# 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. 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) An open standard for agent-driven checkout, letting an AI agent complete a purchase without the shopper leaving the chat surface. OpenAI and Stripe announced ACP on 29 September 2025; it powers ChatGPT's Instant Checkout. 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/). 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. 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/). 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. 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/). 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. The specific term "agentic commerce optimization (ACO)" was introduced in Scot Wingo / ReFiBuy's Retailgentic series over September-October 2025, but attribution is not settled: a Search Engine Journal column floats "ACO" independently with no credit to any originator, and vendor glossaries define it with no named originator. 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. We prefer a sharper, non-colliding pair: agent selection for the winning question, and machine-readability, or legibility, for the substrate underneath it. 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/). 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. 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). 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/). An open protocol for authorizing and proving agent-initiated payments, so a purchase an agent makes carries a verifiable, accountable trail. Google announced AP2 on 16 September 2025; it is payment-method-agnostic and carried signed Intent, Cart, and Payment mandates at launch, since consolidated to Checkout and Payment mandates in the current spec. 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/). A proposed convention, published at llmstxt.org, 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. 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. What to ship, and the honest caveats: [AI crawlers, robots.txt & llms.txt](/ai-crawlers-robots-llms-txt/). A per-page grade for how much of a page an AI agent can read as structured data instead of inferring from layout. 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. 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/). 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. 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. 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/). 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. 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. 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/). 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. 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. Getting a feed *seen* is eligibility; getting it *chosen* is the work: [make your product feed AI-readable](/make-product-feed-ai-readable/). Google's open standard for agents to complete the full commerce journey (discovery, checkout, and post-purchase) without leaving the AI surface. Google announced UCP at NRF 2026 on 11 January 2026 as a vendor-agnostic standard compatible with A2A, AP2, and MCP. 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/).