"Modernizing applications by upgrading language runtimes, migrating SDKs, and refactoring frameworks is important for cloud adoption but can be labor-intensive at scale. Each repository requires analysis of dependencies and transformation needs; custom transformation logic must be built and validated, and changes are often executed sequentially across codebases. If you have hundreds of applications, this stretches timelines from months to years, while introducing inconsistency across your teams."
"AWS Transform custom enables reusable, CLI-driven code transformations for upgrading runtimes, SDKs, and frameworks consistently across large portfolios. Strands Agents provides a framework for building multi-agent systems that coordinate complex transformation workflows. Amazon Bedrock AgentCore delivers the managed runtime, memory, and observability to operate these agents reliably in production. Together, they replace manual, sequential modernization with an intelligent, automated approach that scales."
"The solution uses an agentic architecture that separates intelligent decision-making from deterministic execution, enabling automation at scale while maintaining consistency and control. You will build an AI-driven application modernization system that demonstrates how multi-agent workflows can be applied to large-scale code transformation scenarios. You interact with the system through a React-based frontend or API interface, submitting individual repositories or batch workloads via CSV inputs. Requests are processed asynchronously through an API layer that invokes an orch"
Modernizing applications for cloud adoption requires upgrading language runtimes, migrating SDKs, and refactoring frameworks, but doing so across many repositories is labor-intensive and often sequential. AWS provides reusable, CLI-driven transformation capabilities for consistent upgrades across large portfolios. A multi-agent framework coordinates complex transformation workflows, while a managed agent runtime provides memory and observability for reliable production operation. The proposed system combines these components into a generative AI-powered modernization workflow that analyzes repositories, determines required changes, creates missing transformations, and executes transformations in parallel. The architecture separates intelligent decision-making from deterministic execution, enabling automation at scale while maintaining consistency and control through a frontend or API interface and asynchronous processing.
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