From Data Layer To Decision Layer: Why AI Agents Need More Than Information

Gary Kotovets, Chief Data, Analytics, and AI Officer at Dun & Bradstreet.

​As enterprises accelerate their transition from AI as a querying tool to AI as an autonomous executor, the fundamental role of data within organizational architecture must evolve in kind. The passive data layer, a reservoir sitting beneath the application stack and waiting to be queried, is no longer sufficient to meet the demands of agentic systems.​

Agents tasked with onboarding suppliers, approving customers, flagging risk exposures or recommending next-best actions require more than access to raw information. They need enriched, trusted business context delivered precisely at the moment a decision is being considered, with enough confidence and clarity to support action inside the workflow itself.​

Provenance separates context from noise​.

Not all data is equal. What separates useful context from noise in an agentic workflow is provenance, meaning a clear understanding of where the data originated, how it was verified and whether it is authoritative enough to serve as ground truth for the decision at hand.​

This is where the concept of the data originator as truth becomes operationally significant. When an AI agent needs to evaluate a counterparty, assess a supplier’s risk profile or validate a business relationship, the only data worth trusting is data that has been verified at its source, structured around a persistent business identity and enriched with the commercial relationships that define how that entity actually operates in the world.​

Persistent identity becomes the anchor​.

In commercial contexts, that source of truth should begin with a persistent business identifier. Such an identifier is not simply a reference code; it is an anchor point that connects a verified legal identity to the broader web of attributes, relationships and signals that define a business.​