An agentic semantic layer is a metadata layer between AI agents and a data warehouse that defines business metrics, enforces access control, and exposes governed query interfaces. Instead of writing raw SQL, agents query metric definitions through protocols like MCP (Model Context Protocol) or REST APIs. Every agent gets the same answer because the business logic is defined once, not interpreted per query.
The "agentic" distinction matters. Traditional semantic layers were built for BI dashboards and human analysts. An agentic semantic layer is built for programmatic consumers: LLMs, AI agents, SDKs, and applications. The interface, security model, and deployment patterns are different.
The problem with AI analytics today
AI agents are becoming a primary interface to data. Executives ask Claude for quarterly numbers. Product managers ask Cursor for usage metrics. Customer success teams ask chatbots for account health scores. The agent is the new dashboard.
But most data infrastructure wasn't built for this. Two problems dominate.
