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This article focuses on the critical role of ontologies in building robust agentic AI systems, detailing practical methodologies for automated ontology generation and quality assessment using tools like Neo4j's MCP server. It addresses a central challenge in current AI engineering: moving beyond raw data to structured knowledge graphs that enable reliable agent reasoning without manual intervention.

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Going Meta: A Season of Building (and Grading) Ontologies

Neo4jAugust 27, 20268 min read

We take a look back at the past season of Going Meta, this time with ontologies as the thread running through nearly every episode. This is our recap of Season 3, which Jesús Barrasa and I officially closed out this month. As always, thank you for sticking with us. Our GitHub repository, where we gather the code, queries, datasets and ontologies for each episode, continues to grow alongside the series. We’ve been building with ontologies on this show for years. This season, the rest of the industry caught up. At the AI Engineer World’s Fair this year, one full session (Why Agentic Systems Need Ontologies by Frank Coyle, UC Berkeley) argued that a proper AI agent doesn’t really work without an ontology behind it. We heard versions of that same argument in hallway conversations, unconference sessions, and Slack threads all season. Season 3 is our answer to the follow-up…

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