AI agents are only as useful as their understanding of the business in which they work. Within a given domain, teams can curate that understanding by hand. Thousands of customers now runGenie Agents in production this way, including 7-Eleven, Virgin Atlantic and General Motors. However, enterprises are no longer deploying a single agent for a single domain. They are deploying many agents across every function, and each needs the same accurate and complete understanding of how the business works.

The enterprise context that agents need is far larger than any team can write down or model by hand. It lives in the logic behind thousands of production dashboards, SQL queries, notebooks and BI semantic models, as well as in the documents and applications where teams record definitions and maintain a working knowledge of who does what. It also changes every week as products launch, campaigns run, and teams reorganize.

This is why ontologies have historically struggled in the enterprise. Built by specialists and maintained manually, they captured only a narrow slice of the business and quickly became outdated as the business evolved. Agents change both sides of that problem. They are now the primary consumers of enterprise context, and they can also produce it by learning from the work teams already do. The result is a new kind of agentic ontology, built by agents, used by agents and governed by the people who know the business.