Target Tables and Their Purpose

The integration populates distinct tables categorized by AI system components, models, prompts, tools, subcomponents, and usage. Each table captures relevant attributes mapped from n8n data to enable comprehensive asset and configuration management.

• AI Systems:
  • cmdbaisystemcomponentproductmodel - Stores attributes like name and version of AI system components.
  • almaisystemdigitalasset - Contains external reference IDs, connection URLs, display names, install status, AI models, and language models.
  • cmdbcifunctionai - Holds AI function details including name, object ID, install status, model ID, vendor, and system ID.
• AI Models:
  • cmdbaimodelproductmodel - Captures model name and version.
  • almaimodeldigitalasset - Records external reference ID, connection URL, model name, and type version.
• AI Prompts:
  • cdmbaipromptproductmodel - Stores prompt name, prompt information, parameters, and system messages.
  • almaipromptdigitalasset - Captures external reference ID, asset type, and asset ID.
• AI Tools:
  • snentaitool - Includes external reference ID, workflow plus node names, description, and active status.
• AI Subcomponents:
  • snentaisystemsubcomponentm2m - Maps AI systems to subcomponents using connection URLs, workflow IDs, and node IDs.
  • aisubcomponentreferencetable - References subcomponents such as agents or tools.
• AI Usage:
  • snaidiscaiusage - Tracks AI system usage details including session ID, time, and execution count.

Practical Implications for ServiceNow Customers

By leveraging these target tables, customers can efficiently manage AI assets and their relationships within the ServiceNow platform. The structured data enables better visibility into AI workflows, components, models, prompts, and tool usage. This facilitates informed decision-making, compliance tracking, and enhanced operational management of AI systems integrated through n8n.