Ontologies Are So Back: Why AI Agents Are Reviving the Semantic Web

AI engineers are rediscovering ontologies to give probabilistic agents deterministic guardrails. At AIEWF 2026, UC Berkeley's Frank Coyle argued LLMs need 'logical guardrails' and defined ontologies as 'data as graphs.' Neo4j CEO Emil Eifrem outlined three ontology layers: business concepts, technical metadata, and agent execution traces. Coyle urged developers to reuse existing ontologies like Schema.org and FOAF—they're already in LLM training data, so you can prompt for them instead of building from scratch. He calls this blend of neural nets and symbolic systems 'neurosymbolic AI.' Kingsley Idehen of OpenLink Software added that ontologies define entity types and relationships, giving language computable context that pairs with LLMs' expressive power.

Why it matters: Latent Space deep-dive from AI Engineer World's Fair, putting ontologies back into the agent architecture conversation. Named speakers and concrete use case keep it from being pure theory. Topic sits at the architecture layer though — narrower resonance than a product or model...