Governed Context, Security and Spend Controls for AI Agents

Enterprises have moved past the point of asking whether AI agents can do useful work. The more pressing question now is whether teams can stand behind what those agents say and do. Is the agent answering a question based on the definition of revenue that finance actually approved? Can security see a risky tool call before it executes, and can a budget limit stop a runaway agent before it shows up in the quarterly forecast? If governance and context aren’t built into agents from the start, the potential for conflicting answers and rogue agents compounds with every new agent — not to mention the risk of shadow AI and runaway AI spend. A dashboard reports one figure for net revenue, an analyst reports another, and an AI agent returns a third. When every team calculates "net revenue" in a different way and there’s no record of which version is official, even a perfectly governed agent can return an answer that nobody agrees on. Business Ontology (public preview soon) is the newest addition to Snowflake Horizon Context. It gives organizations one governed place to record what their core business concepts mean, independent of any specific data structure. Business Ontology is also…