---
sourceDocument: Brazil IT Operations Management
sourceDocumentLink: https://www.servicenow.com/docs/r/it-operations-management

 Release :

    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Brazil IT Operations Management

ft:clusterId :

    - itom

bundleId :

    - itom

workflow :

    - Technology


---

# Explore

# Exploring AI Agent Topology Mapping {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 3 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Exploring AI Agent Topology Mapping

AI Agent Topology Mapping is a ServiceNow application that extends the pattern-based discovery framework to identify and track AI-specific infrastructure components across various cloud platforms.
It uses discovery patterns to automatically find AI agents, models, and prompts, and populates the Configuration Management Database (CMDB) with these configuration items (CIs).
This capability integrates AI infrastructure visibility alongside traditional IT assets, enabling centralized monitoring and management.
Show full answer Show less  

## User Roles and Access

Different user roles manage and interact with AI Agent Topology Mapping patterns and modules:

* **Discovery Admin:** Full access to create, edit, publish patterns, run discoveries, migrate probes, and access logs and dashboards.
* **PD Admin:** Can view and modify patterns and related modules.
* **PD User:** Read-only access to Discovery Pattern Logs.
* **PDE Viewer:** Can view command validation tasks and related data without editing permissions.
* **PD MID:** Assigned to MID Server to enable it to interpret and run pattern-based probes.
* **MID Server:** Requires access to the instance for discovery operations.

## Typical Workflow

* Install AI Agent Topology Mapping patterns from the ServiceNow Store.
* Configure cloud credentials with necessary permissions for AI platform discovery.
* Create or update discovery schedules targeting environments with AI resources.
* Run discovery to identify AI agents, models, and prompts and populate the CMDB.
* Monitor discovery logs to ensure successful execution.
* Schedule recurring discoveries to keep AI infrastructure data current.

## Key Benefits

* **Centralized AI Infrastructure Visibility:** Provides a unified view of AI components alongside other IT assets to support AI Control Tower outcomes.
* **Automated Discovery:** Pattern-based automated identification of AI infrastructure during scheduled runs, reducing manual effort.
* **Multi-Cloud Support:** Enables discovery of AI resources across multiple cloud platforms within a single application.
* **Security and Compliance:** Tracks AI component versions and configurations to support governance and compliance requirements.
* **Vulnerability Management:** Helps identify security risks by tracking component versions and dependencies.
* **Change Impact Analysis:** Offers near real-time visibility into AI agent dependencies and topology to support ITSM and AIOps.
* **Business Context and Service Mapping:** Enables building tag-based service maps aligned with the Common Service Data Model (CSDM) using discovered AI assets.

## Next Steps

ServiceNow customers interested in implementing AI Agent Topology Mapping should review configuration guides and reference materials to effectively set up and leverage the discovery patterns for ongoing AI infrastructure management.  
Learn how AI Agent Topology Mapping discovers AI infrastructure components across cloud platforms using patterns.

## AI Agent Topology Mapping overview {#exploring-ai-agent-topology-mapping__cf-exploring-parent-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](https://www.servicenow.com/docs/XcFaWrr5M6~VjCLuqParHw "Service Mapping and Discovery use patterns in their discovery process that cover most industry standard network devices and applications. You can customize these patterns and create new ones.").  
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
{#exploring-ai-agent-topology-mapping__ul_mg3_jtg_23c}

## AI Agent Topology Mapping users {#exploring-ai-agent-topology-mapping__cf-exploring-parent-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.  
{#exploring-ai-agent-topology-mapping__table_mj5_2tg_23c__entry__2}

| 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 \[pd_temp_variable_value_mapping\] {#exploring-ai-agent-topology-mapping__ul_i54_bx2_ghc} |
| 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. |
[Table 1. AI Agent Topology Mapping users and access]

{#exploring-ai-agent-topology-mapping__table_mj5_2tg_23c}

## AI Agent Topology Mapping workflow {#exploring-ai-agent-topology-mapping__cf-exploring-parent-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.
{#exploring-ai-agent-topology-mapping__cf-exploring-parent-workflow-ol}

## AI Agent Topology Mapping benefits {#exploring-ai-agent-topology-mapping__cf-exploring-parent-benefits}

{#exploring-ai-agent-topology-mapping__table_jtn_13g_23c__entry__2}

| 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). |
[Table 2. AI Agent Topology Mapping benefits]

{#exploring-ai-agent-topology-mapping__table_jtn_13g_23c}

## What to explore next {#exploring-ai-agent-topology-mapping__cf-exploring-parent-links}

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

* [Configuring AI Agent Topology Mapping](https://www.servicenow.com/docs/I0hyv~EUVOqszdUlKh77RQ "Install AI Agent Topology Mapping from the ServiceNow Store and configure discovery schedules to identify AI infrastructure components.")
* [AI Agent Topology Mapping reference](https://www.servicenow.com/docs/LJQNsX_vHDrYLz7_VhGTXg "Reference topics provide pattern information for AI Agent Topology Mapping, including prerequisites, tables, fields, and relationships.")
{#exploring-ai-agent-topology-mapping__ul_ktn_13g_23c}