BMC Software this week at its Connect 2024 conference unveiled a generative artificial intelligence (AI) assistant that makes it simpler to invoke a range of AI capabilities in mainframe environments.

Based on the Automated Mainframe Intelligence (AMI) framework that BMC has created to make it simpler to automate a range of tasks, a BMC AMI Assistant now makes it possible to, for example, involve BMC AMI DevX Code Insights, a tool that BMC created to explain how code running on the mainframe has been constructed.

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In addition, BMC AMI Assistant will be integrated with BMC AMI Ops Insights, a tool available in beta that leverages large language models (LLMs) to surface ways to optimize mainframe application environments.

Priya Doty, BMC vice president of industry solutions marketing for BMC AMI, said BMC AMI Assistant will, for example, make it simpler for organizations to capture tribal knowledge about mainframe applications at a time when many of the IT professionals with mainframe expertise are retiring. As BMC continues to develop additional AI agents, mainframes will become more accessible to the next generation of IT professionals, she noted.

Steven Dickens, a chief technology advisor for The Futurum Group, said that while much of the code written for the mainframe is decades old and is often not especially well documented, the platform still drives mission-critical applications. AI will significantly reduce the time and effort required to modernize those applications, he added.

Based on large language models (LLMs) that BMC is training, BMC AMI DevX Code Insights is one of several AI agents that BMC plans to make available via a single console. Those LLMs will either be developed by BMC or based on third-party platforms. Alternatively, BMC also plans to enable organizations to invoke any LLMs they might decide to build themselves.

BMC earlier this year invited organizations to participate in a Design Program through which the company will provide access to generative AI features as they are developed. Ultimately, the goal is to make the mainframe just another type of distributed computing platform that requires much less specialized skills to manage.

It’s not clear to what degree AI will change how IT teams are constructed but there is little doubt the roles and responsibilities will change. As that occurs, it should become simpler for cross-functional teams to build and deploy applications across a distributed computing environment that includes mainframes.

In the meantime, the number of workloads being deployed on mainframes continues to increase as more Java and Python applications are built and deployed on the platform. Python, especially, is expected to drive a wave of workloads that will enable AI to be applied in real-time to the transactions that mainframes continue to process at levels of scale other platforms still can’t match. The challenge now is finding a way to make it simpler to manage a diverse range of workloads running on mainframes that more than six decades after their initial launch continue to play a crucial role in the enterprise.