AWS targets IT modernization gains with new agentic AI features in Transform

Amazon Web Services (AWS) has announced a raft of new agentic AI features for its Transform IT modernization service.

Unveiled earlier this year, Transform aims to speed up processes for IT teams overhauling Windows .NET applications, mainframes, and VMware systems.

As part of the update, AWS Transform ‘custom’ capabilities look to accelerate “organization-wide code and application modernization”, the company noted in a statement.

This will enable teams to build custom, specialized AI agents to automate code and application transformation projects. These agents come pre-built for specific programming languages such as Python, Java, or Node.js, and can be tweaked based on company-specific languages.

Speaking to assembled media ahead of the AWS re:Invent conference in Las Vegas, Asa Kalavade, AWS’ VP of migration and modernization, said the new capabilities will help streamline modernization projects, which have traditionally been a time-consuming and expensive process.

“With custom you can create and execute modernization of all your custom code and applications,” she said.

“It’s very easy to use. All you do is define what you want your source [code] to be modernized into, you give some code snippets, you give documentation, you can even point it to a wiki and AWS Transform then comes up with the definition for modernizing this code,” Kalavade added.

AWS customers have unlocked significant gains from the use of the new Transform agentic features so far, according to Kalavade. Air Canada, for example, used the agentic AI features during a huge IT modernization project.

Using AWS Transform, the airline was able to modernize thousands of Lambda functions, recording an 80% reduction in “expected time and cost” for the project compared to a manual migration.

All told, using agents, enterprises can achieve transformation acceleration gains up to five-times faster compared to when conducted manually.

These agents also automatically capture feedback, meaning they can be continually tweaked and improved over time to further drive efficiency.

Mainframe modernization

Elsewhere, the company also announced new agentic AI features aimed at driving mainframe modernization efficiency gains.

This includes three new agents for automating code analysis, business rule extraction, and technical documentation analysis. Among these custom-built agents is one aimed at accelerating legacy code modernization, for example.

A new task agent also looks to “speed up test planning and validation” by automatically generating test plans and data collection scripts, processes which typically take up to half of project timelines, according to the hyperscaler.

Since the advent of generative AI in late 2022, mainframe modernization efforts have taken center stage for enterprises globally. A recent study from Kyndryl, for example, found a third of enterprises believe mainframes have become a critical foundation for running AI workloads.

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