Agent Friendly
THE AGENT FRIENDLY JOURNAL

Technical discovery

Inspect the path from a link to a readable article. These guides cover rendering, canonical addresses, metadata, and navigation so a working URL is also a discoverable resource.

Technical discovery

8 guides

Technical discovery1 min read

Can AI agents read a JavaScript website?

Some agents use browsers that execute JavaScript; others retrieve HTML or extracted text. Test the access path your customers use, and make essential public facts available without depending on a successful interactive session.

Technical discovery1 min read

Canonical URLs for blogs: choose one durable address

A canonical URL identifies the preferred address for duplicate or substantially similar content. Keep links, sitemap entries, and canonical tags aligned so readers and crawlers encounter a consistent destination.

Technical discovery1 min read

What belongs in a blog sitemap?

Use a blog sitemap as an inventory of public article URLs you want discovered. It complements navigation; it does not replace links from the blog index and relevant articles.

Technical discovery1 min read

Internal linking for topic clusters: give each link a job

An internal link should help a reader answer the next relevant question. Build topic clusters around that progression, with a useful overview linking to narrower guides and those guides pointing back to the overview.

Technical discovery1 min read

Article schema for a business blog: match the visible page

Article or BlogPosting structured data describes the article that visitors can read. Keep its headline, author, dates, and page identity consistent with the visible content rather than adding claims that exist only in markup.

Technical discovery1 min read

How to find orphan pages in a growing blog

An orphan article has no useful internal route leading to it. New posts can become effectively hidden when a site relies on search filters, a sitemap, or a release announcement as the only discovery path.

How we write these guides

Agent Friendly publishes these guides with AI assistance, using the product’s documented behavior and linked primary sources. Practical checks are our editorial suggestions. Examples are illustrative, not customer case studies. Each guide links its evidence and shows its publication and revision dates.

We distinguish discovery, citations, task completion, and payments. We do not claim measured search demand, ranking gains, or AI citations without a recorded baseline. Found a mistake? Send the exact page and correction.