What is Generative AI and is it the Way to AIOps?

One of the challenges in infrastructure is configuring the myriad devices, services, and systems needed to deliver and secure even a single application. Organizations rely on an average of 23 different app services—if you exclude “as a service” offerings. 

Now, I don’t have to tell you that configuring a web app and API protection service is different than configuring a plain old load balancing service. What that means is that the folks responsible for configuring and operating app services may need to be experts in a dozen different “languages.” 

The industry has been trying to address that for years. When APIs became the primary means of configuring everything, app delivery and security services were no exception. Everyone started with imperative APIs, which simply changed how you issued commands. Instead of typing commands on a CLI you sent API commands via HTTP. 

Fairly soon it became clear that the API tax incurred by relying on imperative APIs was too high, and the industry shifted to declarative APIs. But unfortunately, most of the industry decided declarative meant “configuration as JSON.” So instead of the intent (that word is important, remember it) behind declarative, which is “tell me what you want to do, and I’ll do it for you,” we ended up with “here’s the configuration I want, go do the hard work of doing it.” 

It's not quite the same, and it still needed the same level of expertise with the operating model peculiar to a given solution. I’m not sure the industry ever reached agreement on whether load balancers used “pools” or “farms,” let alone the more complex details of how virtual servers interact with real servers and application instances. So, all the industry did with declarative was offload the command-level work from operators to the system.

Now, what Generative AI brings to the table is a form of low code/no code. These are more reliable than some results because they’re based on well-formed specifications that guide the generation of results. There are only so many ways you can write “hello world” after all, while there are millions of ways to answer a question.

Which means I should be able to tell a trained model, “Hey, I want to configure my load balancer to scale App A” and the system should be able to spit out a configuration. But more than that, I should be able to tell it, “Give me a script to do X on system Y using Z” and BAM! Not only should it generate the configuration, but the automation necessary to deploy it to the right system. 

Oh look. It already does.

But this is the easy stuff. I should further be able to tell it, “Oh, by the way, deploy it.” And the thing should do it while I’m enjoying my morning coffee. And perhaps sing me a little song, too. 

But wait, there’s more! What if I can also tell a Generative AI system later, “Hey, users in Green Bay are logging in a lot and performance is down, clone App A and move it to our site in Milwaukee.” 

And it does. Because under the hood, all of this is just a web of APIs, configurations, and commands that can and are often automated by scripts today. Those scripts are often parameterized, which loosely correlates to what are the parameters in my AI prompt: Green Bay, Milwaukee, App A. So what changes is the generator, and the speed with which it can be generated. 

I often say that AI and automation are force multipliers. Because technology doesn't know what it needs to do, we do. But AI and automation can do it much faster and efficiently, effectively amplifying productivity, increasing time to value, and freeing up experts’ time to focus on strategic decisions and projects. And over time, the AI can learn from us, further multiplying our capacity and exposing new possibilities. 

This is no longer science fiction but computer science reality.