Ontologies are re-emerging as a practical data platform layer because AI agents need explicit business meaning, not just schemas or dashboards. Unlike data models, ontologies encode shared concepts, typed relationships, constraints, and limited inference. In enterprise tools, this often appears as typed-edge traversal, semantic layers, or knowledge graphs. High-value deployments still require human-curated semantics, especially where systems can write back and act on decisions.