Business-to-Agent readiness
ChatGPT, Claude and Perplexity help customers research products. Your store needs to give them reliable information. BtoAgent helps you audit your store, review agent access and make product information machine-readable.
Built for the agents that buy
An agent may read your pages, feeds and JSON-LD: name, price, stock and returns. Inaccessible or conflicting facts make product comparison and buying decisions harder.
Definition · B2A (noun)
Business-to-Agent: the commercial model in which businesses are discovered by, serve, and transact with autonomous AI agents acting for a customer, not humans browsing directly. It extends B2B and B2C to a new buyer: the agent.
Selling to businesses. A human buyer, a sales motion, a signed contract.
Selling to consumers. A human browses, is persuaded, and checks out.
Selling through agents. Software reads data, compares options and may buy within the customer's authorization.
Agents evaluate you by reading raw data, not visual design. Each pillar is a set of checks BtoAgent audits, scores and fixes.
Consistent product pages and feeds: name, price, stock, real identifiers, delivery and returns. JSON-LD should reflect the visible facts for the exact product.
Review CDN rules and supported agent verification. A recognized identity still needs the right permissions and access limits.
Evaluate supported agent-assisted payment and tokenization with your PSP. Customer consent, amount limits, authentication and failure handling still matter.
An optional map to your catalog, policies and FAQ. llms.txt complements public content; it replaces neither robots.txt nor security controls.
Publish clear, sourced facts that help answer engines understand your offers. No markup guarantees a citation or ranking.
An approach based on HTTP signatures to help verify agent identity. Verification depends on the supported protocol and infrastructure; it does not replace access authorization. BtoAgent reviews configuration on your infrastructure and follows the evolving specifications.
CDN access, JSON-LD, cart building and payment compatibility: we assess the agent-assisted buying journey, identify obstacles and prioritize fixes. Scope and timing are confirmed during the demo.
Free 30-minute call. We walk your store's agent-readiness live.
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Illustration: 12 prioritized fixes
AI answers may draw on price, stock, delivery and return information. Clear, consistent sources make those facts easier to understand, without guaranteeing a citation.
We help make catalog facts readable, consistent and connected to their sources. Traditional search visibility and answer quality work together.
Business-to-Agent: an AI agent helps a customer research, compare or make an authorized purchase. Readiness starts with accessible, reliable data and testing the purchase flows actually supported.
Agent-assisted purchase flows exist, but availability depends on the platform, country, merchant and payment provider. Verify the actual flow rather than assuming universal compatibility.
An agent authentication approach based on HTTP signatures. A valid signature helps verify identity; it does not automatically authorize a purchase or access to private information.
Not necessarily. Early fixes often concern structured data, feeds and access rules. Payment or platform changes depend on your current setup.
Timing depends on the catalog, available access and scope. We confirm it before starting. The report should separate verified findings, unknowns and prioritized fixes.
You apply the plan, or we help. Fixes are then retested against the same scope, with evidence and named owners.
See your store through an agent's eyes, live, with your fix plan.
Free 30-minute demo · No commitment
Agentic commerce, explained
Structured data, agent access, SEO, AEO and payments: practical answers for preparing an ecommerce site for B2A.
Business-to-Agent describes commerce where software agents help customers discover, compare or buy products. A useful starting point is accurate product information, public buying policies and a supported checkout with clear customer authorization.
Check whether a public product page exposes the exact variant, current price, currency, stock and delivery conditions in readable text. Keep JSON-LD and merchant feeds consistent with that page. Then test access without weakening private account or payment controls.
Product and Offer structured data can describe an item and its offer. Required properties depend on the target search experience. Use real identifiers, validate the deployed page and make the markup agree with visible facts. Eligibility is not a guarantee of rich results.
Start by reviewing the actual theme, product-data output, feed publishers, apps and payment provider. Check for duplicate or conflicting markup. Compatibility depends on the installed stack; a platform name alone does not establish support for an agent payment flow.
SEO concerns how search engines discover, understand and present pages. AEO is commonly used for work that makes content useful in generated answers. These overlap: clear sources, accessible text and accurate facts help both. No special markup guarantees an AI recommendation.
llms.txt is a proposed Markdown guide to useful site content. It can be a maintained discovery aid, but it is not a ranking guarantee. Google says its AI search features do not require special AI text files or special structured data.
No. Decide which public content should be readable and which routes require authentication or authorization. Review CDN and WAF rules with logs. A verified identity can inform policy, but it should not remove rate limits or expose customer information.
Web Bot Auth uses HTTP message signatures to help verify a bot identity through a supported implementation. Header presence alone is not verification. The protocol and provider version matter, and a valid signature is not customer consent to place an order.
Some supported payment ecosystems use tokenized credentials in agent-assisted flows. Actual availability depends on the provider, market and onboarding. A token replaces sensitive card details; the purchase still needs customer authority, authentication where required and duplicate-payment controls.
Define a sample of products and a buying journey. Check data consistency, agent access, supported checkout, public discovery and answer quality. Keep evidence, unknowns and critical blockers beside any score. The 38/100 shown above is an illustration, not an audit of your site.
Keep a fixed set of customer questions. Save the exact answer, platform, locale, date and cited sources. Check product accuracy and policy caveats, then repeat after changes. Treat these tests as diagnostics, not a promise of future citations or revenue.
No. robots.txt communicates crawl preferences and llms.txt guides discovery. Neither is account security. Private customer, order and payment information must remain protected by the application's authentication and authorization controls.
Further reading: Google Product · Google AI Search · Web Bot Auth · llms.txt
