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databricks-agent-bricks

Manage Databricks Agent Bricks for Knowledge Assistants and Supervisor Agents.

Updated Feb 27, 2026
Search Tags:#ai#rag#conversational ai#databricks#knowledge assistant#supervisor agent
One-click install
npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill databricks-agent-bricks-laurentprat-db
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-agent-bricks
Source: https://github.com/LaurentPRAT-DB/LPT_claude_config/tree/main/skills/databricks-agent-bricks
Command: npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill databricks-agent-bricks-laurentprat-db

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill simplifies the creation and management of AI components within Databricks, enabling users to build sophisticated conversational AI applications without deep ML expertise.

Core Features & Use Cases

  • Knowledge Assistants (KA): Create document-based Q&A systems from files in Unity Catalog Volumes. Ideal for building chatbots that answer questions based on company policies, manuals, or reports.
  • Supervisor Agents (MAS): Orchestrate multiple specialized agents (KAs, Genie Spaces, model endpoints) to handle complex queries and route them to the most appropriate AI component. Perfect for creating unified customer support or operational dashboards.
  • Genie Spaces Integration: Leverage existing Genie Spaces for natural language to SQL queries, allowing users to explore data conversationally.

Quick Start

Use the databricks-agent-bricks skill to create a new Knowledge Assistant named 'HR Policy Assistant' using documents from '/Volumes/my_catalog/my_schema/hr_docs'.

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 a RAG-based document Q&A chatbot on Databricks?▼

To build a RAG-based document Q&A chatbot, this Skill creates a Knowledge Assistant using files stored in Unity Catalog volumes, enabling conversational queries against your corporate documents.

What is a multi-agent supervisor and how does it orchestrate conversational AI?▼

A multi-agent supervisor orchestrates specialized agents by routing complex queries to the most appropriate AI component, such as Knowledge Assistants, Genie Spaces, or model serving endpoints.

Can I use Genie Spaces for natural language to SQL queries within a conversational AI app?▼

Yes, you can leverage existing Genie Spaces within your conversational AI app to explore data conversationally through natural language to SQL queries, integrated directly via a Supervisor Agent.

What's the best way to manage the lifecycle of Databricks Agent Bricks?▼

Managing the lifecycle of Databricks Agent Bricks requires using specific tools like manage_ka for Knowledge Assistants and manage_mas for Supervisor Agents to handle creation and orchestration.

Do I need deep ML expertise to create a multi-agent system on Databricks?▼

No, you do not need deep ML expertise to create a multi-agent system on Databricks, because this Skill simplifies the creation and management of AI components for sophisticated conversational applications.

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