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Overview

This lab demonstrates how Dynatrace and Red Hat Ansible Automation Platform (AAP) work together to deploy, observe, automate, and remediate a containerized AI application.

What You Will Build

  • A containerized easyTravel AI Travel Advisor stack on Podman.
  • Automated deployment and configuration workflows with AAP Controller and Event-Driven Ansible (EDA).
  • Dynatrace AI observability and automation integrations for runtime analysis and operational response.

Workshop Architecture

Instructor / Operator
        |
        v
AAP Controller + EDA  ----->  Dynatrace Tenant (Apps, Settings, OneAgent, EdgeConnect)
        |
        v
RHEL 9 host with Podman  ----->  easyTravel AI Travel Advisor + supporting services

Core Capabilities Covered

  • Infrastructure and platform provisioning with repeatable Ansible playbooks.
  • Deployment of Dynatrace apps, APIs, OneAgent, and EdgeConnect.
  • Deployment of a multi-service AI workload with Ansible-controlled Podman workflows.
  • Closed-loop remediation where Dynatrace events trigger EDA automation.

AI Observability and Automation Use Cases

  • Observe prompt-to-response behavior in AI workloads.
  • Track model-related behavior changes and drift signals.
  • Trigger automated corrections when platform health degrades.
  • Compare behavior before and after model, prompt, or retrieval configuration changes.

Why Dynatrace + Red Hat AAP Together

  • Dynatrace provides deep runtime context and high-fidelity problem detection.
  • AAP turns intent into consistent, repeatable automation across deployment and operations.
  • EDA connects detections to actions, reducing mean time to remediation.
  • The combined platform supports reliable AI operations with less manual intervention.

Lab Outcomes

At the end of this lab, you will have implemented an end-to-end flow from deployment to autonomous remediation for an AI-enabled application.

Continue to Prerequisites.