The platform integrates learned enterprise knowledge with structured governance directly overseen through Unity Catalog semantics. While agents automatically generate context snippets from operational queries and dashboards, internal teams establish baseline definitions using specialized governance views. To resolve contradictions across varied assets, the system deploys an authority-aware ranking algorithm called OntoRank. Benchmark testing conducted alongside Turing confirms that grounding agents in this shared semantic structure notably improves their operational accuracy.
Key points
Human teams explicitly govern authoritative business metrics using tools such as Metric V…: Human teams explicitly govern authoritative business metrics using tools such as Metric Views, Domains, and Pages.
The OntoRank algorithm resolves contextual conflicts by assessing criteria like source au…: The OntoRank algorithm resolves contextual conflicts by assessing criteria like source authority, certification, popularity, and recency.
Benchmark evaluations completed with Turing demonstrate measurable accuracy gains for art…: Benchmark evaluations completed with Turing demonstrate measurable accuracy gains for artificial intelligence agents backed by the ontology.
Sources checked by our analysis pipeline: 1
The agentic ontology, built by agents, governed by people, and designed for every agent, is now Generally Available with expanded knowledge sources and new tools to curate and refine it.
by Ken Wong, Paul Roome and Richard Tomlinson
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 run Genie 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.
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