This 2-page brief presents Agentic Data Engineering as an ADAM foundational solution that modernizes legacy ETL migrations, especially IBM DataStage, by replacing rigid, manual, rule-based approaches with an intelligent, adaptive, automation-first workflow. It outlines a series of AI agents that parse DataStage jobs, abstract transformation logic, convert it into Microsoft Fabric-aligned JSON schemas, map components to Fabric pipeline activities, and then integrate outputs with Git-based source control plus CI/CD in Azure DevOps or GitHub Actions for automated testing and deployment. A sample scenario shows end-to-end automation from Oracle extraction through filtering, aggregation, and loading into Azure SQL, with reduced manual effort, fewer errors, faster migrations, and a self-learning

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