databricks-agent-bricks

Original：🇺🇸 English
Translated

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

3installs
Sourcedatabricks/databricks-agent-skills
Added on

NPX Install

npx skill4agent add databricks/databricks-agent-skills databricks-agent-bricks

Tags

Translated version includes tags in frontmatter
agent-bricksknowledge-assistantsupervisor-agentdatabricks-climulti-agent-orchestration

SKILL.md Content

View Translation Comparison →

Agent Bricks

Agent Bricks are pre-built AI tiles in Databricks that provide conversational interfaces. This skill covers Knowledge Assistants and Supervisor Agents.
BrickPurposeThis Skill
Knowledge Assistant (KA)Document Q&A using RAG on PDFs/text in Volumes✓
Supervisor AgentOrchestrates multiple agents (KA, endpoints, UC functions, MCP)✓

Knowledge Assistant

bash
# 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}"  # destructive & irreversible — confirm the id first
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 ≥ v1.0.0). 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.
bash
# 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 (destructive & irreversible — confirm the id first)
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.
bash
# 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
BlockUse 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 varyRun 
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
.
bash
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

TopicFile
KA source types, index, troubleshootingreferences/1-knowledge-assistants.md
UC functions, MCP servers, examplesreferences/2-supervisor-agents.md