engine Journal column floats "ACO" independently with no credit to any originator</Claim>, and <Claim type="reported" source="Paz.ai glossary" date="2026-04-20" href="https://www.paz.ai/glossary/agentic-commerce-optimization-aco">vendor glossaries define it with no named originator</Claim>. <Claim type="reported" source="Accountable Care Organization, Wikipedia" date="accessed 2026-07-08" href="https://en.wikipedia.org/wiki/Accountable_care_organization">The bare acronym is also ambiguous: "ACO" is dominantly the US-healthcare term "Accountable Care Organization", a concept from 2006 that the Affordable Care Act enacted in 2010</Claim>. <Claim type="hypothesis">We prefer a sharper, non-colliding pair: agent selection for the winning question, and machine-readability, or legibility, for the substrate underneath it</Claim>. Both have their own entries here: [agent selection](/glossary/#agent-selection) and [machine-readability score](/glossary/#machine-readability-score). We use ACO and AEO descriptively and stay skeptical of any vendor's specific weightings until they are evidenced. The handbook's take: [agentic commerce optimization](/agentic-commerce-optimization/).


**AI crawler (GPTBot, PerplexityBot, etc.)**

An automated bot that fetches web pages on behalf of an AI system: to train a model, to build a search or answer index, or to fetch a page live in response to a user's query. <Claim type="spec-fact" source="OpenAI, Bots documentation" href="https://developers.openai.com/api/docs/bots">OpenAI documents distinct agents for these jobs: GPTBot (crawls content to train models), OAI-SearchBot (surfaces and links sites inside ChatGPT), and ChatGPT-User (the fetch triggered by a user's own request)</Claim>. The distinction matters for stores, because blocking the wrong token can hide you from the agents that decide whether a shopper sees you. Which to allow, and why: [AI crawlers, robots.txt & llms.txt](/ai-crawlers-robots-llms-txt/).


**AP2: Agent Payments Protocol**

An open protocol for authorizing and proving agent-initiated payments, so a purchase an agent makes carries a verifiable, accountable trail. <Claim type="spec-fact" source="Google Cloud, Announcing AP2 (2025-09-16)" href="https://cloud.google.com/blog/products/ai-machine-learning/announcing-agents-to-payments-ap2-protocol">Google announced AP2 on 16 September 2025; it is payment-method-agnostic and carried signed Intent, Cart, and Payment mandates at launch</Claim>, <Claim type="spec-fact" source="AP2: ap2-protocol.org" href="https://ap2-protocol.org/ap2/specification/">since consolidated to Checkout and Payment mandates in the current spec</Claim>. Like the other rails, it settles *how a purchase clears*, not who gets chosen. Compared with the other protocols: [agentic payment protocols](/agentic-payment-protocols/).


**Choice homogeneity (modal product)**

The tendency of AI shopping agents to concentrate their picks on a small set of "modal" products in each category while ignoring other options, and some brands, entirely. <Claim type="reported" source="ACES, Allouah et al., arXiv:2508.02630" date="2025-12-17" href="https://arxiv.org/abs/2508.02630">In the ACES simulation, agents can exhibit choice homogeneity, often concentrating demand on a few "modal" products while ignoring others entirely</Claim>. This is a controlled simulation across a fixed model set, not live sales, so read it as a pattern to watch rather than a measured market share. What it means for whether your product becomes the modal pick: [how AI agents choose products](/how-ai-agents-choose-products/).


**delegated payment token**

In the Agentic Commerce Protocol, a single-use, allowance-constrained payment credential an agent hands to a merchant's payment provider to charge a specific cart, without the agent ever touching raw card data. <Claim type="spec-fact" source="ACP Delegated Payment Spec, developers.openai.com" href="https://developers.openai.com/commerce/specs/payment">The delegated payment payload is sent to the merchant's PSP or vault directly, is single-use and set with allowances, and the PSP or vault returns a payment token scoped to that delegated payment, keeping it outside PCI scope</Claim>. This is the spec-level concept; [Shared Payment Token](/glossary/#shared-payment-token) is Stripe's named implementation of it. Where it fits among the rails: [agentic payment protocols](/agentic-payment-protocols/).


**GEO: Generative Engine Optimization**

The practice of structuring content so it surfaces inside an AI or generative engine's answer rather than a ranked list of links; the generative-answer counterpart to SEO, coined by the 2023 research paper of the same name. <Claim type="reported" source="GEO: Generative Engine Optimization, Aggarwal et al., arXiv:2311.09735" date="2023-11-16" href="https://arxiv.org/abs/2311.09735">The 2023 paper introduced "Generative Engine Optimization (GEO)" as a framework for improving content visibility inside generative-engine responses, and reports it can lift visibility by up to 40% on the authors' own benchmark</Claim>. That 40% is a result on the paper's own benchmark, not a measured effect on any live shopping engine. GEO answers the same substrate question our [agentic commerce optimization](/glossary/#aco-aeo) framing does; we keep it distinct from the winning question of [agent selection](/glossary/#agent-selection). The handbook's take: [agentic commerce optimization](/agentic-commerce-optimization/).


**Google Merchant Center**

Google's free tool where a merchant uploads and manages product data so those products become eligible to appear across Google's surfaces (Search, Shopping, Maps, YouTube) and, through UCP, agentic checkout. <Claim type="spec-fact" source="Google for Retail, Merchant Center" href="https://business.google.com/en-all/merchant-center/">Merchant Center is a free tool for uploading and managing product data so products appear across Google Search, Maps, YouTube, and more</Claim>. It is an eligibility substrate: it feeds your catalog into Google's [Shopping Graph](/glossary/#shopping-graph) and, through [UCP](/glossary/#ucp), its agentic checkout, but it does not decide which product an agent recommends. Getting the feed itself chosen is the work: [make your product feed AI-readable](/make-product-feed-ai-readable/).


**llms.txt**

<Claim type="spec-fact" source="llms.txt spec, llmstxt.org" href="https://llmstxt.org" date="2026-08-20">A public spec, published at llmstxt.org and revised to v2 in August 2026, for a root-level /llms.txt Markdown file that hands a large language model a curated, easy-to-parse map of a site's most important content</Claim>. <Claim type="hypothesis">There is no confirmed public evidence yet that ChatGPT, Gemini, or Perplexity read it when choosing products, so treat it as low-cost insurance rather than a selection lever</Claim>. What to ship, and the honest caveats: [AI crawlers, robots.txt & llms.txt](/ai-crawlers-robots-llms-txt/).


**Machine-readability score**

A per-page grade for how much of a page an AI agent can read as structured data instead of inferring from layout. <Claim type="reported" source="Adobe Digital Insights, Vivek Pandya" date="2026-04-16" href="https://business.adobe.com/blog/ai-traffic-surge-retail-sites-not-machine-readable">Retail product pages score an average of 66 out of 100 on Adobe's machine-readability index (meaning roughly a third of a typical product page is not cleanly machine-readable), versus 74 for category pages and 75 for homepages</Claim>. Read it as a page-completeness score, not a share of pages. Closing the gap: [make your product feed AI-readable](/make-product-feed-ai-readable/).


**Platform-endorsement signal**

A store- or platform-issued mark that a product is endorsed (an "Overall Pick" badge, a "Choice" label, or similar), which agents tend to reward. <Claim type="reported" source="ACES, Allouah et al., arXiv:2508.02630" date="2025-12-17" href="https://arxiv.org/abs/2508.02630">In the ACES simulation, an "Overall Pick" endorsement lifted a baseline 10% selection probability to 24.3% for a Claude agent, 19.9% for GPT-4.1, and 42.6% for Gemini</Claim>. This is a controlled simulation across a fixed model set, not live sales. The winning move is to *earn* legitimate endorsements, not fake the badge; the mechanics are in [how AI agents choose products](/how-ai-agents-choose-products/).


**Position bias**

An agent's tendency to favor a product because of where it appears in the layout the agent sees, independent of the product's merits. <Claim type="reported" source="ACES, Allouah et al., arXiv:2508.02630" date="2025-12-17" href="https://arxiv.org/abs/2508.02630">In the ACES simulation, moving a product from the bottom-right corner to the top row raised a Claude Sonnet 4 agent's selection rate roughly fivefold; the effect varied by provider and persisted even in text-only interfaces</Claim>. This is a simulation, not live sales, and evidence that there is no universal "top" rank to chase. How it plays out per engine: [how AI agents choose products](/how-ai-agents-choose-products/).


**Product feed**

A structured data file (one row per product, each carrying attributes such as title, price, availability, GTIN, image, and description) that a merchant supplies so shopping surfaces read the catalog as data rather than scraping the page. <Claim type="spec-fact" source="Agentic Commerce Protocol schema, github.com/agentic-commerce-protocol" href="https://github.com/agentic-commerce-protocol/agentic-commerce-protocol/blob/main/spec/2026-04-17/json-schema/schema.feed.json">A machine-readable product feed is part of the ACP open standard, which enumerates the core attributes an agent reads, including identifiers such as GTIN, price, and availability</Claim>. Getting a feed *seen* is eligibility; getting it *chosen* is the work: [make your product feed AI-readable](/make-product-feed-ai-readable/).


**Shared Payment Token**

Stripe's single-use, merchant-and-amount-scoped payment credential for agent-initiated purchases; the first shipping implementation of the Agentic Commerce Protocol's Delegated Payment Spec. <Claim type="spec-fact" source="ACP Delegated Payment Spec, developers.openai.com" href="https://developers.openai.com/commerce/specs/payment">Stripe's Shared Payment Token is the first Delegated Payment Spec-compatible implementation, with more payment providers stated as coming</Claim>. It is the vendor implementation, not the standard: the protocol-level mechanism it implements is the [delegated payment token](/glossary/#delegated-payment-token). How it compares with the other rails: [agentic payment protocols](/agentic-payment-protocols/).


**Shopping Graph**

Google's machine-learning-powered, real-time dataset of the world's products and sellers, the index that powers product results across Google Search, Shopping, and its AI and agentic surfaces. <Claim type="spec-fact" source="Google, Sundar Pichai NRF 2026 remarks (2026-01-11)" href="https://blog.google/company-news/inside-google/message-ceo/nrf-2026-remarks">Google's Shopping Graph holds over 50 billion product listings, including inventory, prices, and reviews, with more than 2 billion refreshed every hour</Claim>. Being listed there is eligibility to appear on Google surfaces, not a promise of being picked. How Google's agents rank what the graph holds: [Gemini and Google AI Mode ranking](/gemini-shopping-ranking/), and where it sits among the [AI shopping platforms](/platforms/).


**Sponsored-tag penalty**

The observed pattern where an agent selects a product *less* often when it is labeled "Sponsored." <Claim type="reported" source="ACES, Allouah et al., arXiv:2508.02630" date="2025-12-17" href="https://arxiv.org/abs/2508.02630">In the ACES simulation, a "Sponsored" tag pushed a baseline 10% selection probability down to 8.9% for a Claude agent, 8.0% for GPT-4.1, and 7.9% for Gemini; agents consistently penalized sponsored tags</Claim>. This is a controlled simulation, not live sales. It is the counterintuitive inverse of paid search, and it argues against buying visibility with a badge agents discount; see [how AI agents choose products](/how-ai-agents-choose-products/).


**UCP: Universal Commerce Protocol**

Google's open standard for agents to complete the full commerce journey (discovery, checkout, and post-purchase) without leaving the AI surface. <Claim type="spec-fact" source="Google, NRF 2026 remarks (2026-01-11)" href="https://blog.google/company-news/inside-google/message-ceo/nrf-2026-remarks">Google announced UCP at NRF 2026 on 11 January 2026 as a vendor-agnostic standard compatible with A2A, AP2, and MCP</Claim>. It is "Universal *Commerce* Protocol," not "Universal Cart" (Google's consumer cart product), and it governs the transaction rail, not product-presentation logic. Side by side with ACP: [UCP vs ACP](/ucp-vs-acp/).