Structured Data for AI and Agents
Structured data is machine-readable markup — usually schema.org in JSON-LD — that states what a page’s content means: this is an article, this is its author, these are the questions it answers. AI systems use it to extract facts without parsing prose, and it raises the odds a page is cited accurately in a generated answer. For agents it is a first step toward a page that declares not just meaning but capability.
Meaning is not capability
Schema.org tells a machine what a page is about. It does not tell an agent what the organisation can do, who authorised an action, or how to pay. Structured data is the discoverability layer done well; the accountability layer — directory, front door, identity — is where an agent goes from understanding a page to acting through it.
Related standards
Questions
Which schema types matter most for AI?
Article/TechArticle, FAQPage, BreadcrumbList and Organization cover most explanatory content; DefinedTerm helps for glossaries. Mark up what is true, never what is not.
Can structured data be faked?
It can assert anything, which is why answer engines cross-check it against visible content and source reputation. Markup that disagrees with the page hurts more than it helps.
Where this lives in the estate
FlashyOS — every learn entry ships schema.org JSON-LD
Keep reading
By Michael Gord · published 2026-10-04 · part of the Agentic Encyclopedia. Dates are the day of publication; events are cited at their own dates.