Amazon Web Services (AWS) has made generally available a service that makes use of artificial intelligence (AI) agents to migrate and modernize infrastructure, applications and code.
Sriram Devanathan, general manager for low-code/no-code AppBuilder service at AWS, said the AWS Transform service provides access to a natural language chat interface that makes it simpler to either refactor, for example, VMware, mainframe and .NET workloads or upgrade older instances of Java.
Initially previewed as an extension to Amazon Q Developer, AWS has a series of AI agents that have been specifically trained to both understand how code is constructed and invoke various tools, such as application assessment tools from CAST Services, to transform it. DevOps teams can use AWS Transform to specify objectives, share project context, assess business plans and cost savings, review and adapt transformation plans, identify dependencies, approve code infrastructure suggestions and execute tests.
For example, an AWS Transform agent for .NET can migrate a Windows-based application to Linux up to four times faster than traditional methods to reduce Microsoft cost by as much as 40%, noted Devanathan.
Similarly, an AWS Transform agent for VMware can automate the conversion of on-premises VMware network configurations to AWS equivalents up to 80 times faster than manual approaches.
An AWS Transform agent for mainframe can also decompose monolithic z/OS COBOL applications into components that can be run in the cloud in minutes. AWS Transform agents can, for example, invoke a Graph Neural Networks capability that makes it simpler to refactor applications written in COBOL, JCL (Job Control Language) and those relying on Customer Information Control System (CICS) transaction manager, BMS (Basic Mapping Support) screens, DB2 databases, and VSAM (Virtual Storage Access Method) data files in a set of Java-based microservices that can be deployed on an open source Postgres database.
In many cases, the documentation generated after that migration is more detailed than what was created when those legacy mainframe applications were initially built and deployed, said Devanathan.
Most organizations will still require specialists to migrate and modernize applications, but the total amount of time and effort required will be substantially reduced, said Devanathan. Most IT teams would be well-advised to revisit their modernization strategy by picking a relatively small project to better understand the level of refactoring that can now be achieved using AI agents, he added.
Longer term, AWS is looking to expand the range of modernization tools that its AI agents are trained to invoke, he added.
During uncertain economic times, the amount of pressure being applied to reduce the total cost of IT always rises. As such, interest in migrating legacy applications always tends to rise in an economic downturn. However, the amount of effort historically required to modernize applications has been substantial. In fact, many of these projects have never been completed simply because the cost of migrating applications proved to be much higher than initially anticipated.
Regardless of motivation, however, the total cost of continuing to run legacy applications can, in many cases, be even more prohibitive than the cost of a modernization initiative that, with the help of AI agents, may now prove to be a more viable option.
