Agentic Semantic Layers Aim to Fix AI Agents Querying Data Inconsistently

An agentic semantic layer is a metadata layer that sits between AI agents and a data warehouse, defining business metrics and enforcing access control through standardized protocols like MCP or REST APIs. Unlike traditional semantic layers built for BI dashboards and human analysts, this approach is designed for programmatic consumers such as LLMs and AI agents. Without such a layer, AI agents writing raw SQL against a warehouse frequently return inconsistent answers, expose data to unauthorized tenants, and leave no auditable trail of how a number was derived. Traditional BI tools like Looker or Power BI solve consistency for dashboards but cannot serve AI agents that need runtime metric discovery and row-level access control over open protocols. As AI agents increasingly become the primary interface for business data queries, the gap between existing data infrastructure and agent requirements is emerging as a critical production challenge.