Data topics

Dive into how organizations can safely scale multi-agent analytics without losing control or accuracy. This post outlines five essential frameworks that ensure strong governance—from semantic layers that enforce consistent metric definitions, to auditing structures that verify agent decisions, and policy controls that manage permissions and risk. Learn how these frameworks help balance autonomy & trust, reduce ambiguity, and make data analysis reliable and repeatable in enterprise contexts.

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.

Inconsistent revenue numbers can frustrate finance teams and erode trust in business metrics. This blog reveals how vague definitions, fragmented dashboards, and mismatched data logic create a misaligned financial picture. It then explains how implementing a smart semantic stack—with standardized metrics**, semantic layers**, and consistent governance—clarifies the confusion. These foundational improvements enable analysts and decision-makers to compare apples to apples, derive trusted insights, and drive faster, more accurate decision-making across the organization.

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.