Agent Bricks

Create and manage Databricks Agent Bricks - pre-built AI components for building conversational applications.

Overview

Agent Bricks are three types of pre-built AI tiles in Databricks:

Brick	Purpose	Data Source
Knowledge Assistant (KA)	Document-based Q&A using RAG	PDF/text files in Volumes
Genie Space	Natural language to SQL	Unity Catalog tables
Multi-Agent Supervisor (MAS)	Multi-agent orchestration	Model serving endpoints

Prerequisites

Before creating Agent Bricks, ensure you have the required data:

For Knowledge Assistants

• Documents in a Volume: PDF, text, or other files stored in a Unity Catalog volume
• Generate synthetic documents using the

  unstructured-pdf-generation

  skill if needed

For Genie Spaces

• Tables in Unity Catalog: Bronze/silver/gold tables with the data to explore
• Generate raw data using the

  synthetic-data-generation

  skill
• Create tables using the

  spark-declarative-pipelines

  skill

For Multi-Agent Supervisors

• Model Serving Endpoints: Deployed agent endpoints to orchestrate
• These could be custom agents, fine-tuned models, or other deployed services

MCP Tools

Knowledge Assistant Tools

create_or_update_ka - Create or update a Knowledge Assistant

• name

  : Name for the KA

• volume_path

  : Path to documents (e.g.,

  /Volumes/catalog/schema/volume/folder

  )

• description

  : (optional) What the KA does

• instructions

  : (optional) How the KA should answer

• tile_id

  : (optional) Existing tile_id to update

• add_examples_from_volume

  : (optional, default: true) Auto-add examples from JSON files

get_ka - Get Knowledge Assistant details

• tile_id

  : The KA tile ID

delete_ka - Delete a Knowledge Assistant

• tile_id

  : The KA tile ID to delete

Genie Space Tools

IMPORTANT: Before creating a Genie Space, you MUST first inspect the table schemas using

get_table_details

to understand the data. This allows you to:

• Select the most relevant tables for the use case
• Write sample questions that reference actual column names and data patterns
• Create a description that accurately explains the data model

Genie Space Creation Workflow:

1. Call

   get_table_details(catalog, schema)

   to fetch table schemas
2. Analyze the columns, data types, and relationships
3. Select tables appropriate for the user's use case (prefer silver/gold over bronze)
4. Generate 5-10 sample questions based on actual columns and business context
5. Write a description explaining what users can explore
6. Call

   create_or_update_genie

   with the prepared content

create_or_update_genie - Create or update a Genie Space for SQL exploration

• display_name

  : Display name for the space

• table_identifiers

  : List of tables (e.g.,

  ["catalog.schema.table1", "catalog.schema.table2"]

  )

• warehouse_id

  : (optional) SQL warehouse ID (auto-detects if not provided)

• description

  : (optional) What the space does - explain the data model and relationships

• sample_questions

  : (optional) List of sample questions that reference actual columns

• space_id

  : (optional) Existing space_id to update

get_genie - Get Genie Space details

• space_id

  : The Genie space ID

delete_genie - Delete a Genie Space

• space_id

  : The Genie space ID to delete

Multi-Agent Supervisor Tools

create_or_update_mas - Create or update a Multi-Agent Supervisor

• name

  : Name for the MAS

• agents

  : List of agent configurations:

  • name

    : Agent name

  • endpoint_name

    : Model serving endpoint name

  • description

    : What this agent handles (used for routing)

• description

  : (optional) What the MAS does

• instructions

  : (optional) Routing instructions

• tile_id

  : (optional) Existing tile_id to update

• examples

  : (optional) List of example questions with

  question

  and

  guideline

  fields

get_mas - Get Multi-Agent Supervisor details

• tile_id

  : The MAS tile ID

delete_mas - Delete a Multi-Agent Supervisor

• tile_id

  : The MAS tile ID to delete

Typical Workflow

1. Generate Source Data

Before creating Agent Bricks, generate the required source data:

For KA (document Q&A):

1. Use `unstructured-pdf-generation` skill to generate PDFs
2. PDFs are saved to a Volume with companion JSON files (question/guideline pairs)

For Genie (SQL exploration):

1. Use `synthetic-data-generation` skill to create raw parquet data
2. Use `spark-declarative-pipelines` skill to create bronze/silver/gold tables

2. Create the Agent Brick

Use the appropriate

create_or_update_*

tool with your data sources.

3. Wait for Provisioning

Newly created KA and MAS tiles need time to provision. The endpoint status will progress:

• PROVISIONING

  - Being created (can take 2-5 minutes)

• ONLINE

  - Ready to use

• OFFLINE

  - Not running

4. Add Examples (Automatic)

For KA, if

add_examples_from_volume=true

, examples are automatically extracted from JSON files in the volume and added once the endpoint is

ONLINE

.

Best Practices

1. Use meaningful names: Names are sanitized automatically (spaces become underscores)
2. Provide descriptions: Helps users understand what the brick does
3. Add instructions: Guide the AI's behavior and tone
4. Include sample questions: Shows users how to interact with the brick
5. Use the workflow: Generate data first, then create the brick

See Also

• 1-knowledge-assistants.md

  - Detailed KA patterns and examples

• 2-genie-spaces.md

  - Detailed Genie patterns and examples

• 3-multi-agent-supervisors.md

  - Detailed MAS patterns and examples