Why semantic layers are becoming AI infrastructure

A shared foundation for AI

As agents read enterprise data, semantic layers serve more than BI reports. Questions, reports, alerts, and workflows all need field meaning, relationships, metrics, terminology, and permissions.

dbt Semantic Layer centralizes reusable metric definitions, while Databricks AI/BI Genie uses governed data spaces and instructions. The implementations differ, but both place stable context before the model.

Why schemas are insufficient

A database does not explain whether active customers exclude test accounts or which date defines revenue. Hidden joins, historical tables, and local abbreviations increase the chance of a plausible but incorrect path.

A semantic layer adds descriptions, keys, relationships, metrics, default time, values, synonyms, owners, and versions. Authorization must still be enforced by the query layer.

AI raises the governance requirement

Traditional BI limits choices inside curated datasets. Agents select data dynamically and reuse semantics across applications, so one outdated definition can affect many generated outputs.

Review, test, version, and roll back semantic changes. Test metric examples, relationship cardinality, paraphrases, and role-based access.

Start with one domain

Use one subject area and 20 real questions. Identify required tables, relationships, metrics, terms, and roles, then run regression tests through the intended agent.

Validate maintainability as well as answers: ownership, lineage, impact analysis, and recovery. A semantic layer exposes data quality assumptions but cannot repair source quality by itself.

Sources and limits

Reviewed on 2026-08-20: dbt Semantic Layer, Databricks AI/BI Genie, and current BuildTable positioning. Architecture choices depend on the existing warehouse, BI, and agent stack.