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Context, Semantics, and Ontology: A Primer for the Agentic Era
AI agents need more than just raw data; they require a structured context layer and ontology to accurately interpret business metrics.
What: Mehdi Ouazza of MotherDuck details how semantic layers, context layers, and ontologies work together to enable reliable AI agents. The MotherDuck team also released a CLI for managing data pipelines and dashboards from the terminal.
Why it matters: As agents take on more analysis, the quality and structure of supporting metadata become the primary bottlenecks to accurate AI-driven insights.
Decoder
- Semantic layer: A logical mapping that organizes data into familiar business terms (e.g., 'revenue') rather than raw table and column names.
- Ontology: A formal structure that defines the relationships and properties of entities within a domain, helping AI understand how different data points connect.
Original article
Semantic layers define business metrics, context layers provide supporting knowledge, and ontologies describe entities and their relationships. Together, they help AI agents interpret data, but people still need to keep that knowledge accurate and current.
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