Exploring AI Agent Topology Mapping

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

    AI Agent Topology Mapping enhances the pattern-based discovery framework in ServiceNow to identify and track AI-specific components across cloud platforms. It uses discovery patterns to find AI infrastructure elements such as AI Agents, AI Models, and AI Prompts, and populates these as configuration items (CIs) in the CMDB. This integration provides centralized visibility of AI infrastructure alongside traditional IT assets, supporting comprehensive infrastructure management.

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    Key Features

    • AI Component Discovery: Detects intelligent AI agents, foundational AI models (e.g., large language models), and AI prompts that guide agent behavior.
    • Role-Based Access: Multiple user roles manage or view discovery patterns, including Discovery Admins (full pattern management and discovery execution), PD Users (read-only logs), PD Admins (pattern editing), PDE Viewers (view command validation tasks), and MID Server roles for executing pattern probes.
    • Multi-Cloud Support: Discovers AI resources across various cloud platforms using a unified application.
    • Automated and Scheduled Discovery: Supports configuration of cloud credentials and discovery schedules to keep AI component data current in the CMDB.

    AI Agent Topology Mapping Workflow

    • Install AI Agent Topology Mapping patterns from the ServiceNow Store.
    • Configure cloud credentials with necessary permissions.
    • Create or update discovery schedules for AI environments.
    • Run discovery to identify AI components and populate the CMDB.
    • Monitor discovery logs to ensure pattern execution success.
    • Schedule recurring discovery jobs to maintain up-to-date AI infrastructure data.

    Benefits

    • Centralized AI Infrastructure Visibility: Integrates AI assets into the CMDB, supporting AI Control Tower initiatives.
    • Automated Discovery: Patterns automatically detect AI components during scheduled runs.
    • Security and Compliance: Enables tracking of AI component versions and configurations for governance and risk management.
    • Vulnerability Management: Identifies security risks by tracking AI component versions and dependencies.
    • Change Impact Analysis: Provides near real-time insight into AI agent dependencies and topology to support ITSM and AIOps.
    • Business Context and Service Mapping: Uses discovered AI assets to build tag-based service maps aligned with the Common Service Data Model (CSDM).

    Next Steps

    To further optimize AI Agent Topology Mapping, ServiceNow customers should explore configuration guidance and reference materials on setting up and customizing AI component discovery patterns.

    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]
    For more information, see Discovery commands for probes and patterns.
    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: