As agents become more capable, the central question shifts from what AI can do to what it should be allowed to do.
This second article in a four-part series on Databricks' Context, Control, Choice, and Cost framework for enterprise AI focuses on Control. It examines how organizations can set and enforce the rules for AI at scale, and how the Databricks Data + AI Platform approaches the problem.
As organizations deploy more models, agents, tools, and MCP servers, they need consistent governance across their entire AI environment. Control means managing who can access AI services, what agents can do, which data they can use, and how every interaction is monitored.
How Databricks delivers control
Unity Catalog governs data and AI assets together. Tables, files, models, agents, tools, MCP services, and other assets can be governed through a common catalog and permission model. This creates a shared control plane rather than separate governance silos.
Unity Gateway controls runtime interactions. Unity Gateway extends governance beyond static assets to model calls, agent actions, MCP services, and tools. Teams can control which services are available, route requests, and manage access from one place.
Guardrails protect inputs and outputs. Organizations can apply safety, compliance, privacy, and business-specific policies to model requests and responses.
Service policies govern agent actions. Policies can be based on agent identity, user context, request parameters, and content. For example, an organization can restrict access to confidential documents, limit write actions, or require human approval before sensitive operations.
Observability and auditability are built in. Teams can track requests, tokens, latency, activity, cost, and model or service usage. Requests and responses can also be logged to governed tables for monitoring, troubleshooting, and compliance.
Business result
Databricks enables organizations to scale AI without treating every new model, agent, or tool as a separate security project. Governance, identity, policy enforcement, and auditability can be applied consistently across the Data + AI estate.
Control is what turns AI from a series of one-off pilots into something an organization can run with confidence. When identity, permissions, policies, and audit trails apply consistently across every model and agent, security and compliance teams can approve new use cases without starting each review from scratch.
A common control layer has another benefit: it makes the technology underneath easier to change. When governance lives in one place rather than inside each integration, adopting a new model or provider doesn't mean rebuilding the rules around it. That flexibility is the subject of the next article in this series: Choice.