---
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


---

# Amazon Bedrock

# Amazon Bedrock pattern-based discovery {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 10 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 Amazon Bedrock pattern-based discovery

The Amazon Bedrock pattern-based discovery in ServiceNow's AI Agent Topology Mapping application enables automated discovery of Amazon Bedrock AI services, agents, and models during horizontal discovery.
This process populates both CMDB and non-CMDB tables with detailed information about AI resources, providing visibility and management capabilities for Amazon Bedrock assets within your ServiceNow environment.
Show full answer Show less  

## Discovery Setup and Requirements

* **REST API Permissions:** Verify necessary permissions by downloading the Cloud Discovery patterns spreadsheet, which includes pattern details and vendor documentation. Keep this spreadsheet updated quarterly.
* **Application Updates:** Ensure the latest versions of AI Agent Topology Mapping, Discovery and Service Mapping Patterns, Visibility Content CMDB CI Class Models, and Data Foundation Model (version 1.13.2 or higher) are installed.
* **CloudFormation Template Stack:** Update the `getOperationGR(type)` method to ensure correct resource listing if using Cloud Provisioning and Governance.
* **CI Relationships:** Activate cloud-related CI relationships from version 1.0.68 for integration with service instances via tag-based discovery.
* **AWS Service Accounts:** Set up AWS management and member accounts with appropriate permissions. Utilize AWS Organizations to simplify member account management and optionally use dynamic credentials acquisition for member accounts.
* **IAM User Policy and MID Server:** Configure MID Server for AWS IAM roles to use IAM user policies for discovery authentication.
* **Discovery Scheduling:** Create AWS Discovery schedules in the Discovery Admin Workspace. Optional configurations include discovering only new members and optimizing discovery to include only datacenters with resources by using Resource Explorer API permissions and MID Server properties.

## Data Collection and Models

The application uses two main discovery patterns:

* **Amazon AWS - Bedrock Agents:** Discovers AI System Digital Assets, AI Prompt Digital Assets, and key-value tagging information.
* **Amazon AWS - Bedrock Foundation Model:** Discovers AI Model Digital Assets.

This discovery populates detailed data across several CMDB and non-CMDB tables, including:

* **AI System Digital Asset:** Contains attributes like display name, ARN, agent state, vendor (Amazon), related AI prompts, models, and linked CI records.
* **AI System Component Product Model:** Stores agent operational status, version, and manufacturer details.
* **AI Prompt Digital Asset and Product Model:** Capture prompt instructions, asset type, vendor, and model identifiers.
* **AI Model Digital Asset and Product Model:** Store foundation model details such as ARN, description, status, and manufacturer.
* **CMDB AI Function and AI Model Deployment:** Represent Bedrock agents and foundation models as configuration items with operational and install statuses, descriptions, and identifiers.
* **Key Value Table:** Stores AWS tags discovered in Bedrock agents.

## Dependency and Relationship Mapping

The discovery process establishes relationships between AI components, including:

* **Deployment relationships:** Between AI Functions and AI System Digital Assets.
* **Hosting relationships:** Between AI Functions or AI Model Deployments and AWS Datacenters.
* **Usage relationships:** Between AI Model Deployments and AI Model Digital Assets.

These relationships enable a comprehensive dependency view of Amazon Bedrock resources within the ServiceNow CMDB, facilitating impact analysis and management.

## Practical Benefits for ServiceNow Customers

* Automates discovery and mapping of Amazon Bedrock AI services and models, enhancing visibility and control.
* Provides detailed asset and configuration data integrated into the CMDB for better lifecycle management.
* Supports dynamic credential handling and optimized discovery for efficient cloud resource management.
* Enables detailed dependency mapping to understand relationships and operational status of AI resources.  
AI Agent Topology Mapping discovers Amazon Bedrock AI services, agents, and models during horizontal discovery.

## Request new or enhanced Patterns on the ServiceNow® Store {#amazon-bedrock-pattern__id_ztn_lkb_5yb}

Visit the [ServiceNow Store](https://store.servicenow.com/sn_appstore_store.do#!/store/application/06a71b1367e4130051c9027e2685ef1e/1.6.0?referer=/store/search?listingtype=allintegrations%253Bancillary_app%253Bcertified_apps%253Bcontent%253Bindustry_solution%253Boem%253Butility%253Btemplate&q=Patterns&sl=sh) to view all the available updates and for information about submitting requests to the store. For cumulative release notes information for all released apps, see the [ServiceNow Store version history release notes](https://www.servicenow.com/docs/r/store-release-notes/sn-store-release-notes.html).{#amazon-bedrock-pattern__p_d21_nfg_h1c}

## Amazon Bedrock data model {#amazon-bedrock-pattern__section_lsx_wjc_43c}

The following diagram illustrates the tables and relationships that the AI Agent Topology Mapping application creates when discovering Amazon Bedrock resources.  
Figure 1. Tables and relationships populated during Amazon Bedrock discovery

## Discovery requirements {#amazon-bedrock-pattern__section_prerequisites}

Verify the REST API permissions
:   Download the [Cloud Discovery patterns spreadsheet](https://downloads.docs.servicenow.com/resource/enus/api/servicenow-discovery-patterns-api-details.xlsx) so you can grant user permissions required for running the Discovery patterns. In addition to permissions, the spreadsheet also includes useful information such as pattern names, types, CI Classes, and links to vendor documentation. New patterns are available quarterly, so check periodically to be sure you have the latest version of the spreadsheet.{#amazon-bedrock-pattern__cloud-discovery-api-ph-short-prereq}

Verify that the applications are up to date
:   Discovering these resources may require updating to the latest version of the following applications from the ServiceNow Store:

    * AI Agent Topology Mapping
    * Discovery and Service Mapping Patterns
    * Visibility Content
    * CMDB CI Class Models
    * Data Foundation Model (at least version 1.13.2)
    {#amazon-bedrock-pattern__ul_dlg_343_23c}

Update the method used for pointed discovery for the AWS CloudFormation Template (CFT) stack
:   If you use Cloud Provisioning and Governance, you must update the getOperationGR(type) method. This update enables the pointed discovery to list the resources correctly for the AWS CFT stack after provisioning. For further information about the steps required to update this method, see the Knowledge Base article [KB0858437](https://support.servicenow.com/nav_to.do?uri=kb_knowledge.do?sys_id=54ecb719db1f1cd0fb115583ca961917).

Activate the cloud-related CI relationships
:   To include discovered components into service instances, enable CI relationships used in tag-based discovery by Service Mapping. These CI relationships are available from the 1.0.68 release on the ServiceNow Store. For operational steps, see [Tag-based discovery configuration](https://www.servicenow.com/docs/eIOd7ZdFc8jWAKQKu1dUoQ "You can refine the default configuration to control which CIs Service Mapping includes in application services during the tag-based discovery process.").

Set up service accounts on the AWS Management Console

:   An AWS Organization is a collection of AWS accounts under a single account. In AWS Organizations, parent accounts are called management accounts. The sub-accounts that belong to a management account are called member accounts.

    The advantages of using management accounts in Discovery are:

    Easy population of member accounts
    :   After you configure the management account and supply the necessary credentials, you can test the connection to the account. If the test succeeds, Discovery returns a list of the member accounts in that management account. From this list, you can choose one or more member account to include in the Discovery of the management account.

    (Optional) Discover member resources using dynamically acquired credentials

    :   When you run Discovery on your cloud resources, you don't need separate credentials for each member account. The Cloud Discovery process handles credentials automatically by acquiring a temporary credential for each member via an AWS API. You can elect to use the default configuration or customize the MID Server to assume other roles for additional controls and security.

    For more information, see [Setup AWS service accounts](https://www.servicenow.com/docs/XjCuXe2~UAY_JXp20uUyNg "Create and configure cloud service accounts at ServiceNow AI Platform for the corresponding Amazon Web Services (AWS) service accounts.").

Use IAM user policy on the AWS Management Console
:   To use the IAM user policy instead of credentials during discovery, configure the MID Server for AWS IAM roles. For more information, see [configure the MID Server for AWS IAM roles](https://www.servicenow.com/docs/wZ_mr2Yr6apRTXLykyecnA "Configure the MID Server to retrieve the temporary security credentials associated with an IAM role.").

    To create the IAM user policy for provisioning AWS resources, see [Control AWS access and permissions using policies](https://www.servicenow.com/docs/uYVyBGyqnEYCvAxt_DljiQ "Configure policies with the necessary level of permissions to provide access to the AWS resources for Cloud Discovery and Cloud Provisioning and Governance.").

Configure access to the AWS resources

:   To discover a single account, create an IAM account in the AWS Management Console, and verify that it has the "ReadOnlyAccess" policy applied. To discover several member or child accounts, configure the credentials as described in [Access setup for AWS service accounts](https://www.servicenow.com/docs/8TVb2LXNYvGM9lxFYzZvHw "Cloud Discovery and Cloud Provisioning and Governance need access to resources in the Amazon Web Services (AWS) service accounts. Learn about different methods of configuring such access.").

Configure the Discovery schedule to support GovCloud
:   Discovering AWS GovCloud (US) accounts requires using a datacenter URL when setting up an AWS service account. For more information, see [Create AWS service accounts](https://www.servicenow.com/docs/AzWp20Be9IzaYIP2IRkfmg "Create AWS service accounts on the ServiceNow AI Platform to access your AWS account during AWS discovery.").

Configure a discovery schedule
:   [Create an AWS Discovery schedule in Discovery Admin Workspace](https://www.servicenow.com/docs/LZBq1FJxrmpQKAqlRakl4g "Use the Discovery Admin Workspace dashboard to create an Amazon Web Services (AWS) Discovery schedule.").

(Optional) Discover datacenters only for new members
:   Starting with Zurich Patch 2, you can discover datacenters only for new members added since the last discovery. For more information, see [Discover datacenters only for new cloud accounts](https://www.servicenow.com/docs/oq~lB4k3k682OyqD4WHyZQ "If you have multiple cloud accounts and datacenters in AWS and Azure, you can discover datacenters for new cloud accounts only, instead of refreshing the entire list.").

Optimize discovery by including only datacenters with resources
:   You can optimize discovery by limiting it to only AWS datacenters with resources.

    * Verify that your service account has the following IAM permissions to access the Resource Explorer API (starting with Discovery and Service Mapping Patterns version 1.35.0).  
      {#amazon-bedrock-pattern__table_flj_f25_zjc__entry__2}

      | IAM permission | Coverage |
      |-|-|
      | `resource-explorer-2:Search` | Partial |
      | `resource-explorer-2:Search` + `iam:CreateServiceLinkedRole` | Full |
      [Table 1. Resource Explorer API permissions and coverage]

      {#amazon-bedrock-pattern__table_flj_f25_zjc}For more information, go to the [AWS Documentation](https://docs.aws.amazon.com/) and search for the "Getting started with Resource Explorer" article.{#amazon-bedrock-pattern__verify-optimize-aws-resource-explorer-api}
    {#amazon-bedrock-pattern__verify-optimize-aws-resource-explorer-api}
    * Discover only datacenters with resources by setting the mid.cloud.discovery.sonar.discover_all_aws_datacenters MID Server property to false. For more information, see [Limit AWS discovery to datacenters with resources](https://www.servicenow.com/docs/6S~EBHTJSEJXS_Luq3UIEg "Optimize AWS discovery by limiting it to datacenters with resources.").{#amazon-bedrock-pattern__enable-discovery-aws-datacenters-property}
    {#amazon-bedrock-pattern__enable-discovery-aws-datacenters-property}
    * To exclude specific resource types from AWS datacenter discovery, configure the sn_itom_pattern.discovery.aws.ldc.excluded_resource_types system property with a comma-separated list of resource types to exclude (starting with Discovery and Service Mapping Patterns version 1.35.0). For more information, see [Exclude AWS resource types from datacenter discovery](https://www.servicenow.com/docs/G~phWBRWTiKB5q1Hn8FCVA "You can configure which AWS resource types to exclude from AWS datacenter discovery, to avoid triggering discovery patterns in regions with only default resources.").{#amazon-bedrock-pattern__exclude-aws-resource-types-postreq}
    {#amazon-bedrock-pattern__exclude-aws-resource-types}

    {#amazon-bedrock-pattern__ul_cqf_q1c_fhc}  
    Note:  
    Discovery and Service Mapping Patterns versions 1.29.0 through 1.32.0 used the AWS Config service instead of the Resource Explorer API to determine datacenter activity. For instructions on configuring AWS Config recorder, go to the [AWS Documentation](https://docs.aws.amazon.com/) and search for the "Recording resources in the AWS Config console" article.
:   For more information, see the AWS resources discovery by datacenters section in [AWS discovery using patterns](https://www.servicenow.com/docs/Y0yjdHKR~_I76hi21l9C4A "Discovery and Service Mapping Patterns uses patterns to discover components of the Amazon AWS Cloud deployment during horizontal discovery. Discovering some of these resources might require updating to the latest version of the Discovery and Service Mapping Patterns application from the ServiceNow Store.").

(Optional) Populate Service Account and Logical Datacenter fields in cloud CIs
:   Starting with Discovery and Service Mapping Patterns version 1.30.2, you can improve query performance by populating Service Account and Logical Datacenter fields directly in cloud CIs. For more information, see [Improved query performance with direct field population in CI tables](https://www.servicenow.com/docs/v2LqO55fNZJKQV3ctb~rBg "The Populate Service Account and LDC IN CMDB scheduled job populates the Service Account and Logical Datacenter fields in cloud configuration item (CI) tables, and the Virtual Machine Object field in the Hardware [cmdb_ci_hardware] table. This direct population reduces query complexity and improves query performance.").

## Data collection {#amazon-bedrock-pattern__section_dvn_tcp_l3c}

The AI Agent Topology Mapping application includes the following patterns for Amazon Bedrock discovery:

* Amazon AWS - Bedrock Agents
* Amazon AWS - Bedrock Foundation Model
{#amazon-bedrock-pattern__ul_bedrock_patterns}

The Amazon AWS - Bedrock Agents pattern collects AI System Digital Assets, AI Prompt Digital Assets, and Key Value information. You can view this information in the Related Links section of the CI record by
navigating to AllConfigurationAWSAWS Bedrock Agents and selecting the relevant record.

The Amazon AWS - Bedrock Foundation Model pattern collects AI Model Digital Assets. You can view this information in the Related Links section of the CI record by navigating to AllConfigurationAWSAWS Bedrock Models and selecting the relevant record.

AI Agent Topology Mapping application populates data in both CMDB and non-CMDB tables.

## Data stored in non-CMDB tables {#amazon-bedrock-pattern__section_nbj_bcp_l3c}

The AI Agent Topology Mapping application populates data in non-CMDB tables when running the Amazon Bedrock patterns.

If you have the AI Control Tower application, you can view the information in the AI Control Tower Workspace.  
{#amazon-bedrock-pattern__table_alm_ai_system__entry__2}

| Field | Description |
|-|-|
| Display name \[display_name\] | Display name of the Bedrock agent. |
| External record reference \[external_ref_id\] | Amazon Resource Name (ARN) of the Bedrock agent. |
| Model \[model\] | References the AI System Component Product Model \[cmdb_ai_system_component_product_model\] table. |
| Asset type \[model_category\] | Asset type of the agent. Value is set to Agentic AI. |
| State \[install_status\] | State of the agent. Value is set to Deployed. |
| Vendor \[vendor\] | Vendor of the agent. The value is set to Amazon. |
| AI prompts \[ai_prompts\] | References the AI Prompt Digital Asset \[alm_ai_prompt_digital_asset\] table. |
| AI models \[ai_models\] | References the AI Model Digital Asset \[alm_ai_model_digital_asset\] table. |
| Configuration Item \[ci\] | References the AI Function \[cmdb_ci_function_ai\] table. |
[Table 2. AI System Digital Asset \[alm_ai_system_digital_asset\]]

{#amazon-bedrock-pattern__table_alm_ai_system}  
{#amazon-bedrock-pattern__table_cmdb_ai_system_component__entry__2}

| Field | Description |
|-|-|
| Name \[name\] | Name of the Bedrock agent. |
| Short description \[short_description\] | Description of the Bedrock agent. |
| Status \[status\] | Operational status of the agent as returned by Bedrock. For example: PREPARED. |
| Version \[version\] | Version of the Bedrock agent. |
| Manufacturer \[manufacturer\] | Name of the company defined in the glide.appcreator.company.friendly_name system property. |
| Model categories \[cmdb_model_category\] | Model category. Value is set to Agentic AI. |
[Table 3. AI System Component Product Model \[cmdb_ai_system_component_product_model\]]

{#amazon-bedrock-pattern__table_cmdb_ai_system_component}  
{#amazon-bedrock-pattern__table_alm_ai_prompt__entry__2}

| Field | Description |
|-|-|
| Prompt information \[prompt_info\] | Instruction text defined for the Bedrock agent. |
| Model \[model\] | References the AI Prompt Product Model \[cmdb_ai_prompt_product_model\] table. |
| Asset type \[model_category\] | Asset type of the prompt. Value is set to AI prompt. |
| Vendor \[vendor\] | Vendor of the prompt. The value is set to Amazon. |
| Configuration Item \[ci\] | References the AI Function \[cmdb_ci_function_ai\] table. |
[Table 4. AI Prompt Digital Asset \[alm_ai_prompt_digital_asset\]]

{#amazon-bedrock-pattern__table_alm_ai_prompt}  
{#amazon-bedrock-pattern__table_cmdb_ai_prompt_product_model__entry__2}

| Field | Description |
|-|-|
| Name \[name\] | Name of the Bedrock agent, used to identify the associated prompt model. |
| Manufacturer \[manufacturer\] | Name of the company defined in the glide.appcreator.company.friendly_name system property. |
[Table 5. AI Prompt Product Model \[cmdb_ai_prompt_product_model\]]

{#amazon-bedrock-pattern__table_cmdb_ai_prompt_product_model}  
{#amazon-bedrock-pattern__table_alm_ai_model__entry__2}

| Field | Description |
|-|-|
| Display name \[display_name\] | Name of the foundation model. |
| External record reference \[external_ref_id\] | ARN of the foundation model. |
| Model \[model\] | References the AI Model Product Model \[cmdb_ai_model_product_model\] table. |
| Asset type \[model_category\] | Asset type of the model. Value is set to AI model. |
| Vendor \[vendor\] | Provider of the foundation model. |
| Configuration Item \[ci\] | References the AI Model Deployment \[cmdb_ci_ai_model_deployment\] table. |
[Table 6. AI Model Digital Asset \[alm_ai_model_digital_asset\]]

{#amazon-bedrock-pattern__table_alm_ai_model}  
{#amazon-bedrock-pattern__table_cmdb_ai_model_product_model__entry__2}

| Field | Description |
|-|-|
| Name \[name\] | Name of the foundation model. |
| Model number \[model_number\] | Model ID assigned by Amazon Bedrock. |
| Description \[description\] | Description of the foundation model. |
| Manufacturer \[manufacturer\] | Provider of the foundation model. |
| Model categories \[cmdb_model_category\] | Model category. Value is set to AI model. |
[Table 7. AI Model Product Model \[cmdb_ai_model_product_model\]]

{#amazon-bedrock-pattern__table_cmdb_ai_model_product_model}

## Data stored in CMDB tables {#amazon-bedrock-pattern__section_data_collected}

AI Agent Topology Mapping populates the data in the CMDB when running the Amazon Bedrock patterns.  
{#amazon-bedrock-pattern__table_ai_function__entry__2}

| Field | Description |
|-|-|
| Name \[name\] | Name of the Bedrock agent. |
| Object ID \[object_id\] | Concatenation of the agent ARN and version. For example: arn:aws:bedrock:us-east-1:123456789012:agent/AGENTID123/versions/DRAFT. |
| Description \[short_description\] | Description of the Bedrock agent. |
| Model number \[model_number\] | Agent ID assigned by Amazon Bedrock. |
| Operational status \[operational_status\] | Operational status of the resource. Default value is set to Operational. |
| Install Status \[install_status\] | Install status of the resource. Default value is set to Installed. |
| Product instance identifier \[product_instance_id\] | Version of the Bedrock agent. |
| Comments \[comments\] | Resource type identifier. Value is set to AWS::Bedrock::Agent. |
| Manufacturer \[manufacturer\] | Manufacturer of the resource. Value is set to AWS. |
[Table 8. AI Function \[cmdb_ci_function_ai\]]

{#amazon-bedrock-pattern__table_ai_function}  
{#amazon-bedrock-pattern__table_ai_model_deployment__entry__2}

| Field | Description |
|-|-|
| Name \[name\] | Name of the Amazon Bedrock foundation model. |
| Object ID \[object_id\] | ARN of the foundation model. |
| Description \[short_description\] | Description of the foundation model. |
| Model number \[model_number\] | Model ID assigned by Amazon Bedrock. |
| Manufacturer \[manufacturer\] | Provider of the foundation model. |
| Attributes \[attributes\] | Lifecycle status of the model as reported by the API. For example: ACTIVE or LEGACY. |
| Comments \[comments\] | Resource type identifier. Value is set to AWS::Bedrock::Foundation Model. |
| Operational status \[operational_status\] | Operational status of the resource. Default value is Operational. |
| Install Status \[install_status\] | Install status of the resource. Default value is Installed. |
[Table 9. AI Model Deployment \[cmdb_ci_ai_model_deployment\]]

{#amazon-bedrock-pattern__table_ai_model_deployment}

## Dependency Views map {#amazon-bedrock-pattern__section_olb_r4v_n3c}

On the Dependency Views map, you can view discovered Amazon Bedrock resources and the relationships between them.  
Note:  
The following relationships are stored in the Asset-CI Relationship \[cmdb_rel_asset_ci\] table and aren't represented in the Dependency View map:

* The Deployed as::Deployment of relationship between AI Function \[cmdb_ci_function_ai\] and AI System Digital Asset \[alm_ai_system_digital_asset\].
* The Used by::Uses relationship between AI Model Deployment \[cmdb_ci_ai_model_deployment\] and AI Model Digital Asset \[alm_ai_model_digital_asset\].
{#amazon-bedrock-pattern__ul_asset_ci_rels}  
Figure 2. Amazon Bedrock dependency view

## CI relationships and references {#amazon-bedrock-pattern__section_relationships}

The Amazon Bedrock patterns create the following relationships and references to support Amazon Bedrock discovery. References link to records in other tables and don't appear in the CI Relationship \[cmdb_rel_ci\] table.  
{#amazon-bedrock-pattern__table_relationships__entry__3}

| CI/Table | Relationship | CI/Table |
|-|-|-|
| AI Function \[cmdb_ci_function_ai\] | Deployed as::Deployment of | AI System Digital Asset \[alm_ai_system_digital_asset\] |
| AI Function \[cmdb_ci_function_ai\] | Hosted on::Hosts | AWS Datacenter \[cmdb_ci_aws_datacenter\] |
| AI Function \[cmdb_ci_function_ai\] | Depends on::Used by | AI Model Deployment \[cmdb_ci_ai_model_deployment\] |
| AI Model Deployment \[cmdb_ci_ai_model_deployment\] | Hosted on::Hosts | AWS Datacenter \[cmdb_ci_aws_datacenter\] |
| AI Model Deployment \[cmdb_ci_ai_model_deployment\] | Used by::Uses | AI Model Digital Asset \[alm_ai_model_digital_asset\] |
[Table 10. CI relationships]

{#amazon-bedrock-pattern__table_relationships}  
{#amazon-bedrock-pattern__table_references__entry__3}

| CI/Table | Field | Referenced CI/Table |
|-|-|-|
| AI System Digital Asset \[alm_ai_system_digital_asset\] | Model \[model\] | AI System Component Product Model \[cmdb_ai_system_component_product_model\] |
| AI System Digital Asset \[alm_ai_system_digital_asset\] | AI prompts \[ai_prompts\] | AI Prompt Digital Asset \[alm_ai_prompt_digital_asset\] |
| AI System Digital Asset \[alm_ai_system_digital_asset\] | AI models \[ai_models\] | AI Model Digital Asset \[alm_ai_model_digital_asset\] |
| AI System Digital Asset \[alm_ai_system_digital_asset\] | Configuration Item \[ci\] | AI Function \[cmdb_ci_function_ai\] |
| AI Prompt Digital Asset \[alm_ai_prompt_digital_asset\] | Model \[model\] | AI Prompt Product Model \[cmdb_ai_prompt_product_model\] |
| AI Prompt Digital Asset \[alm_ai_prompt_digital_asset\] | Configuration Item \[ci\] | AI Function \[cmdb_ci_function_ai\] |
| AI Model Digital Asset \[alm_ai_model_digital_asset\] | Model \[model\] | AI Model Product Model \[cmdb_ai_model_product_model\] |
| AI Model Digital Asset \[alm_ai_model_digital_asset\] | Configuration Item \[ci\] | AI Model Deployment \[cmdb_ci_ai_model_deployment\] |
| Key Value \[cmdb_key_value\] | Configuration item \[configuration_item\] | AI Function \[cmdb_ci_function_ai\] |
[Table 11. CI references]

{#amazon-bedrock-pattern__table_references}

## AWS tag discovery {#amazon-bedrock-pattern__section_tag_discovery}

The Amazon AWS - Bedrock Agents pattern collects tags and populates them in the Key Value \[cmdb_key_value\] table.  
{#amazon-bedrock-pattern__table_key_value__entry__2}

| Field | Description |
|-|-|
| Key \[key\] | Tag name. |
| Value \[value\] | Tag value. |
| Configuration item \[configuration_item\] | References the AI Function \[cmdb_ci_function_ai\] table. |
[Table 12. Key Value \[cmdb_key_value\]]

{#amazon-bedrock-pattern__table_key_value}
**Related reference**   

* [AWS discovery using patterns](https://www.servicenow.com/docs/Y0yjdHKR~_I76hi21l9C4A "Discovery and Service Mapping Patterns uses patterns to discover components of the Amazon AWS Cloud deployment during horizontal discovery. Discovering some of these resources might require updating to the latest version of the Discovery and Service Mapping Patterns application from the ServiceNow Store.")

*[\>]: and then