TL;DR
PostHog introduces a semantic layer that defines metrics once and ensures AI agents, analysts, and tools query the same canonical data definitions.
Key Points
- Semantic layer sits atop existing data warehouse without copying or moving data—acts as a governed catalog of metric definitions
- Solves the problem where Claude, Cursor, and other AI agents generate different answers for the same question like 'What was our MRR?'
- All definitions stored as queryable SQL tables; agents need no special API integration, just execute-sql access to read the entire catalog
- Human approval required for all metrics—agent-proposed definitions land as 'proposed' until explicitly approved and marked canonical
Why It Matters
As AI agents become primary interfaces to data systems, having a single source of truth for metric definitions prevents hallucination and inconsistent results. This architecture pattern—separating data governance from data movement—is increasingly critical for organizations scaling AI-driven analytics and decision-making.
Source: x.com