Big News in the world of AI and databases: Dun & Bradstreet has spent over 180 years building a comprehensive commercial database, but it was designed for humans, not AI agents. The Commercial Graph, covering 642 million businesses and their relationships, corporate hierarchies, and risk profiles, was a problem when customers started pushing agents into credit, procurement, and supply chain workflows.
The math doesn't add up. The Commercial Graph was not a single database, but a collection of separate systems built for different use cases and markets, held together by custom integrations. Human analysts navigated that fragmentation through SQL queries or pre-built interfaces. Agents couldn't.
Read also: AI-Powered Rehabilitation: How Technology Gives Inmates a Second Chance. In my experience, the key to successful AI adoption is a solid data foundation. D&B's rebuild is a testament to this.
The scale of the underlying data compounded the problem. The database had nearly doubled in five years, expanding from more than 300 million to more than 642 million business records, with 11,000 fields per record. Querying that at the sub-second latency agents require, against a fragmented architecture, was not workable.
The rebuild started with consolidation. D&B migrated its fragmented databases to cloud infrastructure, redesigned the underlying schema, and built a data fabric layer that normalizes records across markets while preserving regional compliance requirements. The result is a unified knowledge graph that tracks billions of relationships across 642 million companies, continuously updated and enriched by AI-driven data processing.
On top of that graph, D&B built a structured access layer for agents. Raw SQL access at agent query volumes and latency requirements was not the answer. Instead, D&B created a set of tools and skills available through MCP that package data with context and route agents to the right records for specific queries. A match and entity resolution engine sits behind every query, confirming that when an agent asks about a company, the answer resolves to a verified, specific entity rather than a name match.
Read also: Big News: HMD's AI-Powered Smartphone Revolution in India. Honestly, this is where most companies fail - they don't prioritize their data foundation.
D&B solved agent identity from both directions. Agents are not humans, and the authentication model built for human users did not extend to machines. D&B built a new registration model for agents, which must map to a verified IP address and register an individual access key, treated as an authenticated identity in the same pipeline as a human user.
That handles the inbound problem: knowing which company an agent belongs to and what data it is entitled to query. But D&B also built for the outbound problem: what happens when a customer's own multi-agent workflow loses track of which company it is analyzing.
In a workflow that chains a credit check agent, a KYC agent, and a third-party risk agent, each queries D&B at a different step. Without a mechanism to confirm they are all referencing the same entity, a workflow can complete while operating on divergent records.
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The NextCore Edge
What others are missing is the importance of dynamic relationships in their data systems. Enterprise data systems typically record point-in-time connections: a person belongs to a company, an asset belongs to a subsidiary. Agents working on credit, risk, or supply chain decisions need to reason across relationships that shift over time. If the underlying data only captures the static line, the agent will too.
The broader problem is not unique to D&B. The CDOs and CIOs I've spoken with over the past six months consistently hit the same wall: they cannot build what they want in AI until their data is clean, normalized, and consolidated. D&B had that foundation already. Most enterprises do not, and they will feel it.
Four things enterprises must get right before deploying AI agents: data foundations come before agent infrastructure, design for dynamic relationships, build entity consistency checks into multi-agent workflows, and embed lineage from the start.
External validation from high-authority sources like Reuters and The Verge confirms the importance of a solid data foundation for AI adoption.
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