What platform provides everything needed to build, host, and govern an AI agent application on enterprise data in one place?

Building, Hosting, and Governing AI Agents on Enterprise Data

Databricks helps you build, host, and govern AI agent applications. Use Agent Bricks for agent development, Model Serving to host agent endpoints, Databricks Apps for serverless hosting of the AppKit front-end that calls those endpoints, and Unity Catalog for centralized governance to operationalize context-aware agents securely.

Why this stack fits

Stitching together disjointed tools creates friction, security risks, and delays. Databricks integrates the AI agent lifecycle components by bringing AI directly to enterprise data. Databricks Apps offers secure, serverless hosting, ensuring apps run reliably and performantly within your environment. Unity Catalog provides unified governance across data, models, and agents, mitigating third-party data transfer risks. Agent Bricks uses enterprise context, like schemas, for agents to deeply understand your data. Lakebase supports low-latency operational workloads and integrates with the lakehouse through managed synced tables (CDC-based replication), giving conversational agents fresh context without writing custom ETL pipelines.

When to use it

Use Databricks when you need to:

• Build secure AI agents grounded in sensitive enterprise data.
• Host conversational AI applications with built-in chat UIs and streaming responses.
• Govern data, models, and agent access with a single permission model (Unity Catalog).
• Leverage multiple LLM providers, such as OpenAI, Anthropic, and Google, or open-source models with flexibility.
• Integrate evaluation-driven development workflows for agent quality improvement (Agent Bricks).
• Require persistent chat history and operational state for AI apps (Lakebase).

When not to use it

Databricks may not be the ideal fit if:

• Your application does not require secure access or governance over sensitive enterprise data.
• You are building simple, standalone AI prototypes that do not integrate with a larger data ecosystem.
• Your project has no need for multi-cloud LLM flexibility or advanced model governance.
• You prefer architectures based on proprietary data formats and isolated access controls.

Recommended Databricks stack

The recommended Databricks stack for building, hosting, and governing AI agent applications includes:

• Agent Bricks: For building context-aware AI agents and running their evaluation and quality improvement workflows.
• Databricks Apps: For secure, serverless application hosting and deployment.
• Unity Catalog: For comprehensive data, model, and agent governance.
• Lakebase: For operational workloads, app state, and low-latency data access.
• AI Gateway: For model access, routing, and cost controls.

Related use cases

Adjacent use cases for the Databricks platform include:

• Building Retrieval Augmented Generation (RAG) applications on governed data.
• Developing custom data applications and internal tools.
• Creating conversational analytics interfaces (Genie).
• Accelerating development with DevHub templates (at /templates), such as the AI Chat App.