Enterprises are rapidly deploying agentic applications at scale, from back-office micro apps that automate routine tasks to agents that power customer experiences across industries and departments. But general-purpose foundation models, disconnected from enterprise data and lacking centralized governance controls, can't deliver the accuracy, compliance, or business context these agents and applications demand. Equally critical, they introduce risk: uncontrolled model and data access, inconsistent policies, lack of observability, and fragmented audit trails.

We believe Gartner's decision to reclassify this category from "Data Science and Machine Learning" to "AI Platforms for Data Science and Machine Learning" confirms our longstanding view: AI is no longer a peripheral experiment — it's the operating model of the modern enterprise, grounded in business context.

We believe our position as a Leader in this category is rooted in a singular philosophy: you cannot have an AI strategy without a data strategy — and you cannot scale either without a governance strategy. While many vendors stitch together separate products for data, models, agents, and governance, Databricks delivers one unified platform.

That means one copy of your data, one governance layer across data and AI, and one consistent way to build, monitor, and control agents in production. By unifying the lakehouse, Lakebase, Agent Bricks, and Unity Catalog, we give every team, from developers to business users, a single place to turn enterprise data into trusted, compliant, production-grade agents and applications. With Unity AI Gateway, organizations gain centralized policy enforcement, model access controls, usage tracking, cost management, and real-time guardrails across every request and response.

Agents are only as useful as the data and context they can reason over. With Agent Bricks, teams build production-ready custom agents that are automatically optimized for cost and quality, grounded in governed enterprise data in the Databricks lakehouse and backed by Lakebase, our serverless, Postgres-compatible operational store for agent state and applications. Agents retrieve the right information, interpret business semantics consistently, and act with the accuracy and reliability enterprises require. YipitData used this approach to scale unstructured data intelligence, achieving a 20x increase in company coverage and 92–95% tagging accuracy out of the box.

Business users can get trusted insights and take agentic actions through Databricks Genie One and Genie Agents, powered by Genie Ontology which provides business context, grounded in your data. easyJet is using this flexibility to reimagine airline retailing on top of Lakebase, Agent Bricks, and Apps.