databricks-agent-bricks

name	databricks-agent-bricks
description	Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.

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
Supervisor Agent (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 databricks-unstructured-pdf-generation skill if needed

For Genie Spaces

• See the databricks-genie skill for comprehensive Genie Space guidance
• Tables in Unity Catalog with the data to explore
• Generate raw data using the databricks-synthetic-data-gen skill
• Create tables using the databricks-spark-declarative-pipelines skill

For Supervisor Agents

• Model Serving Endpoints: Deployed agent endpoints (KA endpoints, custom agents, fine-tuned models)
• Genie Spaces: Existing Genie spaces can be used directly as agents for SQL-based queries
• Mix and match endpoint-based and Genie-based agents in the same Supervisor Agent

For Unity Catalog Functions

• Existing UC Function: Function already registered in Unity Catalog
• Agent service principal has EXECUTE privilege on the function

For External MCP Servers

• Existing UC HTTP Connection: Connection configured with is_mcp_connection: 'true'
• Agent service principal has USE CONNECTION privilege on the connection

MCP Tools

Knowledge Assistant Tool

manage_ka - Manage Knowledge Assistants (KA)

• action: "create_or_update", "get", "find_by_name", or "delete"
• name: Name for the KA (for create_or_update, find_by_name)
• volume_path: Path to documents (e.g., /Volumes/catalog/schema/volume/folder) (for create_or_update)
• description: (optional) What the KA does (for create_or_update)
• instructions: (optional) How the KA should answer (for create_or_update)
• tile_id: The KA tile ID (for get, delete, or update via create_or_update)
• add_examples_from_volume: (optional, default: true) Auto-add examples from JSON files (for create_or_update)

Actions:

• create_or_update: Requires name, volume_path. Optionally pass tile_id to update.
• get: Requires tile_id. Returns tile_id, name, description, endpoint_status, knowledge_sources, examples_count.
• find_by_name: Requires name (exact match). Returns found, tile_id, name, endpoint_name, endpoint_status. Use this to look up an existing KA when you know the name but not the tile_id.
• delete: Requires tile_id.

Genie Space Tools

For comprehensive Genie guidance, use the databricks-genie skill.

Basic tools available:

• create_or_update_genie - Create or update a Genie Space
• get_genie - Get Genie Space details
• delete_genie - Delete a Genie Space

See databricks-genie skill for:

• Table inspection workflow
• Sample question best practices
• Curation (instructions, certified queries)

IMPORTANT: There is NO system table for Genie spaces (e.g., system.ai.genie_spaces does not exist). To find a Genie space by name, use the find_genie_by_name tool.

Supervisor Agent Tool

manage_mas - Manage Supervisor Agents (MAS)

• action: "create_or_update", "get", "find_by_name", or "delete"
• name: Name for the Supervisor Agent (for create_or_update, find_by_name)
• agents: List of agent configurations (for create_or_update), each with:
  • name: Agent identifier (required)
  • description: What this agent handles - critical for routing (required)
  • ka_tile_id: Knowledge Assistant tile ID (use for document Q&A agents - recommended for KAs)
  • genie_space_id: Genie space ID (use for SQL-based data agents)
  • endpoint_name: Model serving endpoint name (for custom agents)
  • uc_function_name: Unity Catalog function name in format catalog.schema.function_name
  • connection_name: Unity Catalog connection name (for external MCP servers)
  • Note: Provide exactly one of: ka_tile_id, genie_space_id, endpoint_name, uc_function_name, or connection_name
• description: (optional) What the Supervisor Agent does (for create_or_update)
• instructions: (optional) Routing instructions for the supervisor (for create_or_update)
• tile_id: The Supervisor Agent tile ID (for get, delete, or update via create_or_update)
• examples: (optional) List of example questions with question and guideline fields (for create_or_update)

Actions:

• create_or_update: Requires name, agents. Optionally pass tile_id to update.
• get: Requires tile_id. Returns tile_id, name, description, endpoint_status, agents, examples_count.
• find_by_name: Requires name (exact match). Returns found, tile_id, name, endpoint_status, agents_count. Use this to look up an existing Supervisor Agent when you know the name but not the tile_id.
• delete: Requires tile_id.

Typical Workflow

1. Generate Source Data

Before creating Agent Bricks, generate the required source data:

For KA (document Q&A):

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

2. Create the Agent Brick

Use manage_ka(action="create_or_update", ...) or manage_mas(action="create_or_update", ...) 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

Example: Multi-Modal Supervisor Agent

manage_mas(
    action="create_or_update",
    name="Enterprise Support Supervisor",
    agents=[
        {
            "name": "knowledge_base",
            "ka_tile_id": "f32c5f73-466b-...",
            "description": "Answers questions about company policies, procedures, and documentation from indexed files"
        },
        {
            "name": "analytics_engine",
            "genie_space_id": "01abc123...",
            "description": "Runs SQL analytics on usage metrics, performance stats, and operational data"
        },
        {
            "name": "ml_classifier",
            "endpoint_name": "custom-classification-endpoint",
            "description": "Classifies support tickets and predicts resolution time using custom ML model"
        },
        {
            "name": "data_enrichment",
            "uc_function_name": "support.utils.enrich_ticket_data",
            "description": "Enriches support ticket data with customer history and context"
        },
        {
            "name": "ticket_operations",
            "connection_name": "ticket_system_mcp",
            "description": "Creates, updates, assigns, and closes support tickets in external ticketing system"
        }
    ],
    description="Comprehensive enterprise support agent with knowledge retrieval, analytics, ML, data enrichment, and ticketing operations",
    instructions="""
    Route queries as follows:
    1. Policy/procedure questions → knowledge_base
    2. Data analysis requests → analytics_engine
    3. Ticket classification → ml_classifier
    4. Customer context lookups → data_enrichment
    5. Ticket creation/updates → ticket_operations

    If a query spans multiple domains, chain agents:
    - First gather information (analytics_engine or knowledge_base)
    - Then take action (ticket_operations)
    """
)

Related Skills

• databricks-genie - Comprehensive Genie Space creation, curation, and Conversation API guidance
• databricks-unstructured-pdf-generation - Generate synthetic PDFs to feed into Knowledge Assistants
• databricks-synthetic-data-gen - Create raw data for Genie Space tables
• databricks-spark-declarative-pipelines - Build bronze/silver/gold tables consumed by Genie Spaces
• databricks-model-serving - Deploy custom agent endpoints used as MAS agents
• databricks-vector-search - Build vector indexes for RAG applications paired with KAs

See Also

• 1-knowledge-assistants.md - Detailed KA patterns and examples
• databricks-genie skill - Detailed Genie patterns, curation, and examples
• 2-supervisor-agents.md - Detailed MAS patterns and examples