Exploring AI Agent Topology Mapping

  • Release version: Zurich
  • Updated March 12, 2026
  • 3 minutes to read
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    Summary of Exploring AI Agent Topology Mapping

    AI Agent Topology Mapping enhances ServiceNow’s pattern-based discovery framework to identify and track AI infrastructure components across cloud platforms. It populates the Configuration Management Database (CMDB) with AI-specific configuration items (CIs), providing centralized visibility of AI agents, AI models, and AI prompts alongside traditional IT assets. This unified visibility supports managing AI infrastructure in conjunction with overall IT operations.

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    Key Features

    • Pattern-Based Discovery: Uses discovery patterns to automatically find AI agents, foundational AI models, and prompt configurations from multiple cloud platforms, updating the CMDB accordingly.
    • Role-Based Access: Provides multiple user roles—such as Discovery Admin, PD Admin, and PDE Viewer—with specific permissions to create, edit, view, or run discovery patterns and logs.
    • Multi-Cloud Support: Enables discovery of AI resources across diverse cloud environments using a single application.
    • Discovery Workflow: Involves installing patterns from the ServiceNow Store, configuring cloud credentials, scheduling and running discovery, and monitoring results to maintain up-to-date AI infrastructure data.

    Benefits

    • Centralized AI Infrastructure Visibility: Consolidates AI components with IT assets in the CMDB, supporting AI governance and operational oversight.
    • Automated and Recurring Discovery: Ensures continuous, automated updates of AI infrastructure states to keep the CMDB current.
    • Security and Compliance: Tracks AI component versions and configurations to facilitate governance, vulnerability management, and compliance with security policies.
    • Change Impact and Service Mapping: Provides near real-time insights into AI agent dependencies and topology, enhancing ITSM, AIOps, and service mapping aligned with the Common Service Data Model (CSDM).

    Practical Application for ServiceNow Customers

    ServiceNow customers can leverage AI Agent Topology Mapping to gain comprehensive visibility into their AI infrastructure alongside traditional IT assets, enabling better governance, security, and operational management. By automating discovery across multiple cloud platforms, customers maintain an accurate and current inventory of AI agents, models, and prompts. This supports risk mitigation, compliance, and integration of AI components into broader IT service management and AIOps processes.

    Learn how AI Agent Topology Mapping discovers AI infrastructure components across cloud platforms using patterns.

    AI Agent Topology Mapping overview

    AI Agent Topology Mapping extends the pattern-based discovery framework to identify and track AI-specific components in your environment. The application uses patterns to discover AI components from cloud platforms, populating the CMDB with configuration items (CIs). This approach provides centralized visibility into your AI infrastructure alongside traditional IT assets. For more information about how patterns work, see Discovery patterns used by ITOM Visibility.

    AI Agent Topology Mapping discovers the following AI components:
    • AI Agents: Intelligent entities that perform tasks and orchestrate AI workflows
    • AI Models: Foundational models that power AI capabilities, such as large language models
    • AI Prompts: Instructions and configurations that guide agent behavior and responses

    AI Agent Topology Mapping users

    The following user roles have access to patterns or pattern-related modules and can perform various actions. Note that customizing patterns requires basic knowledge of programming.

    Table 1. AI Agent Topology Mapping users and access
    User Description
    Discovery admin Can view, create, edit, and publish patterns. The role enables users to run discovery, migrate probes or CAPI to patterns, and access discovery logs and dashboards.
    PD user Has read-only access to Discovery Pattern Log.
    PD admin Can view, create, edit, and publish patterns.
    PDE viewer Starting with Pattern Designer Enhancements version 3.9.0, users can view Command Validation Tasks, Command Validation Tasks Results, and Command List.

    The pde_viewer can view the Command Validation Tool modules and related tables, but doesn't have permissions to modify or edit them.

    The pde_viewer role can view the following tables only:
    • Command List [pd_command_list]
    • Command Validation Task [pd_command_validation]
    • Command Validation Task Results [pd_command_validation_results]
    • Pattern Shared Library Mapping [pd_pattern_to_shared_library_mapping]
    • Temporary Variable Mappings [pd_temp_variable_value_mapping]
    PD MID Not assigned to a user directly but to the MID Server record or the user under which the MID Server runs. The role enables the MID Server to interpret and run pattern-based probes.
    MID Server Can grant the MID Server access to the instance.

    AI Agent Topology Mapping workflow

    The following workflow describes how a discovery administrator uses AI Agent Topology Mapping to discover and track AI infrastructure components.

    1. Install AI Agent Topology Mapping patterns from the ServiceNow Store.
    2. Configure cloud credentials with appropriate permissions for AI platforms.
    3. Create or update discovery schedules for environments containing AI resources.
    4. Run discovery to identify AI components and populate the CMDB.
    5. View discovered AI agents, models, and prompts in CMDB and non-CMDB tables.
    6. Monitor discovery logs to verify successful pattern execution.
    7. Schedule recurring discovery to maintain up-to-date AI infrastructure inventory.

    AI Agent Topology Mapping benefits

    Table 2. AI Agent Topology Mapping benefits
    Benefit Description
    Centralized AI infrastructure visibility Visibility into AI agents, models, and prompts alongside other IT assets in the CMDB, supporting AI Control Tower outcomes.
    Automated discovery Patterns automatically discover AI agents from supported cloud platforms during scheduled runs and populate the CMDB.
    Multi-cloud support Discovery of AI resources across multiple cloud platforms using a single application.
    Security and compliance Tracking of AI component versions and configurations to help maintain governance and compliance requirements for AI deployments.
    Vulnerability management Tracking of AI component versions and dependencies to identify security risks.
    Change impact analysis Near real-time visibility into AI agent dependency and topology relationships to support ITSM and AIOps use cases.
    Business context and service mapping Discovered AI assets serve as the foundation for building tag-based service maps aligned with the Common Service Data Model (CSDM).

    What to explore next

    To learn more about configuring and using AI Agent Topology Mapping, see: