Semantic Layer & AI Agents

The semantic execution layer is where business meaning becomes runtime. It is the typed, versioned graph that every agent, dashboard, and SQL query passes through before reaching the warehouse - the layer that makes "revenue," "customer," and "compliance" mean exactly the same thing whether the question comes from a financial analyst, an autonomous AI agent, or a downstream report.

This collection covers the architecture, mechanics, and operational consequences of moving meaning into infrastructure. Posts here examine how a multi-scope semantic graph (global → tenant → persona → user) replaces fragile prompt-time guessing, why warehouse-native semantic layers stop at the warehouse boundary, and how SQL is becoming an intermediate representation under a higher-order semantic layer.

You'll also find deep dives on autonomous semantic systems: the maintenance agents that detect drift, resolve conflicts, and evolve the graph as the business changes. Together, these pieces argue that the semantic layer is not a UI layer or a metric API - it is the new system of record for enterprise meaning, and the substrate every reliable AI agent will eventually compile through.