Schema Markup

Featured image for blog titled "How Website Complexity Can Undermine Your Brand in AI", depicting AI filling in an incomplete puzzle, meant to represent AI making an inference about a brand.

How Website Complexity Can Undermine Your Brand in AI

AI is exposing the longstanding Enterprise Website Complexity Problem Enterprise complexity has become a business risk in the age of AI. Enterprise websites grow through acquisitions, new product launches, CMS…
Featured image for blog titled "The Modern Data Stack for the Agentic Web" depicting an AI agent accessing and working off of the agentic web data stack.

The Modern Data Stack for the Agentic Web

This article is part 4 of a 5-part series from Schema App CTO Mark van Berkel exploring the shift from Schema Markup to Content Knowledge Graphs, governed entity data, and…
Get practical guidance on Schema Markup, entity SEO, content knowledge graphs, and AI search from our free educational resources.

Key Takeaways

  • The agentic web requires more than better prompts. It depends on a layered, machine-readable infrastructure built on structured, reliable data. Without that foundation, even the best prompts cannot overcome poor data quality.
  • The Modern Data Stack has nine layers in three groupings: trust (foundation, graph, governance), precision (entity resolution, vector, retrieval), and agentic capability (conversational, action, analytics). Each layer depends on the one below it, which is why most AI search advice solves the wrong layer.
  • The competitive frontier is the action layer. Brands that publish machine-readable Agentic Entry Points stop being merely discoverable and become callable, which is the structural advantage agentic AI systems will reward.