B2A

B2A stands for Business-to-Agent. It is the model where your customer is an autonomous AI agent, such as ChatGPT, Claude, or Perplexity, acting on behalf of a person or company rather than a human browsing your site directly.

B2B sells to businesses and B2C sells to consumers, but both assume a human is reading your pages. B2A assumes the buyer is software that reads structured data and APIs, compares options, and checks out programmatically. Machine-legibility matters more than visual design or persuasion.

Historically, B2A also meant Business-to-Administration (companies dealing with government). In the context of AI and commerce it now overwhelmingly means Business-to-Agent, which is the sense used throughout this site.

Agentic commerce is when AI agents carry out shopping tasks for people, including discovering products, comparing options, and completing purchases, with little or no human involvement. B2A is the business-side view of that shift.

The agent economy describes the growing share of commercial activity, from discovery to decisions to transactions, handled by AI agents instead of humans. Analysts project trillions of dollars flowing through it as adoption grows.

It is an active shift with real infrastructure. Stripe launched an Agentic Commerce Suite and the open Agentic Commerce Protocol, OpenAI and Perplexity added agentic shopping, Shopify shipped agent-facing catalog and checkout tooling, and Gartner projects the majority of B2B purchasing will involve AI agents by 2028.

Expose your key facts as structured data (Product, Offer, AggregateRating, and return and shipping policies), make catalog, cart, and checkout available via APIs, adopt an agentic checkout protocol such as ACP, publish an llms.txt file, allow AI crawlers in robots.txt, and write clear, quotable content answer engines can cite.

Agent-ready means your prices, availability, policies, and content are legible to software without human mediation: readable as structured data and reachable through APIs, so an agent can evaluate and transact with you.

At minimum, Schema.org Product (name, SKU, description), Offer (price, currency, availability), and AggregateRating (rating value, review count). Add hasMerchantReturnPolicy and shipping details, plus BreadcrumbList and Organization sitewide. Missing or broken schema is a disqualifying signal.

Many AI crawlers do not fully execute JavaScript, so anything rendered only on the client can be invisible to them. Serve your key content and structured data in the initial HTML, via server-side rendering or static generation, so agents see it without running scripts.

llms.txt is a plain-text file at your site root that gives AI models a concise, machine-readable map of what you offer and where your key content and endpoints live. It is a low-cost signal that helps agents understand and cite you.

If you want to be discovered and cited by AI answer engines, yes. Allow GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and CCBot in robots.txt. Blocking them keeps you out of the answers those systems generate.

Agents shop with constraints, so expose the fields that answer them: return policy (category, days, fees), shipping and delivery times, and genuine ratings. Accurate, structured trust signals move you from merely readable to recommendable.

Audit your structured data with a schema validator, confirm your key content appears in raw HTML rather than only after JavaScript runs, check that robots.txt allows AI crawlers, and verify you publish a sitemap and an llms.txt. Our get-agent-ready playbook walks through each step.

AEO is the practice of structuring your content and data so AI answer engines and agents can extract it, trust it, and cite it in their answers, rather than simply ranking a page for a human to click.

SEO optimizes for a human scanning a results page and clicking; AEO optimizes for a model parsing content and choosing an answer. SEO wants the click; AEO wants the citation. AEO extends SEO rather than replacing it. See our AEO vs SEO guide for the full method.

No. Clean, fast, well-structured pages help both search crawlers and AI agents. The emphasis shifts from earning clicks to earning citations, but the technical foundations of good SEO still apply.

Answer real questions plainly and early, use clear question-style headings, mark up facts with structured data, keep stable URLs, allow AI crawlers, and aim to be the primary, quotable source rather than a paraphrase of one.

They read machine-legible signals such as price, availability, ratings, and return and shipping policies, then pick the option that best fits the buyer's constraints. If they cannot parse your data, you are excluded from consideration.

Largely yes. GEO and AEO both describe optimizing content so generative AI systems surface and cite it, and the tactics overlap: structured data, clear direct answers, and provable trust.

ACP is an open standard, introduced by Stripe and partners, that lets AI agents and businesses transact programmatically. It includes shared payment tokens so an agent can pay securely on a buyer's behalf.

MCP is an open standard for connecting AI agents to external tools and data sources. It is how an agent can securely reach your systems, such as catalogs, carts, and order status, through a consistent interface.

Yes. With agentic checkout protocols and platform support, for example Stripe's Agentic Commerce Suite and Shopify's agent tooling, an agent can move from discovery to a completed transaction without a human filling in a form.

General assistants like ChatGPT, Claude, Gemini, and Perplexity are adding shopping and research abilities, and commerce platforms are exposing agent-facing catalogs and checkout. Capabilities vary by platform and are expanding quickly.

Yes. Stripe offers an Agentic Commerce Suite and co-authored the open Agentic Commerce Protocol, and Shopify has shipped agent-facing catalog and checkout tooling. If you are on a major platform, adopting its agentic features is the fastest path.

The infrastructure has arrived faster than most businesses have adapted. Agents can already discover and transact, so the constraint is now on the business side: whether your data is readable, your checkout reachable, and your trust provable.

Gartner projects that around 90% of B2B purchases will involve AI agents by 2028, and analysts size automated exchange in the trillions of dollars. Adoption is early but moving fast.

Not overnight, but a growing share of discovery and buying will happen through agents instead of direct clicks. Sites that are legible to agents keep winning recommendations; those that are not lose a channel they cannot see.

Retail and e-commerce, travel, and B2B procurement are early movers, because their catalogs, prices, and policies map cleanly to what an agent needs to evaluate and transact. Any business that can be compared on structured facts is exposed.

If agents cannot read your prices, stock, and policies, they simply leave you out of their answers and purchases. There is no second page to fall back to: you are either in the answer or invisible.