AI agents can discover products through several paths: search indexes, live web retrieval, direct URLs, public APIs, structured feeds, documentation, and third-party references. There is no single "submit to every agent" switch.
The strongest strategy is a clear public product record supported by traditional web fundamentals and consistent external evidence.
Discovery starts with access#
An agent or its retrieval system needs a reachable page. Check:
- The server returns a successful response.
- Important content is available without authentication.
- Robots rules express the intended crawler policy.
- Canonical tags point to the preferred URL.
- Navigation contains real links.
- The sitemap includes public canonical pages.
- Content is not hidden entirely behind client interaction.
Access does not guarantee ingestion. It removes avoidable barriers.
Entity clarity helps systems connect facts#
A product page should consistently state:
- Product and company name.
- Canonical website.
- What the product does.
- Who it serves.
- Important features and limits.
- Pricing or a path to pricing.
- Documentation and support.
- Ownership and contact information.
The same identity should appear in relevant third-party profiles. Contradictory names, categories, and descriptions make entity resolution harder for people and machines.
Structure makes answers easier to extract#
Use descriptive headings, short answer-first passages, comparison tables when useful, and definitions attached to the correct entity. Put important facts in visible text rather than images.
JSON-LD can describe a product, organization, article, breadcrumb, or dataset. It should represent the visible page accurately. It is not a place to hide additional marketing claims.
Google's introduction to structured data explains how markup helps systems understand page meaning while requiring accurate implementation.
Links supply discovery and corroboration#
Internal links show how the publisher organizes its own information. External links from relevant independent sources help establish that the product exists beyond its homepage.
Useful sources can include customer documentation, editorial articles, software marketplaces, repositories, and maintained directories. The objective is not to manufacture mentions. It is to make accurate references available in the places a researcher would expect.
This is where startup link building and quality directory listings overlap with AI discovery.
Feeds and Markdown can reduce parsing work#
Public JSON feeds, OpenAPI descriptions, RSS, and Markdown representations present the same information with less layout noise. Agents may use them when discovered, but publishers should not assume every system supports every format.
Keep canonical HTML authoritative. Version and maintain alternate representations so they do not contradict the page.
llms.txt is emerging, not magic#
An llms.txt file can summarize a site's purpose and point to important resources. Chrome Lighthouse now documents it as an emerging optional convention. That makes it a reasonable low-cost addition for a content-rich product.
It is not a formal ranking signal and does not override crawler policies, canonicalization, or access control. Treat it as orientation.
Mydentify's llms.txt links public catalogs, intent pages, articles, directory collections, and action boundaries.
Agent cards and skills describe capability#
Some agent ecosystems use well-known files, agent cards, or skills to advertise operations. These documents are most useful when a real service supports the described action.
Do not publish an agent card claiming transactional capabilities that do not exist. Do not treat a skill file as authorization. Safe workflows distinguish read-only research from actions that send data, create accounts, or spend money.
Third-party evidence remains important#
A product's own page is a primary source for features and policies. It is not an independent source for market leadership, customer satisfaction, or comparative superiority.
Agents need corroboration for those claims. Publish customer evidence with permission, earn editorial coverage through useful work, and keep directory profiles accurate. Avoid invented review counts, synthetic testimonials, and circular citations between sites you control.
Measure what you can observe#
Track:
- AI crawler requests by path and response.
- Referral traffic from assistants that provide referrers.
- Search Console queries reflecting product and category discovery.
- Mentions and citations in a defined set of provider prompts.
- Whether cited pages are canonical and current.
- Changes after substantive content or structure updates.
Do not infer a citation guarantee from crawler traffic. A crawl proves retrieval, not recommendation.
The practical order of operations#
- Publish an excellent, accessible product page.
- Clarify the entity and canonical URLs.
- Add relevant structured data and sitemaps.
- Build normal internal links and public documentation.
- Earn accurate third-party references.
- Add feeds, Markdown, llms.txt, and capability files where useful.
- Monitor retrieval and citation behavior without overstating causation.
That sequence is less exciting than an "AI SEO hack," but it creates the durable source material every discovery system needs.
Read why Mydentify was rebuilt for people and agents, traditional SEO vs AI discovery, and what Cloudflare Wallets mean for sellers next.
See How We Checked It
We name the source, record the review date, and separate reported claims from our own checks. Read the methodology and editorial policy before citing a finding or sending a correction.
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