AWS DevOps & Developer Productivity Blog
Category: Generative AI
AWS Transform custom: Enterprise Code Modernization with the Learn-Scale-Improve Flywheel
Enterprise modernization has reached an inflection point. You can transform one repository easily. Existing tools, including AWS Transform custom, work well for individual repositories, and the process is understood. But what about 50 repositories? 100? 200? When you need to modernize at enterprise scale, transforming code is only part of the challenge. Coordinating people, capturing […]
Troubleshooting environment with AI analysis in AWS Elastic Beanstalk
Introduction AWS Elastic Beanstalk simplifies the process of deploying and scaling web applications. You upload your code, and Elastic Beanstalk handles capacity provisioning, load balancing, auto scaling, and application health monitoring. Elastic Beanstalk now offers AI Analysis to help troubleshoot environment health issues. When you request an analysis, Elastic Beanstalk triggers a script on the […]
Automate AWS Lambda Runtime Upgrades with AWS Transform custom
Introduction Organizations carry a growing burden of technical debt — aging codebases, outdated runtimes, and legacy frameworks that slow innovation, increase security risk, and inflate maintenance costs. Addressing this debt requires tackling a wide range of code transformation challenges: version upgrades, runtime migrations, framework transitions, and language translations, all of which must be repeated across […]
Building a scalable code modernization solution with AWS Transform custom
Introduction Software maintenance and modernization is a critical challenge for enterprises managing hundreds or thousands of repositories. Whether upgrading Java versions, migrating to new AWS SDKs, or modernizing frameworks, the scale of transformation work can be overwhelming. AWS Transform custom uses agentic AI to perform large-scale modernization of software, code, libraries, and frameworks to reduce […]
AWS Transform custom: AI-driven Java modernization to reduce tech debt
In today’s rapidly evolving software landscape, maintaining and modernizing Java applications is a critical challenge for many organizations. As new Java versions are released and best practices evolve, the need for efficient code transformation becomes increasingly important. Organizations today face significant challenges when modernizing their Java applications. Legacy codebases often contain outdated patterns, deprecated APIs, […]
From AI agent prototype to product: Lessons from building AWS DevOps Agent
At re:Invent 2025, Matt Garman announced AWS DevOps Agent, a frontier agent that resolves and proactively prevents incidents, continuously improving reliability and performance. As a member of the DevOps Agent team, we’ve focused heavily on making sure that the “incident response” capability of the DevOps Agent generates useful findings and observations. In particular, we’ve been […]
Automating AWS SDK for Java v1 to v2 Upgrades with AWS Transform
The AWS SDK for Java v2 represents a fundamental shift in how Java applications interact with AWS services, addressing critical security requirements while delivering measurable performance improvements. For organizations still operating on v1, this transition extends beyond a routine version upgrade—it’s a strategic imperative for maintaining secure, efficient cloud operations. With v1 reaching end-of-support on December 31, 2025, […]
Introducing AWS Cloud Control API MCP Server: Natural Language Infrastructure Management on AWS
Today, we’re officially announcing the AWS Cloud Control API (CCAPI) MCP Server. This MCP server transforms AWS infrastructure management by allowing developers to create, read, update, delete, and list resources using natural language. As part of the awslabs/mcp project, this new and innovative tool serves as a bridge between natural language commands and AWS infrastructure […]
Flexibility to Framework: Building MCP Servers with Controlled Tool Orchestration
MCP (Model Context Protocol) is a protocol designed to standardize interactions with Generative AI models, making it easier to build and manage AI applications. It provides a consistent way to communicate context with different types of models, regardless of where they’re hosted or how they’re implemented. The protocol helps bridge the gap between model deployment […]
AI-Driven Development Life Cycle: Reimagining Software Engineering
Business and technology leaders are constantly striving to improve productivity, increase velocity, foster experimentation, reduce time-to-market (TTM), and enhance the developer experience. These North Star goals drive innovation in software development practices. This innovation is increasingly being powered by artificial intelligence. Particularly, generative AI powered tools such as Amazon Q Developer and Kiro have already […]








