Default Agents Reference

These are commonly used system-provided agents. This is not an exhaustive list — to see all available agents (including custom agents), run:

python -m cli agent list

Default agents can be used directly or cloned to create custom variations.

Available Default Agents

SQL Query Agent (sql_query_agent)

Purpose: Write and execute SQL queries against data products.

Tools:

• sql_execution_tool - Execute SQL queries
• get_data_schema_tool - Retrieve schema information

Input Schema:

{
  "message": "User's question",
  "data_product_id": "Data product to query",
  "data_product_version": "Optional version"
}

Best For: Direct database queries, data extraction, analytics questions.

Catalog Search Agent (catalog_search_agent)

Purpose: Search and explore the data catalog.

Tools:

• search_catalog_tool - Search catalog objects
• get_context_by_id_tool - Get detailed context

Input Schema:

{
  "message": "Search query or question"
}

Best For: Finding tables, columns, data assets, understanding data landscape.

Charting Agent (charting_agent)

Purpose: Create data visualizations and charts.

Tools:

• generate_chart_tool - Create visualizations
• generate_chart_from_sql_and_code_tool - Charts from SQL results

Input Schema:

{
  "message": "What to visualize"
}

Best For: Creating bar charts, line graphs, pie charts from data.

Deep Research Agent (deep_research_agent)

Purpose: Multi-step research and comprehensive analysis.

Tools: Multiple search, retrieval, and analysis tools.

Input Schema:

{
  "message": "Research question or topic"
}

Output: Structured research plan with summary.

Best For: Complex questions requiring multiple data sources, thorough analysis.

Data Product Query Agent (data_product_query_agent)

Purpose: Query data products with automatic product discovery.

Tools:

• list_data_products_tool - Find available data products
• get_data_product_spec_tool - Get specifications
• sql_execution_tool - Execute queries

Input Schema:

{
  "message": "Query or question",
  "data_product_id": "Data product ID",
  "auth_id": "Optional auth credentials ID"
}

Best For: Querying when data product context is important.

BI Report Agent (bi_report_agent)

Purpose: Search and explore BI reports.

Tools:

• bi_report_search_tool - Search BI reports

Input Schema:

{
  "message": "What report to find"
}

Best For: Finding dashboards, reports, BI artifacts.

Query Flow Agent (query_flow_agent)

Purpose: Orchestrate multi-agent query workflows.

Tools: Multiple agent-as-tool configurations.

Input Schema:

{
  "message": "Complex query",
  "marketplace_id": "Optional marketplace filter"
}

Best For: Complex queries that need multiple specialized agents.

Curation Agent (curation_agent)

Purpose: Curate and manage catalog content.

Tools: Catalog management and update tools.

Input Schema:

{
  "message": "Curation request",
  "asset_ids": ["Optional", "list", "of", "assets"]
}

Best For: Updating metadata, descriptions, tags on catalog objects.

Alamigo Agent (alamigo_agent)

Purpose: General help with the Alation product itself (not for data queries).

Tools: Broad set of Alation-specific tools.

Input Schema:

{
  "message": "Any Alation-related question"
}

Best For: "How do I use X?" questions about the Alation product. Not suited for data queries — use other agents for that.

Catalog Context Search Agent (catalog_context_search_agent)

Purpose: Search catalog with rich context retrieval.

Tools:

• search_catalog_tool
• get_context_by_id_tool
• alation_context_tool

Input Schema:

{
  "message": "Search query with context needs"
}

Best For: When you need detailed context about found objects.

Revise Data Product Agent (revise_data_product_agent)

Purpose: Improve and revise data products based on evaluation.

Tools: Data product management and evaluation tools.

Input Schema:

{
  "message": "Revision request",
  "data_product_id": "Product to revise",
  "data_product_version": "Optional version"
}

Best For: Iteratively improving data product quality.

Using Default Agents

Get Default Agent Details

python -m cli agent get-default sql_query_agent

Clone for Customization

# Get the ID first
python -m cli agent get-default catalog_search_agent

# Clone it
python -m cli agent clone <agent-id>

# Customize the clone
echo '{"name": "My Custom Search", "prompt": "..."}' | \
  python -m cli agent update <new-id>

Selecting the Right Agent

This is a quick reference for common use cases. There may be other default or custom agents better suited to your task — run python -m cli agent list to see everything available.