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databricks-agent-bricks

Create and orchestrate Databricks Agent Bricks for conversational AI workflows.

Updated Mar 19, 2026
Search Tags:#ai#rag#databricks#genie-spaces#agent-bricks#mas#ka
One-click install
npx skills add https://github.com/Blackkadder/databricks-apps-and-agents-workshop --skill databricks-agent-bricks-blackkadder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-agent-bricks
Source: https://github.com/Blackkadder/databricks-apps-and-agents-workshop/tree/main/.claude/skills/databricks-agent-bricks
Command: npx skills add https://github.com/Blackkadder/databricks-apps-and-agents-workshop --skill databricks-agent-bricks-blackkadder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Agent Bricks enable rapid composition of end-to-end conversational AI apps on Databricks by combining Knowledge Assistants (KA), Genie Spaces, and Supervisor Agents (MAS). These reusable building blocks simplify integration of document Q&A, SQL-based exploration, and multi-agent orchestration into a single, scalable workflow.

Core Features & Use Cases

  • KA: create document-based Q&A assistants that retrieve and answer questions from Unity Catalog volumes.
  • Genie Space: enable natural language to SQL interactions over Unity Catalog data.
  • Supervisor Agent (MAS): orchestrate multiple specialized agents (KA, Genie, endpoints) into a unified interface.
  • End-to-end workflows: mix and match agents to build multi-domain AI assistants for enterprise use cases.

Quick Start

Create KA, Genie Space, and MAS bricks using manage_ka and manage_mas, then provision and test in the Databricks UI.

Frequently Asked Questions about databricks-agent-bricks

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build conversational AI apps on Databricks using Agent Bricks?▼

Build conversational AI apps on Databricks by orchestrating Knowledge Assistants, Genie Spaces, and Supervisor Agents to combine document Q&A, natural language SQL exploration, and multi-agent routing into scalable end-to-end workflows.

What is a Supervisor Agent in Databricks and when do I need it?▼

A Supervisor Agent (MAS) in Databricks orchestrates multiple specialized agents like Knowledge Assistants and Genie Spaces into a unified interface. You need it when building scalable multi-domain enterprise AI assistants that require multi-agent routing.

How do I create a Knowledge Assistant for document Q&A in Databricks?▼

Create a Knowledge Assistant using the manage_ka tooling guidance to retrieve and answer questions from documents stored in Unity Catalog volumes, then provision and test the KA directly within the Databricks UI.

Can I use Genie Spaces for natural language to SQL queries over Unity Catalog data?▼

Yes, Genie Spaces enable natural language to SQL interactions over Unity Catalog data. You provision them as reusable building blocks to allow conversational data exploration within your end-to-end Databricks AI workflows.

What are the limitations of mixing Knowledge Assistants and Genie Spaces in a single workflow?▼

Mixing Knowledge Assistants and Genie Spaces requires a Supervisor Agent for orchestration. You must provision each brick individually using manage_ka and manage_mas, ensuring proper multi-agent routing configuration before testing in the Databricks UI.

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