AI Agents Empower Mainframe Operations
Key Points
- Combining AI agents with mainframe computing extends simple “Call Home” alerts into proactive, intelligent hardware and workload management.
- Unlike narrow ML models or static LLMs, AI agents can perceive inputs, make informed decisions, and act—such as rebalancing loads or generating actionable reports.
- An agent’s “memory” is split into context (the business goal like minimizing downtime or optimizing CPU usage) and knowledge (structured and unstructured data from sources like Call Home and SMF records).
- By integrating this context and knowledge, agents can invoke appropriate tools to execute complex, context‑aware actions across the enterprise ecosystem.
Sections
- AI Agents for Mainframe Proactive Management - The speaker outlines how integrating AI agents with mainframe “Call Home” systems can transform simple hardware threshold alerts into intelligent, decision‑making tools that predict issues and automate preventative maintenance.
- AI Enhancing Mainframe Operations - The speaker urges moving beyond generic AI productivity and fraud‑detection applications toward using AI to automate and simplify day‑to‑day mainframe administration tasks, making SREs’ work more enjoyable.
Full Transcript
# AI Agents Empower Mainframe Operations **Source:** [https://www.youtube.com/watch?v=IRa6XmG8QCQ](https://www.youtube.com/watch?v=IRa6XmG8QCQ) **Duration:** 00:06:43 ## Summary - Combining AI agents with mainframe computing extends simple “Call Home” alerts into proactive, intelligent hardware and workload management. - Unlike narrow ML models or static LLMs, AI agents can perceive inputs, make informed decisions, and act—such as rebalancing loads or generating actionable reports. - An agent’s “memory” is split into context (the business goal like minimizing downtime or optimizing CPU usage) and knowledge (structured and unstructured data from sources like Call Home and SMF records). - By integrating this context and knowledge, agents can invoke appropriate tools to execute complex, context‑aware actions across the enterprise ecosystem. ## Sections - [00:00:00](https://www.youtube.com/watch?v=IRa6XmG8QCQ&t=0s) **AI Agents for Mainframe Proactive Management** - The speaker outlines how integrating AI agents with mainframe “Call Home” systems can transform simple hardware threshold alerts into intelligent, decision‑making tools that predict issues and automate preventative maintenance. - [00:05:56](https://www.youtube.com/watch?v=IRa6XmG8QCQ&t=356s) **AI Enhancing Mainframe Operations** - The speaker urges moving beyond generic AI productivity and fraud‑detection applications toward using AI to automate and simplify day‑to‑day mainframe administration tasks, making SREs’ work more enjoyable. ## Full Transcript
What happens when you combine technology's foundation with its frontier? We're going to look
at how to bring AI agents to mainframe computing. Now, let's look at this
background. We have enterprise systems and the way they're set up, your technology is broken up
into various sysplexes, various environments where you're running your applications, you're running
your business. And in this system, there's a facility for Call Home. And so, Call
Home is going to send events like one where hardware is running hot, or there's an
upcoming plausible problem with a part of your system. And so, the part can get
ready, it can be set up, and you could be notified to say this upcoming problem can be
avoided if we schedule this maintenance ahead of time. This ability to really deal with the
hardware on a proactive basis is a criti ... critical part of
enterprise computing. But this is simple. These are simple events. This is a hardware threshold,
a thermometer threshold, a simple environment. Imagine, let's think what can
we do? Bring in AI agents and we get a whole lot more information. So, what is an AI
agent? Well, the difference between AI agents and previously, LLMs, or even before that,
traditional ML models, is that AI agents can perceive inputs, make informed decisions and
then they act or they generate. And the way that looks against traditional models, like some of
what you were mentioning earlier, would be uh, previously, they were narrow purpose. You know, they
raise a flag or they make a simple prediction, but they can't really do anything about it or handle
the complexity or the context of the business using all these mainframes in their ecosystem. So,
what an agent would do is take some kind of action, and that
could look something like uh, rebalancing loads across different systems, or generating
reports that would enable a sysadmin to look through and take the right action, essentially
a recommendation. And that takes into account multiple types of data. So we're going to look
in ... look at what goes into an agent that enables it to take this action. So the first thing that
we're going to look at, we're going to call it memory. But we're going to break it down into two
subparts. We're going to call it context and knowledge.
So context in ... in this case is going to be the business need for this agent. What exactly is it
trying to optimize? Are we talking about minimizing downtime? Are we talking about
preventing errors or, you know, managing CPU usage as ... as an average? We don't want it to think unidimensionally,
so we want to set a persistent context for the system that enables it to then go
to this next source of information, which is the knowledge or the data that it can get from
systems like Call Home or SMF records. going to look through both unstructured and structured
data, and arrive at an action that is governed by the tools that it has access
to. So tools could be another model that is running on the structured
data and producing a summary or an aggregation based on which it can decide what to do next. So
there are other components in here. For ... for instance, there is the summarization model which could be
its own agent, or there could be a problem identification subpart which could be its own
agent, followed by an action or a recommendation or a remedy component. So that's how all three of
these components feed into the agent's ability to act on our problem. Now, if we take this
environment and we take that first scenario that I talked about, we have very complex environments
with multiple sysplex running in the environment. Each one of these is managed
on its own. You see the performance of each one, you manage the workload of each one. And so we
manage each one independently. But when we think about the entire environment we're
running, we really need to look across all of these. So if we can take this agent technology
and apply it across all of the systems, we can get the information from all of them and make better
decisions across them all. Today, what people do, yeah, they shut off dev test. They just turn it
off and say, sorry, you're out of luck. Well, maybe we don't have to do that. Maybe if we have the
context of what's going on, the current environment, we can make better decisions
with the agent technology to actually maybe not turn it off entirely but just reduce it
appropriately so that we can have a better en ... overall context. And, more
importantly, if we're doing this with agentic AI and it's able to take all of this
information and perform actions, then my system programmers don't have to spend all that
time looking at all this data, processing all this data themselves, going through all
that RMF data and understanding how it's performing. What they can spend the time doing
more fun things like building up new systems or experimenting with new opportunities.
And so really and truly, instead of just focusing on AI as a
general productivity improvement or the most common use case, which is fraud detection
mainframes, which is absolutely wonderful use case But let's take it a step further. Let's make it ...
use in our internal systems, on our mainframes to make our lives better and
make the system programmer or system administrator's life or SRE, whichever way you want
to call it, a lot more fun and remove those manual things that they're having to do today. Bringing
this AI technology into the mainframe really can help make all of our lives
better.