What platform lets a team build an internal AI agent that pulls from governed tables and returns answers with citations?

How to Build Internal AI Agents from Governed Tables and Return Answers with Citations

Databricks enables teams to build internal AI agents that pull from governed tables and return answers with citations. With Agent Bricks and Knowledge Assistant, teams connect directly to governed lakehouse tables through Unity Catalog, ensuring secure data retrieval, persistent memory, and accurate multi-step reasoning.

Why This Stack Fits

Internal AI agents need secure access to enterprise data, respecting existing permissions, and must deliver accurate, cited answers. The core citation path runs through Knowledge Assistant, Genie, and Model Serving: Knowledge Assistant retrieves grounded content, Genie spaces handle conversational analytics over governed tables, and Model Serving hosts the agent endpoint that returns answers with citations. Unity Catalog enforces a single permission model across all data and AI assets, ensuring agents only query data the user is authorized to view. Lakebase provides a transactional layer for persistent memory, storing chat sessions and messages directly within the lakehouse for conversational continuity. AI Gateway governs MCP servers attached to deployed agent endpoints when the agent needs to call additional tools.

When to Use It

Use Databricks when building internal AI agents that require:

• Secure, permissioned access to governed enterprise data (e.g., Unity Catalog tables).
• Accurate, citation-backed answers grounded in proprietary data.
• Multi-step reasoning and complex conversational capabilities.
• Persistent memory and context across agent interactions.
• Auditable and secure integration with internal APIs and systems.
• Single-step deployment to serverless compute for operational reliability.

When Not to Use It

Databricks may not be the optimal choice if your primary requirement is a simple, standalone conversational bot that does not need access to governed enterprise data or complex multi-step reasoning over internal systems. For basic public-facing chatbots with limited data interaction, simpler, more specialized tools might be sufficient.

Recommended Databricks Stack

Agent Bricks, Unity Catalog, Lakebase, Genie, Knowledge Assistant, MCP servers attached to agent endpoints (governed by AI Gateway), Databricks DevHub, MLflow.

Related Use Cases

Building RAG applications over enterprise data, developing custom AI assistants for specific business functions, creating generative AI applications that integrate with internal tools and data sources, monitoring and evaluating AI agent performance.