AWS Transform now supports agentic modernization of custom code

Does AI-generated code add to, or reduce, technical debt? Amazon Web Services is aiming to reduce it with the addition of new capabilities to AWS Transform, its AI-driven service for modernizing legacy code, applications, and infrastructure.

“Modernization is no longer optional for enterprises these days,” said Akshat Tyagi, associate practice leader at HFS Research. They need cleaner code and updated SDKs to run AI workloads, tighten security, and meet new regulations, he said, but their inability to modernize custom code quickly and with little manual effort is one of the major drivers of technical debt.

AWS Transform was introduced in May to accelerate the modernization of VMware systems and  Windows.Net and mainframe applications using agentic AI. Now, at AWS re:Invent, it’s getting some additional capabilities in those areas — and new custom code modernization features besides.

New mainframe modernization agents add functions including activity analysis to help decide whether to modernize or retire code; blueprints to identify the business functions and flows hidden in legacy code; and automated test plan generation.

AWS Transform for VMware gains new functionality including an on-premises discovery tool; support for configuration migration of network security tools from Cisco ACI, Fortigate, and Palo Alto Networks; and a migration planning agent that draws business context from unstructured documents, files, chats and business rules.

The company is also inviting partners to integrate their proprietary migration tools and agents with its platform through a new AWS Transform composability initiative. Accenture, Capgemini, and Pegasystems are the first on board.

Customized modernization for custom code

On top of that, there’s a whole new agent, AWS Transform custom, designed to reduce the manual effort involved in custom code modernization by learning a custom pattern and operationalizing it throughout the target codebase or SDK. In order to feed the agent the unique pattern, enterprise teams can use natural-language instructions, internal documentation, or example code snippets that illustrate how specific upgrades should be performed.

AWS Transform custom then applies these patterns consistently across large, multi-repository codebases, automatically identifying similar structures and making the required changes at scale; developers can then review and fine-tune the output, which the agent adapts and operationalizes, allowing it to continually refine its accuracy, the company said.

Generic is no longer good enough

Tyagi said that the custom code modernization approach taken by AWS is better than most generic modernization tools, which rely solely on pre-packaged rules for modernization.

“Generic modernization tools no longer cut it. Every day we come across enterprises complaining that the legacy systems are now so intertwined that pre-built transformation rules are now bound to fail,” he said.

Pareekh Jain, principal analyst at Pareekh Consulting, said Transform custom’s ability to support custom SDK modernization will also act as a value driver for many enterprises.

“SDK mismatch is a major but often hidden source of tech debt. Large enterprises run hundreds of microservices on mismatched SDK versions, creating security, compliance, and stability risks,” Jain said.

“Even small SDK changes can break pipelines, permissions, or runtime behavior, and keeping everything updated is one of the most time-consuming engineering tasks,” he said.

Similarly, enterprises will find support for modernization of custom infrastructure-as-code (IaC) particularly valuable, Tyagi said, because it tends to fall out of date quickly as cloud services and security rules evolve.

Large organizations, the analyst noted, often delay touching IaC until something breaks, since these files are scattered across teams and full of outdated patterns, making it difficult and error-prone to clean up manually.

For many enterprises, 20–40% of modernization work is actually refactoring IaC, Jain said.

Not a magic button

However, enterprises shouldn’t see AWS Transform’s new capabilities as a magic button to solve their custom code modernization issues.

Its reliability will depend on codebase consistency, the quality of examples, and the complexity of underlying frameworks, said Jain.

But, said Tyagi, real-world code is rarely consistent.

“Each individual writes it with their own methods and perceptions or habits. So the tool might get some parts right and struggle with others. That’s why you still need developers to review the changes, and this is where human intervention becomes significant,” Tyagi said.

There is also upfront work, Jain said: Senior engineers must craft examples and review output to ground the code modernization agent and reduce hallucinations.

The new features are now available and can be accessed via AWS Transform’s conversational interface on the web and the command line interface (CLI).