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"welcome_message": "Welcome to AgentOps Stacks — a Declarative Automation Bundle template for production-ready AI agents on Databricks.\n\nThis template generates a multi-agent scaffold with shared components, CI/CD wiring, and per-agent Databricks Apps — all driven by your choices.",
"min_databricks_cli_version": "v1.1.0",
"properties": {
"input_project_name": {
"order": 1,
"type": "string",
"default": "my_agentops_project",
"description": "\nProject name. Used as the bundle name, default catalog suffix, and root directory name.",
"pattern": "^[a-z][a-z0-9_]{2,}$",
"pattern_match_failure_message": "Project name must start with a lowercase letter and contain only lowercase letters, digits, and underscores (min 3 chars)."
},
"input_root_dir": {
"order": 2,
"type": "string",
"default": "{{ .input_project_name }}",
"description": "\nRoot directory name."
},
"input_initial_agent_name": {
"order": 3,
"type": "string",
"default": "default",
"description": "\nName of the first agent. Additional agents can be added later.\ninput_initial_agent_name",
"pattern": "^[a-z][a-z0-9_]{2,}$",
"pattern_match_failure_message": "Agent name must start with a lowercase letter and contain only lowercase letters, digits, and underscores (min 3 chars)."
},
"input_cloud": {
"order": 4,
"type": "string",
"description": "\nCloud provider for the workspace. Determines CI/CD auth blocks.\ninput_cloud",
"default": "aws",
"enum": ["aws", "azure", "gcp"]
},
"input_cicd_platform": {
"order": 5,
"type": "string",
"description": "\nCI/CD platform for promotion workflows.\ninput_cicd_platform",
"description": "\nInclude shared Vector Search component for unstructured data retrieval (RAG).\ninput_use_vector_search",
"default": "no",
"enum": ["yes", "no"]
},
"input_has_chunked_table": {
"order": 7,
"type": "string",
"description": "\nDo you already have a chunked Delta table to sync from? If no, the scaffold includes ingestion and preparation notebooks.\ninput_has_chunked_table",
"description": "\nType of agent memory to configure with Lakebase.\ninput_memory_type",
"default": "short_term",
"enum": ["short_term", "long_term", "both"],
"skip_prompt_if": {
"properties": {
"input_use_lakebase": { "const": "no" }
}
}
},
"input_use_uc_functions": {
"order": 10,
"type": "string",
"description": "\nInclude Unity Catalog function tool references as a shared component.\ninput_use_uc_functions",
"default": "no",
"enum": ["yes", "no"]
},
"input_uc_functions_exist": {
"order": 11,
"type": "string",
"description": "\nAre the UC functions already defined in your catalog? If no, the scaffold includes definition stubs.\ninput_uc_functions_exist",
"default": "no",
"enum": ["yes", "no"],
"skip_prompt_if": {
"properties": {
"input_use_uc_functions": { "const": "no" }
}
}
},
"input_enable_cost_tags": {
"order": 12,
"type": "string",
"description": "\nTag project resources (project, environment, agent) for usage tracking and cost attribution in system.billing.usage.\ninput_enable_cost_tags",
"default": "yes",
"enum": ["yes", "no"]
},
"input_usage_policy_id": {
"order": 13,
"type": "string",
"description": "\nExisting serverless usage policy ID to attribute serverless usage (Apps, Vector Search, pipelines, Lakebase). Find it in the workspace under Settings > Compute > Serverless usage policies > Manage (the ID is shown on the policy's page). The policy must carry project and environment tags. Leave blank if you don't have one yet; you can create and wire it later with scripts/create_usage_policy.sh (see docs/usage-tracking.md).\ninput_usage_policy_id",
"pattern_match_failure_message": "Usage policy ID must be a UUID (e.g. 1a2b3c4d-1234-5678-9abc-0123456789ab) or blank.",
"skip_prompt_if": {
"properties": {
"input_enable_cost_tags": { "const": "no" }
}
}
},
"input_eval_dataset_source": {
"order": 14,
"type": "string",
"description": "\nHow to create the evaluation dataset for this agent.\n synthetic — generate a golden dataset using an LLM simulator\n manual — scaffold a notebook with example rows to fill in manually\n production_traces — build from production traces filtered by tag\n existing — skip dataset creation (you already have one)\ninput_eval_dataset_source",
"success_message": "\n*** AgentOps Stacks project created in '{{.input_root_dir}}' ***\n\nNext steps:\n 1. cd {{.input_root_dir}}/src/agents/{{.input_initial_agent_name}}\n 2. uv sync\n 3. cp .env.example .env — configure Databricks auth\n 4. uv run python app/start_server.py — run the agent locally\n 5. databricks bundle deploy -t dev — deploy to Databricks\n\nYour agent: src/agents/{{.input_initial_agent_name}}/agent.py\nEval config: src/agents/{{.input_initial_agent_name}}/eval/gates.yml{{ if (and (eq .input_enable_cost_tags \"yes\") (not .input_usage_policy_id)) }}\n\nUsage tracking: no usage policy ID set, so serverless usage is untagged. See docs/usage-tracking.md.{{ end }}"
