Semantic Layer

Semantic layers give agents the business context they need to ask the right questions and deliver meaningful answers. Without a semantic layer, an agent querying raw tables must infer what "revenue" means, how "active users" are defined, or which filters constitute a "North American" segment. A well-defined semantic layer encodes these business rules once and makes them available to every agent, dashboard, and analyst in the organization.

This consistency is critical in agentic workflows. When multiple agents operate across different parts of the data stack, the semantic layer ensures they all compute metrics the same way. It eliminates the risk of conflicting definitions and provides a shared vocabulary that bridges the gap between technical data structures and business concepts. Agents can reference semantic definitions to generate accurate SQL, validate results, and explain their reasoning in terms that stakeholders understand.

The combination of semantic layers with large language models creates a powerful paradigm for natural language analytics. Agents can translate plain-language questions into precise, semantically grounded queries, confident that the resulting metrics align with organizational standards. As semantic layers become more expressive and widely adopted, they will serve as the authoritative source of business truth in the agentic data stack.