awscdk cfnPractitioner

Scaling Application Modernization with Strands and AWS Transform

5 min read AWS DevOps BlogMay 11, 2026Reviewed for accuracy

Practitioner — Hands-on experience recommended

In today's fast-paced tech landscape, application modernization is not just beneficial; it's essential. Legacy systems can hinder agility and innovation, making it crucial to upgrade runtimes, SDKs, and frameworks efficiently. Strands and AWS Transform custom provide a robust solution for automating these transformations at scale, allowing teams to focus on delivering value rather than getting bogged down in manual upgrades.

The architecture separates intelligent decision-making from deterministic execution, enabling you to automate workflows while maintaining oversight. You interact with the system via a React-based frontend or API, submitting repositories or batch workloads through CSV inputs. The orchestrator agent on Amazon Bedrock AgentCore coordinates specialized agents to analyze codebases, identify transformation needs, and manage execution workflows. If a transformation isn't available, a creation agent can dynamically generate one, enhancing the system's capabilities over time. Once identified, transformations are executed at scale using AWS Batch jobs, leveraging the AWS Transform custom CLI for parallel processing.

To implement this solution effectively, ensure you have the prerequisites in place, including the AWS CLI and AWS SAM CLI. Be aware of the version requirements, such as Docker v20.x+ and Node.js v18.x+. These details can significantly impact your deployment success. The system's flexibility and automation capabilities can greatly enhance your modernization efforts, but understanding the underlying architecture and configuration is key to leveraging its full potential.

Key takeaways

→Leverage Strands Agents to build multi-agent systems for complex transformation workflows.
→Utilize AWS Transform custom for reusable, CLI-driven code transformations across large portfolios.
→Submit repositories or batch workloads via CSV inputs for efficient processing.
→Deploy using AWS Batch jobs to execute transformations at scale.
→Ensure all prerequisites, including AWS CLI and Docker, are met before deployment.

Why it matters

This approach can drastically reduce the time and effort required for application modernization, enabling teams to respond faster to market demands and improve overall software quality.

Code examples

Bash

git clone https://github.com/aws-samples/aws-transform-custom-samples.git
cd aws-transform-custom-samples/agentic-atx-platform

Bash

# Configure AWS CLI
aws configure
# Verify credentials
aws sts get-caller-identity

Bash

cdk deploy AtxContainerStack AtxInfrastructureStack AtxUiStack --require-approval never -c existingVpcId=vpc-xxx -c existingSubnetIds=subnet-aaa,subnet-bbb -c existingSecurityGroupId=sg-ccc