frank-luongt/skills/codex/databricks-agent-bricks

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name: databricks-agent-bricks

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

• See the databricks-genie skill for comprehensive Genie Space guidance
• Tables in Unity Catalog 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 (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 MAS

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

find_ka_by_name - Find a Knowledge Assistant by name

• name: The exact name of the KA to find
• Returns: 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_ka - Delete a Knowledge Assistant

• tile_id: The KA tile ID to delete

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.

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, 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 (use for custom agents)
  • Note: Provide exactly one of: ka_tile_id, genie_space_id, or endpoint_name
• description: (optional) What the MAS does
• instructions: (optional) Routing instructions for the supervisor
• 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

find_mas_by_name - Find a Multi-Agent Supervisor by name

• name: The exact name of the MAS to find
• Returns: tile_id, name, endpoint_status, agents_count
• Use this to look up an existing MAS when you know the name but not the 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
• databricks-genie skill - Detailed Genie patterns, curation, and examples
• 3-multi-agent-supervisors.md - Detailed MAS patterns and examples

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