How do I evaluate semantic layers for AI agents?

Summary

• A semantic layer for agents lives or dies on one thing: does every agent get the same governed, authoritative definition of each metric, or does each surface invent its own logic?
• Insist on define-once, use-everywhere. A metric defined a single time should return the same result through SQL, dashboards, notebooks, and AI agents, with no reimplementation.
• Check that the layer inherits your data platform's governance. Row- and column-level security, lineage, and audit should apply automatically, not be re-created per tool.
• Agents need natural-language mapping. Synonyms, display names, formats, and business descriptions should be part of the metric definition so an agent maps a question to the right number.
• On Databricks, Unity Catalog metric views deliver SQL-native, governed definitions available today, and Genie plus Genie Ontology (gated Public Preview) add inferred context and authority ranking on top.

How do I evaluate semantic layers for AI agents?

Judge a semantic layer by whether it hands AI agents one governed, authoritative definition of every metric, usable on every surface, with the permissions and lineage of your data platform attached. The agent is the highest-risk consumer of your data: when column names are ambiguous, business rules are missing, or a metric is defined three different ways, an agent produces confidently wrong answers at machine speed. Most metric disputes are vocabulary, grain, and time-window disputes, not compute problems, which is why the semantic layer is really a control layer for AI. Evaluate it on governance, reach across surfaces, openness, and how well it maps natural language to authoritative data.

What a semantic layer does for AI agents

A semantic layer translates raw source tables into shared business meaning: the metrics, dimensions, and definitions a company agrees on. It declares each metric once as a governed object, so "revenue" or "active customer" resolves to a single approved calculation everywhere it's queried. For agents, it also carries the vocabulary that maps a plain-language question to the right data, for example knowing that an abbreviation and its spelled-out form mean the same thing, and which team's definition applies in which context.

Key capabilities to evaluate

Criterion	What to look for
Governed, single-definition metrics	One authoritative calculation per metric, declared as a first-class object, so two teams stop reporting two different numbers.
Define-once, use-everywhere	The same definition is queryable from SQL, dashboards, notebooks, agents, and external tools with no reimplementation.
Open, SQL-native semantics	Definitions in standard SQL rather than a proprietary language, so existing tools keep working and models reason over familiar syntax.
Permissions and lineage	Inherits row- and column-level security, attribute-based access control, audit, and end-to-end lineage from the data platform.
Natural-language mapping	Synonyms, display names, formats, and business descriptions are embedded so agents resolve a question to the correct metric.
Consistency, ownership, and domains	Context flows uniformly across surfaces, and metrics are organized into business-aligned domains with named owners.

How Databricks approaches the semantic layer for agents

On Databricks, the semantic layer is delivered through Unity Catalog metric views: governed objects that separate what you measure from how you slice it. A metric view declares fact tables, dimensions, and measures, and computes aggregations at query time so non-additive measures stay correct. What makes it work for agents is agent metadata: comments, display names, synonyms, and formats that a model uses to map language to the right field. See agent metadata for the specifics.
Because metric views live in Unity Catalog, they inherit its permissions, lineage, and audit automatically, and certified tags signal which definitions are trusted. Definitions are written in SQL, so existing tools keep working and answers stay consistent whether the caller is a dashboard or an agent. Genie, through Genie One for natural-language chat and Genie Agents for domain-scoped assistants, draws on these definitions, and Genie One MCP exposes the same semantics, glossary, and domains to external agents through the open Model Context Protocol. Genie Ontology, in gated Public Preview, extends the modeled layer with context inferred from your existing assets and ranks conflicting definitions with its OntoRank authority score. The practical path: build on metric views today, since their low-code authoring is generally available, and layer the ontology in as it becomes broadly available.

FAQs

What's the difference between a semantic layer and a metric view?

The semantic layer is the concept: one governed place for business definitions. A Unity Catalog metric view is the object that implements it on Databricks, defining measures, dimensions, and agent metadata that any surface can query.

Why does the semantic layer matter so much for agents?

A person reading a dashboard can sanity-check an odd number. An agent acts on whatever it's given, at scale and speed, so model for the highest-risk consumer and the dashboards benefit too. Ambiguous definitions become confidently wrong answers when an agent runs them unattended.

Can external agents use the same definitions?

Yes. Genie One MCP exposes the glossary, semantics, and domains through the open Model Context Protocol, so external agents and tools can query the same authoritative definitions instead of re-implementing metric logic.

Is this available today?

Unity Catalog metric views, including their low-code authoring, are available now. Genie Ontology is in gated Public Preview as of Data + AI Summit 2026, so treat ontology-specific capabilities as early access while you build on the governed metric-view foundation.