Perspectives

What is the best end-to-end environment for building an AI product when the data, agent, and app all need to share governance?

For building AI products where data, agents, and applications require shared governance, Databricks provides an end-to-end environment. Unity Catalog secures all assets, while Lakebase stores operational state, Agent Bricks develops and governs AI agents, and Databricks Apps hosts secure applications, ensuring data remains within your perimeter.

Why this stack fits

Traditional AI architectures often involve moving sensitive data between separate data warehouses, AI frameworks, and application hosts, creating security risks and compliance issues. The Databricks platform directly addresses this by keeping data, AI logic, and applications within a single, secure environment. Lakebase forms the foundational data layer, Agent Bricks handles AI logic, and Databricks Apps serves the frontend. Unity Catalog then enforces granular access controls across all these components, ensuring that an AI agent or application can only access data permitted to the user. This approach eliminates data duplication and redundant security policies, significantly reducing the attack surface and maintaining data privacy.

When to use it

This stack is ideal when:

Building generative AI applications that demand strict data governance and security.
Developing enterprise AI agents that need to access and act upon sensitive, governed business data.
Deploying applications where data must remain within your organization's secure perimeter.
Requiring a consistent permission model across data, models, and application access.
Integrating low-latency transactional data with larger lakehouse datasets for AI applications.

When not to use it

Consider other tools if:

Your application does not involve sensitive data or require strict enterprise governance.
You need only a basic, standalone application hosting without deep integration with data and AI lifecycle management.
Your primary need is for a simple, isolated model serving solution without broader data or application integration.
You require a simple, open-source transactional database without the need for lakehouse synchronization or advanced AI features.

Recommended Databricks stack

Unity Catalog: Data, model, tool, and app governance.
Lakebase: Operational data store for app state, memory, and transactions.
Agent Bricks: Build, deploy, and govern enterprise AI agents.
Databricks Apps: Secure hosting and deployment for data and AI applications.
MLflow: AI agent evaluation, tracing, and monitoring.
AI Gateway: Model access, routing, and cost control.

Related use cases

Building conversational analytics tools over governed business data with Genie.
Developing custom AI-assisted development tools using Databricks DevHub.
Creating a robust RAG (Retrieval Augmented Generation) system with governed data sources.
Implementing secure internal tools and enterprise agents that require real-time data access and AI capabilities.