Data Ontology defined: The context layer your AI agents are missing
AI agents hallucinate on bad data. Here's why your data dictionary matters more than your model.
Why it matters
Data ontology—semantic consistency across enterprise data—is emerging as critical infrastructure for reliable agent deployments. Without it, agents make confident mistakes on canonical business terms.
The key facts
8 to knowData ontology as agent reliability engineering
Semantic ambiguity (five definitions of 'revenue') breaks agent reasoning
Context layer needed between agent and data layer
Databricks positioning ontology as production agent prerequisite
Data ontology (semantic definitions) identified as foundational for agent reliability
Problem: agents without shared context hallucinate or misinterpret business concepts across systems
Databricks framing ontology as enterprise AI infrastructure, similar to metadata and governance layers
Published Sept 2026 — positions ontology as an emerging best practice for production agent deployments
Go to the source
Databricksdatabricks.com
Publisher excerpt: Ask five people at the same company what "revenue" means and there's a chance you'll get five different answers...