Databricks Genie Ontology bridges general-purpose AI agents with structured enterprise data.
“General-purpose AI agents are good at searching the web, reasoning, and writing code.”
semantic-layer
9 tracked signals on semantic-layer.
Databricks introduces Genie Ontology to give AI agents shared business context
Enterprise AI agents waste effort rediscovering data sources; a shared ontology-based semantic layer fixes this.
“every single time a team has to build an agent, they have to figure out from scratch where the data that they require for that agent to operate, where it sits”
Denodo and Amazon Bedrock AgentCore enable governed agentic access to federated enterprise data via MCP.
Strategy Mosaic exposes a unified semantic layer to AI agents via MCP, keeping business logic out of prompts
“The joins, metric definitions, and per-caller row and column filters live inside Mosaic, not in the agent prompt.”
AtScale exposes a single dimensional semantic model to AI agents and BI tools via MCP, SQL, and XMLA simultaneously
Snowflake semantic views feeding Amazon Quick Sight create a shared governed data layer so AI and BI return consistent answers.
“This leads to trustworthy answers and significantly reduces the risk of AI hallucinations.”
Mercedes-Benz Korea built a trusted, scalable natural-language 'Talk to Data' capability on Databricks by adding a semantic layer.
Data agents still hallucinate despite RAG and MCP, so a semantic foundation is needed for accurate answers.
“talking to the data is the new normal”