Created on 2025-08-16 04:30
Published on 2025-08-16 05:23
We are entering an era where AI agents are multiplying rapidly, each capable of reading, processing, and acting on information autonomously. But they all share one dependency: access to quality, structured data.
For humans, the situation is similar — valuable content (articles, posts, comments, research) floods in constantly from countless sources, especially social media. You can’t possibly process it all in real time.
As a reader: How do you retain important insights for later reuse and recall?
As a creator or publisher: How do you stand out, build an audience, and sustain engagement when attention is scarce?
Without a way to capture, structure, and control access to knowledge, both readers and creators risk being left behind in the AI-driven information economy.
LLM-powered AI agents can transform unstructured content into functional knowledge bases, which in turn become knowledge graphs via RDF and Linked Data principles. These entity relationship graphs are:
Interconnected: Every fact is linked, making knowledge serendipitously discoverable and easily explorable.
Enrichable: Improve progressively as more information is added.
Queryable: Accessible through natural language, text search, and declarative query languages such as SQL, SPARQL, and GraphQL.
For readers, this means every article, post, or comment you consume can be distilled into a structured, queryable knowledge graph — a long-term memory you can tap on demand.
For creators, these graphs enable serendipitous discovery of your content by both humans and AI agents. They also open up new monetization models: offering subscriptions to specific parts of your knowledge graph or licensing the graph wholesale. This is where fine-grained attribute-based access control (ABAC) comes into play:
Every subscription offer defines exactly what a subscriber can access.
Purchasing an offer automatically generates the corresponding access controls over specific aspects of these knowledge graphs.
Capture — AI agents read the same content you do and create tailored notes
Transform — Notes are converted into RDF-based Knowledge Graphs using Linked Data principles leveraging hyperlinks for entity and relationship naming.
Triangulate — Use document metadata to connect notes with generated Knowledge Graphs.
Enrich — These graphs grow organically as new connections and facts are added, expanding naturally alongside your notes.
Control — Offers define ABAC rules; purchasing an offer automatically creates access controls over the relevant segments of target graphs.
Access — Retrieve exactly what you need, when you need it — via AI Agents using natural language, text search, SQL, SPARQL, or GraphQL.
Leverage — Share, license, or sell access to your knowledge graphs, enabling consumption by both humans and AI agents in line with your business model.
Soon, AI agents will become the primary consumers of content published across the Internet and Web, outpacing human readers. They will scan, query, and recommend items from your knowledge graphs — but only if they have access. In this emerging landscape, your knowledge graphs are no longer just reference points; they become vital fuel for the AI Agent economy.
Owning a high-quality knowledge graph protected by fine-grained, attribute-based access controls means you control a resource that AI agents — and their human beneficiaries — cannot operate without. Those who manage this effectively won’t just be content publishers; they’ll be infrastructure providers for tomorrow’s agent-driven world.
Here are live examples of documents generated through AI-aided note-taking, along with their associated knowledge graphs. These examples illustrate all the points from the How section above — with knowledge graph ACLs intentionally disabled.
The Hidden Cost of Data Silos and How (and If) You Should Tackle Them -- by Colin Hardie
Did Craigslist Kill Newspapers? -- by Rick Edmonds , featuring Craig Newmark