One year ago, AWS introduced a specialized suite of tools designed to address the complex challenge of legacy modernization. The service, known as AWS Transform, was initially purpose-built for .NET, Mainframe, and VMware workloads. It represents a significant shift in how organizations approach enterprise application modernization, moving away from manual refactoring toward automated, AI-driven strategies. At re:Invent 2025, the platform evolved further with the introduction of AWS Transform custom, allowing organizations to manage and transform code at scale using both AWS-managed and custom transformations. For engineers preparing for the AWS certifications, understanding these architectural shifts is crucial for designing scalable migration strategies.
Evolution of Transformation Capabilities
In the first twelve months of operation, the service demonstrated substantial impact across the cloud ecosystem. Thousands of customers successfully migrated hundreds of thousands of servers, saving over 1.6 million hours of manual labor. Furthermore, the system processed more than 4.5 billion lines of code. The core functionality allows engineers to upgrade language versions, migrate legacy frameworks, and optimize performance metrics. These transformations are available as ready-to-use modules or can be customized to meet specific organizational requirements. This flexibility is particularly relevant for professionals studying for the AWS ML Specialty certification, as it demonstrates practical application of machine learning in DevOps pipelines.
Windows Modernization and Mainframe Reimagine
Recent updates have expanded the scope of the platform to include full-stack Windows modernization capabilities. Additionally, the AWS Transform suite now includes Reimagine capabilities specifically for mainframe environments. These features automate testing functionality, ensuring that legacy systems can be validated before migration. For DevOps professionals, this means reduced risk during the transition from on-premises infrastructure to cloud-native environments. The ability to analyze code bases using these transformations provides deep insights into technical debt, helping teams prioritize refactoring efforts effectively.
Integration with AI Agent Ecosystems
Celebrating its one-year anniversary, the AWS Transform agents are now available within major AI development environments, including Kiro, Claude, Cursor, and Codex. This integration allows developers to interact with transformation agents directly within their preferred coding interfaces. The agent builder toolkit, known as Kiro power, enables the creation of customized transformation agents tailored to specific project needs. This level of integration is essential for AI engineers looking to implement autonomous coding assistants in their workflows. It bridges the gap between high-level AI models and the gritty details of legacy code maintenance.
What This Means For You
For cloud engineers and architects, the maturity of AWS Transform signals a new era in enterprise modernization. The shift from manual intervention to automated, AI-driven processes reduces the operational overhead associated with maintaining legacy systems. Organizations can now focus on innovation rather than upkeep. As you plan your next migration project, consider leveraging these tools to accelerate your roadmap. Whether you are preparing for the AWS DevOps Pro exam or managing a real-world transformation, these capabilities offer a robust foundation for success. The data shows that early adopters have already achieved significant efficiency gains, validating the strategic value of investing in these modernization tools.