AWS Transform Custom Automates Lambda Runtime Upgrades at Scale

Managing AWS Lambda functions often involves a recurring challenge: keeping their runtimes up-to-date. As Lambda runtimes follow a published deprecation schedule, periodic upgrades are essential to avoid security vulnerabilities, maintain AWS Support, and ensure compliance. While this is manageable for a small number of functions, organizations operating hundreds or thousands of Lambda functions face a substantial engineering effort that can divert valuable resources from core feature development. To mitigate this, AWS has introduced Transform custom, an innovative Agentic AI service specifically engineered for code modernization. This service is designed to automate and simplify the process of upgrading Lambda function runtimes at scale. AWS Transform custom provides comprehensive support across all stages of a runtime upgrade. It begins by surfacing potential risks associated with outdated runtimes, then helps confirm the adequacy of existing test coverage. Crucially, it performs the necessary code transformations and subsequently validates these changes, ensuring a smooth and reliable upgrade process. The tool offers significant flexibility, allowing users to leverage AWS-provided transformations for common upgrade scenarios or to create their own custom transformations. This adaptability is vital for organizations with unique compliance requirements or specific compatibility needs. It can handle various aspects of the upgrade, such as refactoring Node.js callback-based handlers to `async/await` and updating configuration files to reflect new runtime versions like `nodejs22.x` to `nodejs24.x`. For platform teams, AWS Transform custom facilitates the orchestration of large-scale upgrade campaigns through a dedicated web application. Teams can define specific upgrade goals, such as migrating all Node.js 22 functions to Node.js 24, and track progress centrally. The actual transformations can be executed using the AWS Transform custom CLI or Kiro Power, supporting both interactive and non-interactive batch modes for efficiency. Ultimately, the service aims to streamline runtime upgrades, freeing up engineering capacity and enabling organizations to stay ahead of deprecation challenges. By automating these updates, teams can more readily adopt newer runtime features, benefit from performance enhancements, and reduce the overall technical debt associated with maintaining serverless applications. Charges for the service are based solely on active agent work during server-side operations, not on user idle time or client-side processing.