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Quentin Ambard
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Replace mas_manager.py with native supervisor-agents CLI
The supervisor-agents CLI group (Beta, CLI 0.299.2+) covers the full lifecycle previously implemented in mas_manager.py — create/list/get/ update/delete agents, tools, and examples — so the 640-line Python shim is no longer needed. - Rewrote agent-bricks SKILL.md + 2-supervisor-agents.md around `databricks supervisor-agents ...` invocations - Verified Genie + KA tool wiring end-to-end against real resources; routing returned correct results for both - Corrected knowledge_assistant block field: `knowledge_assistant_id` (not `id`) — only genie_space uses `id` - Marked unverified tool types as such instead of asserting wrong field shapes - Deleted mas_manager.py + its integration test - Bumped MIN_CLI_VERSION to 0.299.2 (required for supervisor-agents) Co-authored-by: Isaac
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‎DECOMMISSION_PLAN.md‎

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| Skill | Python File | Test Status |
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|-------|-------------|-------------|
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| `databricks-agent-bricks` | `mas_manager.py` | Has tests |
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| `databricks-unstructured-pdf-generation` | `pdf_generator.py` | Has tests (needs fix) |
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| Other skills with .py files | Various | Need tests |
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‎databricks-skills/.tests/test_agent_bricks_manager.py‎

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‎databricks-skills/databricks-agent-bricks/2-supervisor-agents.md‎

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# Supervisor Agents - Details
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For commands, see [SKILL.md](SKILL.md). `<SKILL_ROOT>` in examples = the directory containing SKILL.md.
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For commands, see [SKILL.md](SKILL.md). All operations use the native `databricks supervisor-agents` CLI (Beta, requires CLI ≥ 0.299.2).
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## Unity Catalog Functions
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- UC Function exists (`CREATE FUNCTION` or Python UDF)
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- Grant execute: `GRANT EXECUTE ON FUNCTION catalog.schema.func TO \`<agent_sp>\`;`
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**Config:**
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```json
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{"name": "enricher", "uc_function_name": "catalog.schema.enrich_data", "description": "Enriches customer records"}
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**Attach as a tool:**
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```bash
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databricks supervisor-agents create-tool supervisor-agents/<id> enricher --json '{
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"tool_type": "uc_function",
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"description": "Enriches customer records",
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"uc_function": {"name": "catalog.schema.enrich_data"}
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}'
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```
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## External MCP Servers
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GRANT USE CONNECTION ON my_mcp TO `<agent_sp>`;
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```
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**3. Config:**
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```json
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{"name": "operations", "connection_name": "my_mcp", "description": "Execute operations: approve invoices, trigger workflows"}
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**3. Attach as a tool:**
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```bash
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databricks supervisor-agents create-tool supervisor-agents/<id> operations --json '{
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"tool_type": "uc_connection",
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"description": "Execute operations: approve invoices, trigger workflows",
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"uc_connection": {"name": "my_mcp"}
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}'
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```
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**Test connection:**
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## Adding Examples
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Examples help evaluation and routing optimization. **The MAS endpoint must be ONLINE.** Right after `create_mas` (or a big `update_mas`), the endpoint is `NOT_READY` and takes **up to ~10 minutes** to come ONLINE. Pass `--wait` to block until then:
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Examples help evaluation and routing optimization. **The serving endpoint must be ONLINE.** Right after `create-supervisor-agent` (or a structural `update-supervisor-agent`), the endpoint takes **up to ~10 minutes** to come ONLINE. Examples can be added before that — they're stored on the agent definition — but querying the endpoint to evaluate routing requires readiness.
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```bash
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# Fails fast if endpoint isn't ONLINE yet
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python <SKILL_ROOT>/scripts/mas_manager.py add_examples TILE_ID '[
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{"question": "I need my invoice for March", "guideline": "Route to billing_agent"},
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{"question": "API returns 500 error", "guideline": "Route to tech_agent"}
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]'
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# --wait blocks until endpoint is ONLINE (default timeout 15 min) then adds.
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# The process must stay alive for the whole wait — there is no background queue.
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python <SKILL_ROOT>/scripts/mas_manager.py add_examples TILE_ID '[...]' --wait
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python <SKILL_ROOT>/scripts/mas_manager.py list_examples TILE_ID
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# Add an example (guidelines is a repeated string — must use --json)
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databricks supervisor-agents create-example supervisor-agents/<id> --json '{
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"question": "I need my invoice for March",
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"guidelines": ["Route to billing_agent"]
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}'
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databricks supervisor-agents create-example supervisor-agents/<id> --json '{
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"question": "API returns 500 error",
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"guidelines": ["Route to tech_agent"]
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}'
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# List / inspect / remove
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databricks supervisor-agents list-examples supervisor-agents/<id>
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databricks supervisor-agents get-example supervisor-agents/<id>/examples/<ex_id>
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databricks supervisor-agents delete-example supervisor-agents/<id>/examples/<ex_id>
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# Check endpoint readiness before querying
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databricks serving-endpoints get <endpoint_name>
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```
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## Troubleshooting

‎databricks-skills/databricks-agent-bricks/SKILL.md‎

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## Supervisor Agent
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**No CLI** — use `mas_manager.py` from this skill's `scripts/` folder. All `<SKILL_ROOT>/...` paths below are relative to the directory containing this SKILL.md (resolve to the absolute path in your install location).
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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.
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```bash
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# Create MAS
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python <SKILL_ROOT>/scripts/mas_manager.py create_mas "My Supervisor" '{
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"description": "Routes queries to specialized agents",
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"instructions": "Route data questions to analyst, document questions to docs_agent.",
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"agents": [
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{"name": "analyst", "genie_space_id": "01abc...", "description": "SQL analytics"},
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{"name": "docs_agent", "ka_tile_id": "dab408a2-...", "description": "Answers from documents"}
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]
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# Create the supervisor agent (display name positional, description/instructions as flags)
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databricks supervisor-agents create-supervisor-agent "My Supervisor" \
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--description "Routes queries to specialized agents" \
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--instructions "Route data questions to analyst, document questions to docs_agent."
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# → returns {name: "supervisor-agents/<uuid>", endpoint_name: "mas-<short>-endpoint", ...}
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# List / get / find by name
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databricks supervisor-agents list-supervisor-agents
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databricks supervisor-agents get-supervisor-agent supervisor-agents/<id>
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databricks supervisor-agents list-supervisor-agents | jq '.[] | select(.display_name == "My Supervisor")'
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# Update — UPDATE_MASK + new DISPLAY_NAME are positional; description/instructions optional flags
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databricks supervisor-agents update-supervisor-agent supervisor-agents/<id> \
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"display_name,description,instructions" "My Supervisor (v2)" \
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--description "..." --instructions "..."
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# Delete
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databricks supervisor-agents delete-supervisor-agent supervisor-agents/<id>
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```
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### Tools (the agents the supervisor routes to)
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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.
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```bash
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# Attach a Genie space — find its space_id with `databricks genie list-spaces`
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databricks supervisor-agents create-tool supervisor-agents/<id> analyst --json '{
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"tool_type": "genie_space",
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"description": "SQL analytics on the analytics warehouse",
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"genie_space": {"id": "<genie_space_id>"}
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}'
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# Attach a Knowledge Assistant — find ka_id with `databricks knowledge-assistants list-knowledge-assistants`
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databricks supervisor-agents create-tool supervisor-agents/<id> docs_agent --json '{
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"tool_type": "knowledge_assistant",
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"description": "Answers from product documentation",
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"knowledge_assistant": {"knowledge_assistant_id": "<ka_id>"}
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}'
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# Check status and manage. list_mas enumerates every MAS you can access
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# and returns {tile_id, name, endpoint_status, agents_count} — use it to
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# find a tile_id / see which MAS are ONLINE before operations.
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python <SKILL_ROOT>/scripts/mas_manager.py list_mas
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python <SKILL_ROOT>/scripts/mas_manager.py get_mas TILE_ID
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python <SKILL_ROOT>/scripts/mas_manager.py update_mas TILE_ID '{"agents": [...]}'
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python <SKILL_ROOT>/scripts/mas_manager.py delete_mas TILE_ID
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# Add examples — requires endpoint_status=ONLINE. After create_mas the MAS is
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# NOT_READY and takes up to ~10 min to reach ONLINE. Without --wait, this
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# fails fast if not ONLINE yet. With --wait, it blocks until ONLINE then adds.
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python <SKILL_ROOT>/scripts/mas_manager.py add_examples TILE_ID '[{"question": "...", "guideline": "..."}]' [--wait]
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# Find IDs
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databricks knowledge-assistants list-knowledge-assistants --output json | jq '.[].id'
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databricks genie list-spaces --output json | jq '.[].space_id'
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# List / get / delete tools
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databricks supervisor-agents list-tools supervisor-agents/<id>
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databricks supervisor-agents get-tool supervisor-agents/<id>/tools/<tool_id>
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databricks supervisor-agents delete-tool supervisor-agents/<id>/tools/<tool_id>
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```
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**Agent types** (use exactly ONE per agent):
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**Tool types** (`tool_type` value → type-specific block):
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| Field | Type |
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|-------|------|
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| `ka_tile_id` | Knowledge Assistant |
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| `genie_space_id` | Genie Space |
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| `endpoint_name` | Model serving endpoint |
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| `uc_function_name` | UC function (`catalog.schema.func`) |
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| `connection_name` | MCP server (UC HTTP Connection) |
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| `tool_type` | Block | Use for |
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|---|---|---|
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| `genie_space` | `{"id": "<space_id>"}` | Natural language → SQL via Genie |
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| `knowledge_assistant` | `{"knowledge_assistant_id": "<ka_id>"}` | Document Q&A via a KA |
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| `uc_function` | `{"name": "catalog.schema.func"}` | UC SQL/Python function |
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| `uc_connection` | `{"name": "<connection_name>"}` | External MCP server via UC HTTP Connection |
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| `volume` | `{"name": "<full_volume_name>"}` | UC Volume browsing |
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| `app` | `{"name": "<app_name>"}` | Databricks App |
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| 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. |
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### Examples (training the supervisor)
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Examples must use `--json` — the positional `GUIDELINES` arg doesn't accept any encoding because guidelines is a `repeated string`.
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```bash
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databricks supervisor-agents create-example supervisor-agents/<id> --json '{
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"question": "What were Q4 revenue numbers?",
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"guidelines": ["Route to analyst Genie space", "Always group by region"]
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}'
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databricks supervisor-agents list-examples supervisor-agents/<id>
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databricks supervisor-agents get-example supervisor-agents/<id>/examples/<ex_id>
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databricks supervisor-agents delete-example supervisor-agents/<id>/examples/<ex_id>
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```
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**Status:** `NOT_READY` (up to ~10 min after create/big update) → `ONLINE` → `OFFLINE`
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**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>`.
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---
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