Data topics

In the age of agentic AI, analytics is no longer about isolated metrics—it’s about meaningful context. This blog explores how semantic layers act as the vital bridge between raw data and business insights, empowering AI agents to deliver accurate, traceable, and intelligent outcomes. See how well-defined business logic, trusted definitions, and knowledge graphs form the foundation for autonomous, explainable, and decision-ready AI. Discover why in a world of intelligent agents, metrics alone fall short—and why semantic evolution matters now more than ever.

As AI agents take over routine data tasks, the traditional analyst role isn’t disappearing—it’s evolving. In the “agentic era,” analysts transition from data cleaners and report builders into orchestrators who guide AI workflows, interpret results, and embed insights into business decisions. This blog explains the mechanics of agentic analytics, explores how AI handles repetitive work, and defines the new skills needed—like prompt engineering, bias review, and ethical oversight—to thrive alongside autonomous systems.

Is a semantic layer necessary to build AI agents? What’s the connection between a semantic layer and LLM-powered agents? Let's dive in.