Automate AI workflows with Red Hat Ansible Certified Content Collection amazon.ai for generative AI

Automation unleashed

December 10, 2025
Alina Buzachis
Related topics:
Artificial intelligenceAutomation and managementDeveloper ProductivityDevOps
Related products:
Red Hat Ansible Automation Platform

In part 1 of this blog, we introduced the Red Hat Ansible Certified Content Collection amazon.ai for generative AI and how it brings declarative automation to Amazon Bedrock and DevOps Guru.

Now it's time to move from theory to practice. In this post, we'll explore hands-on use cases that demonstrate how to automate AI workflows, from deploying Bedrock Agents to orchestrating DevOps Guru monitoring.

If you've ever felt the pain of manually managing AI agents, configuring multiple endpoints, or pulling operational insights for audits, this post is for you. By the end, you’ll see how you can treat AI infrastructure as code, which provides repeatable, auditable, and reliable automation.

Why automation matters in practice

Manual AI management isn't just slow; it's error-prone:

  • Inconsistent deployments: Recreating an agent or model in a new environment might produce subtle differences, which leads to unexpected failures.
  • Error-prone configuration: Complex action groups, API schemas, and IAM roles are easy to misconfigure manually.
  • Operational blind spots: Without automated monitoring, anomalies can go undetected, and audits become difficult or incomplete.
  • Limited scalability: Repeating manual tasks across multiple agents or services quickly becomes unsustainable.

The Red Hat Ansible Certified Content Collection amazon.ai for generative AI helps solve these challenges by providing declarative modules for Bedrock and DevOps Guru. These modules allow teams to:

  • Deploy and validate AI agents automatically.
  • Invoke foundation models programmatically.
  • Configure and audit operational monitoring at scale.
  • Generate compliance-ready reports.

In other words, you can now treat AI and its operational ecosystem as first-class code artifacts.

Use cases: amazon.ai in action

The playbooks below provide robust, comprehensive automation examples built around the new Red Hat Ansible Certified Content Collection amazon.ai.

Use case 1: End-to-end agent deployment, validation, and auditing

Suppose you want to deploy an AI-powered IT support assistant built on Amazon Bedrock. This agent helps employees resolve help desk issues, like password resets and service status checks by calling backend AWS Lambda functions.

Using the Red Hat Ansible Certified Content Collection for amazon.ai, you can automate the full agent lifecycle. The following playbook does the following:

  • Deploy or update the Bedrock agent with the proper foundation model and IAM role.
  • Configure the action group linked to operational Lambda functions.
  • Create an alias for integration with the chat.
  • Validate the agent's functionality through a test query.
  • Collect and log all configuration details for auditing and compliance.

Pro tip: Optionally upload a JSON audit report to Amazon S3 for long-term traceability and governance.

Outcome: After execution, you will have a fully deployed Bedrock IT Support agent with:

  • An active alias endpoint for user queries.
  • A Lambda-powered action group performing automated tasks.
  • Validation logs showing the agent's responses.
  • A structured audit report, optionally uploaded to S3 for compliance.
--- - name: Full Agent Lifecycle - Deploy, Validate, and Audit   hosts: localhost   gather_facts: false   vars:     agent_name: "ITSupportAssistant"     alias_name: "support-alias"     action_group_name: "SupportTasks"     foundation_model: "anthropic.claude-v2"     iam_role_arn: "arn:aws:iam::123456789012:role/BedrockAgentRole"     lambda_arn: "arn:aws:lambda:us-east-1:123456789012:function:ITSupportLambda"     upload_audit: true  # Set to true to enable S3 upload     audit_bucket: "it-support-audit-logs"     current_date: "{{ lookup('pipe', 'date +%Y-%m-%d') }}"   tasks:     - name: Create a Bedrock Agent       amazon.ai.bedrock_agent:         state: present         agent_name: "{{ agent_name }}"         foundation_model: "{{ foundation_model }}"         instruction: "You are an internal IT Support Assistant that helps employees with technical issues like password resets or server status checks."         agent_resource_role_arn: "{{ iam_role_arn }}"       register: agent_deploy          - name: Configure an Action Group       amazon.ai.bedrock_agent_action_group:         state: present         agent_name: "{{ agent_name }}"         action_group_name: "{{ action_group_name }}"         description: "Handles IT support automation tasks (password reset, system checks)."         lambda_arn: "{{ lambda_arn }}"         api_schema: "{{ lookup('file', 'files/api_schema.yml') }}"       register: action_group_deploy     - name: Create an Alias       amazon.ai.bedrock_agent_alias:         state: present         agent_name: "{{ agent_name }}"         alias_name: "{{ alias_name }}"         description: "Endpoint for the IT Support Assistant."       register: alias_deploy          - name: Validate agent with a test query       amazon.ai.bedrock_invoke_agent:         agent_id: "{{ agent_deploy.agent.agent_id }}"         agent_alias_id: "{{ alias_deploy.agent_alias.agent_alias_id }}"         input_text: "Can you reset my password for the dev portal?"         enable_trace: true       register: validation_test          - name: Retrieve agent configuration details       amazon.ai.bedrock_agent_info:         agent_name: "{{ agent_name }}"       register: agent_info     - name: Retrieve Action Group configuration       amazon.ai.bedrock_agent_action_group_info:         agent_name: "{{ agent_name }}"         action_group_name: "SupportTasks"       register: action_group_info     - name: List All Aliases for the Agent       amazon.ai.bedrock_agent_alias_info:         agent_name: "{{ agent_name }}"       register: aliases_list     - name: Render audit report from template       ansible.builtin.template:         src: "templates/audit_report.json.j2"         dest: "/tmp/audit_report.json"     - name: Optionally upload report to S3 for audit trail       amazon.aws.s3_object:         bucket: "{{ audit_bucket }}"         object: "reports/audit_{{ current_date) }}.json"         mode: put         src: "/tmp/audit_report.json"       when: upload_audit | default(false)         # Final console summary     - name: Final audit and validation summary       ansible.builtin.debug:         msg: |           === IT Support Assistant Deployment Summary ===           Agent: {{ agent_name }} ({{ agent_deploy.agent.agent_id | default('N/A') }})           Alias: {{ alias_name }} ({{ alias_deploy.agent_alias.agent_alias_id | default('N/A') }})           Model: {{ agent_info.agents.0.foundation_model | default('unknown') }}           Validation: {{ validation_test.response_text | truncate(100) }}           Total Aliases: {{ aliases_list.agent_aliases | length }}           Audit Uploaded: {{ upload_audit }}           =================================================

This workflow ensures the agent is ready for production, fully validated, and auditable. Operations teams can confidently deploy agents at scale with consistent results.

Use case 2: Personalized content generation

Suppose you want to dynamically generate personalized email content for customers based on recent behavior, preferences, or purchase history.

Pro tip: The generated content can optionally be stored in Amazon S3 for auditing or reuse, or sent to a ServiceNow ticket for review.

Outcome: After execution:

  • A Bedrock model is automatically selected and invoked.
  • A fully generated personalized message is returned and stored in the generated_message variable.
  • This message can be logged, reviewed, sent, or processed downstream for marketing or operational workflows.
--- - name: Personalized Content Generation   hosts: localhost   connection: local   gather_facts: false   vars:     prompt_text: "Generate a personalized marketing email for a customer who purchased a smartwatch last week."   tasks:     - name: List available text generation models       amazon.ai.bedrock_foundation_models_info:         by_output_modality: 'TEXT'       register: image_models     - name: Select an on-demand compatible image model       set_fact:         chosen_text_model: >-           {{ (image_models.foundation_models              | selectattr('inference_types_supported', 'defined')             | selectattr('inference_types_supported', 'contains', 'ON_DEMAND')             | map(attribute='model_id')             | first) }}     - name: Inspect the selected model       amazon.ai.bedrock_foundation_models_info:         model_id: "{{ chosen_text_model }}"       register: model_details          - name: Build payload for content generation       set_fact:         text_payload:           messages:             - role: "user"               content: "{{ prompt_text }}"           max_tokens: 500           temperature: 0.7           top_p: 0.9          - name: Generate personalized content       amazon.ai.bedrock_invoke_model:         model_id: "{{ chosen_text_model }}"         body: "{{ text_payload }}"         content_type: "application/json"         accept: "application/json"       register: model_response          - name: Extract generated message       set_fact:         generated_message: >-           {{ model_response.response.body.output_text              | default(model_response.response.body.completion)              | default(model_response.response.body.message)              | default('No output returned') }}

This workflow combines AWS Bedrock AI capabilities with Ansible automation, ensuring agility, compliance, and operational visibility in a single process.

Use case 3: Comprehensive DevOps Guru monitoring, diagnostics, and audit reporting

For example, a compliance mandate requires that all resources associated with the core WebBackend tag must be monitored by DevOps Guru. The operations team automate the following steps:

  1. Configure a resource collection for the WebBackend service to ensure all relevant resources are monitored.
  2. Notify a Simple Notification Service (SNS) topic of high-severity alerts for operational visibility.
  3. Retrieve a full diagnostic package (anomalies and recommendations) for recently closed insights for post-mortem reporting.

Pro tip: Optionally generate a structured audit report that can be:

  1. Uploaded to an S3 bucket for compliance and traceability.
  2. Attached to a ServiceNow or Jira ticket for operational follow-up or review.

Outcome: After execution:

  • All WebBackend resources are actively monitored.
  • Alerts are routed automatically to the designated SNS topic.
  • A detailed diagnostic and audit report is generated for compliance and post-mortem analysis.
--- - name: DevOps Guru Monitoring, Configuration, and Diagnostics   hosts: localhost   connection: local   gather_facts: false   vars:     ops_sns_arn: "arn:aws:sns:us-east-1:123456789012:OpsAlertsTopic"   tasks:     - name: Configure Resource Collection to Monitor WebBackend Service       amazon.ai.devopsguru_resource_collection:         state: present         tags:           - app_boundary_key: "Devops-guru-Service"             tag_values: ["WebBackend"]         notification_channel_config:           sns:             topic_arn: "{{ ops_sns_arn }}"           filters:             severities: ["HIGH"]             message_types: ["NEW_INSIGHT", "SEVERITY_UPGRADED"]       register: config_result     - name: Audit - Check the Configured Resource Collection Details                 amazon.ai.devopsguru_resource_collection_info:         resource_collection_type: "AWS_TAGS"       register: collection_audit     - name: Diagnostics - List Detailed Info for Insights       amazon.ai.devopsguru_insight_info:           status_filter:             closed:               type: 'REACTIVE'               end_time_range:                 from_time: "2025-10-20"                 to_time: "2025-10-22"           include_recommendations:             locale: EN_US           include_anomalies:             filters:               service_collection:                 service_names:                   - EC2         register: insight_details          - name: Build Audit Report       set_fact:         audit_report:           timestamp: "{{ lookup('pipe', 'date +%Y-%m-%dT%H:%M:%S') }}"           resource_collection_status: "{{ config_result.msg }}"           monitored_tags: "{{ collection_audit.resource_collection.tags | default('None') }}"           insight_count: "{{ insight_details.reactive_insights | length }}"           insights: "{{ insight_details.reactive_insights | default([]) }}"     - name: Render DevOps Guru audit report from template       ansible.builtin.template:         src: "templates/devopsguru_audit_report.json.j2"         dest: "/tmp/devopsguru_report.json"

This workflow demonstrates a full AI-to-ops compliance and monitoring lifecycle, combining AWS DevOps Guru, Ansible automation, and optional audit/reporting integration.

Final thoughts

The launch of the Red Hat Ansible Certified Content Collection amazon.ai for generative AI is more than just the addition of new modules; it's an important step in bridging the gap between AI innovation and enterprise operations. Whether you're scaling foundation models, orchestrating intelligent agents, or monitoring complex systems with DevOps Guru, this collection lets you treat AI as code. That means deployments are repeatable, configuration drift is minimized, and auditability is built in from day one.

Explore the full use case playbooks in the GitHub Repository. Migrating your configurations to these automated workflows is the first step toward building a fully automated AI ecosystem.

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