Agent Skill · Databricks

databricks-agent-bricks

Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).

View SKILL.md on GitHub → Source repository Provider profile

Provider: Databricks Path in repo: experimental/databricks-agent-bricks/SKILL.md

Skill body

Agent Bricks

Agent Bricks are pre-built AI tiles in Databricks that provide conversational interfaces. This skill covers Knowledge Assistants and Supervisor Agents.

Brick	Purpose	This Skill
Knowledge Assistant (KA)	Document Q&A using RAG on PDFs/text in Volumes	✓
Supervisor Agent	Orchestrates multiple agents (KA, endpoints, UC functions, MCP)	✓

Knowledge Assistant

# Find volumes
databricks volumes list CATALOG SCHEMA
databricks experimental aitools tools query --warehouse WH "LIST '/Volumes/catalog/schema/volume/'"

# Create KA
databricks knowledge-assistants create-knowledge-assistant "Name" "Description"

# Add knowledge source. With --json, pass ONLY the PARENT as a positional arg
# and put display_name / description / source_type / the source body (files|index|file_table)
# inside the JSON. Mixing positional DISPLAY_NAME/DESCRIPTION/SOURCE_TYPE with --json errors.
databricks knowledge-assistants create-knowledge-source \
  "knowledge-assistants/{ka_id}" \
  --json '{
    "display_name": "Docs",
    "description": "Documentation files",
    "source_type": "files",
    "files": {"path": "/Volumes/catalog/schema/volume/"}
  }'

# Sync and check status
databricks knowledge-assistants sync-knowledge-sources "knowledge-assistants/{ka_id}"
databricks knowledge-assistants get-knowledge-assistant "knowledge-assistants/{ka_id}"

# List/manage
databricks knowledge-assistants list-knowledge-assistants
databricks knowledge-assistants delete-knowledge-assistant "knowledge-assistants/{ka_id}"

Source types: files (Volume path) or index (Vector Search: index.index_name, index.text_col, index.doc_uri_col)

Status: CREATING (2-5 min) → ONLINE → OFFLINE

Supervisor Agent

Native CLI: databricks supervisor-agents (Beta, requires CLI ≥ 0.299.2). Resource paths look like supervisor-agents/{id} — every command takes either that full path or a PARENT of that shape. list-supervisor-agents and list-examples/list-tools return bare JSON arrays.

# Create the supervisor agent (display name positional, description/instructions as flags)
databricks supervisor-agents create-supervisor-agent "My Supervisor" \
    --description "Routes queries to specialized agents" \
    --instructions "Route data questions to analyst, document questions to docs_agent."
# → returns {name: "supervisor-agents/<uuid>", endpoint_name: "mas-<short>-endpoint", ...}

# List / get / find by name
databricks supervisor-agents list-supervisor-agents
databricks supervisor-agents get-supervisor-agent supervisor-agents/<id>
databricks supervisor-agents list-supervisor-agents | jq '.[] | select(.display_name == "My Supervisor")'

# Update — UPDATE_MASK + new DISPLAY_NAME are positional; description/instructions optional flags
databricks supervisor-agents update-supervisor-agent supervisor-agents/<id> \
    "display_name,description,instructions" "My Supervisor (v2)" \
    --description "..." --instructions "..."

# Delete
databricks supervisor-agents delete-supervisor-agent supervisor-agents/<id>

Tools (the agents the supervisor routes to)

Each tool wires the supervisor to a downstream resource. tool_type lives in --json (the CLI rejects it as a positional when --json is used). Each type has a type-specific block (genie_space, knowledge_assistant, etc.) whose identifier field differs by type — see the table below.

# Attach a Genie space — find its space_id with `databricks genie list-spaces`
databricks supervisor-agents create-tool supervisor-agents/<id> analyst --json '{
    "tool_type": "genie_space",
    "description": "SQL analytics on the analytics warehouse",
    "genie_space": {"id": "<genie_space_id>"}
}'

# Attach a Knowledge Assistant — find ka_id with `databricks knowledge-assistants list-knowledge-assistants`
databricks supervisor-agents create-tool supervisor-agents/<id> docs_agent --json '{
    "tool_type": "knowledge_assistant",
    "description": "Answers from product documentation",
    "knowledge_assistant": {"knowledge_assistant_id": "<ka_id>"}
}'

# List / get / delete tools
databricks supervisor-agents list-tools supervisor-agents/<id>
databricks supervisor-agents get-tool supervisor-agents/<id>/tools/<tool_id>
databricks supervisor-agents delete-tool supervisor-agents/<id>/tools/<tool_id>

Tool types (tool_type value → type-specific block):

`tool_type`	Block	Use for
`genie_space`	`{"id": "<space_id>"}`	Natural language → SQL via Genie
`knowledge_assistant`	`{"knowledge_assistant_id": "<ka_id>"}`	Document Q&A via a KA
`uc_function`	`{"name": "catalog.schema.func"}`	UC SQL/Python function
`uc_connection`	`{"name": "<connection_name>"}`	External MCP server via UC HTTP Connection
`volume`	`{"name": "<full_volume_name>"}`	UC Volume browsing
`app`	`{"name": "<app_name>"}`	Databricks App
Other types (`serving_endpoint`, `lakeview_dashboard`, `supervisor_agent`, `uc_table`, `vector_search_index`, `catalog`, `schema`, `web_search`)	Block name and field shape vary	Run `databricks supervisor-agents create-tool --help` and probe — these were not verified end-to-end here.

Examples (training the supervisor)

Examples must use --json — the positional GUIDELINES arg doesn’t accept any encoding because guidelines is a repeated string.

databricks supervisor-agents create-example supervisor-agents/<id> --json '{
    "question": "What were Q4 revenue numbers?",
    "guidelines": ["Route to analyst Genie space", "Always group by region"]
}'

databricks supervisor-agents list-examples supervisor-agents/<id>
databricks supervisor-agents get-example supervisor-agents/<id>/examples/<ex_id>
databricks supervisor-agents delete-example supervisor-agents/<id>/examples/<ex_id>

Endpoint readiness: after create-supervisor-agent, the serving endpoint takes up to ~10 minutes to come online before it can answer queries. get-supervisor-agent returns the endpoint name immediately, but querying it is gated on the endpoint’s own readiness — check via databricks serving-endpoints get <endpoint_name>.

Reference

Topic	File
KA source types, index, troubleshooting	references/1-knowledge-assistants.md
UC functions, MCP servers, examples	references/2-supervisor-agents.md