Agentic AI in DevOps: The next stage or just more hype? CTRL+ALT+DEPLY Ep 1

Hey everyone. I'm Alan Shimmel of Techstrong and welcome to episode one of Control Alt Deploy. Control.

Alt Deploy is one of our newest shows, exploring cutting edge and everyday topics in DevOps. It is, uh, sponsored by our very good friends at OpenText, and we're very happy for their sponsorship and participation. Today's episode is Agen AI and DevOps the next stage or just more hype?

Well, that's a loaded topic and we're gonna have a lot of fun. Our, our panel for today is myself. And then let me introduce you to our guest, first of all, joining us from Ottawa, where she just got back from, I think being in Europe.

She's one of the, uh, leaders of the Canadian DevOps community and known in DevOps communities around the world. My friend Garima Boal. Hi Garima, how are you?

Hello. I'm good. How are you?

Very good. Thanks, Garima for being here and then joining us from Israel today. Uh, she's from OpenText.

She can tell you a little bit more about what she does there. Tali Levy, Joseph. Hey, Tali, how are you?

Hey, how are you? Thank you. I'm very excited to be here and talk about agent AI in DevOps.

So Absolutely Tali. Just quickly tell people kinda where, what do you do at OpenText? So, I'm heading product and engineering within OpenText, within the, uh, application delivery management, so-called DevOps business unit.

Very good. Okay, let's jump into it ladies. So, you know, you can't walk five feet in tech without tripping over AI Today.

Everybody has an AI story. Everything is being influenced by ai, disrupted by ai, or at least, so the story goes, I don't, you know, there's, I think there's definitely a gap between the story and reality a little bit. I think reality will catch up eventually, but certainly the story seems to be way out in front.

Um, first it was generative ai, but you know, certainly this year the buzzword is agent ai. Everybody's making agents and everybody wants to manage all these agents. And, uh, how is ag agentic ai, you know, uh, influencing or or disrupting DevOps?

Karima, I'm gonna ask you if you don't mind to kick off, what's your take? Is it just a lot of hype at this point where, where's rubber meet the road? So as you know, I start from the basics and the fundamentals, right?

So where, what is our DevOps story history reminds us that DevOps is about flow feedback and experimentation, right? So what we are doing at this stage with, you know, agent take AI is experimentation. A lot of experimentation which will result in flow improvement.

And we will get feedback and then we will probably have more systematic overview of, you know, a agent take AI systems. And again, we can talk about what a agent take AI means in terms of DevOps, what capabilities we could like expect in the experimentation phase and how it'll improve the flow and feedback for the community. But I mean, again, as you said that it can, it could be perceived as a next step towards, you know, uh, the injection of agent tech AI systems in DevOps, which is more promising than generative ai, to be honest.

So I'm looking forward for that. And tally, I think I would look for your comments because you are coming from product and enduring domain, probably you have a lot to say here. Yeah.

A a and I think, you know, if we look at ai, right? You know, all the, all of the hype we've experienced so far, you know, it's about, like, I look at it from several dimension. One is the experience and the way we interact with applications, right?

And this is more on, you know, instead of in, you know, I would say clicks. Now you have conversation with data. So this is one thing.

The other thing is automation. And I think automation was, especially in the first early days, was about we define a task and we identify and basically built an agent to perform this task. But it was very, I would say, goal oriented thing, right?

And I think with iGen ai, we take it to the next level because it's the ability, you talked about flow, this is exactly it, it's not just about one task, but it's more of an ab abstracting, I would say, goal that we give to, you know, uh, to the system. Um, like, you know, identify risk and basically generate the tests that are, you know, that should mitigate the risk of the code we just committed or of this release. So it involves several tasks and several areas where you should kind of not just automate, but operate, you know, some kind of flow and thinking.

And that's the ability for agents to interact with each other and to operate whatever needs for a certain, you know, task or I would say value to be, uh, delivered. So I think that really takes us to the next level. And I think the sky's the limit because, you know, when we talk about digital workers, you know it because it's always about dev, two ops and the wall between them.

I think in the future it will all be, you know, digital workers that will interact with each other, right? And human will kind of architect this, you know, help to architect the flow. But, but yeah.

Fascinating. I totally agree. Experimenting, we're still experimenting.

Yeah. That, and I think that's the important thing. If we're gonna have one theme for today's show, gerima, I think you hit it right off the bat, which is we're still in the experimental phase.

Tali, I don't disagree with you in the future, I think all of those things are possible, doable and will be done. As I sit here today, I see a lot of experiments. But I, I'll tell you something.

I read an article over the weekend, uh, uh, mark Benioff from Salesforce, and granted Salesforce, you know, with their agent force and everything else is trying to be one of the leaders of this move to Agen ai, right? But he's claiming, you know, something like 50% of the work being done at Salesforce now is being done by agents and AI that AI is writing, you know, half of the code they're using AI is checking for the quality and security and testing using agents and stuff. Now, I'm, I'm not saying he's lying, and maybe, maybe, maybe that is really the case in Salesforce, though.

I think if you press them on it, he'll say that was for very distinct experiments, not necessarily across the board, but I don't think we're there yet. I think for most people watching this show, it very much is still aspirational, inspirational, even, but not real today. Garima, you, you talk to people all over the world with this, what do you think?

So I would suggest, uh, this like two-way conversation that from an enterprise perspective, what are the positive value adds? And I start with that because I'm a community leader. I kind of, you know, think about, you know, what agent AI is contributing to this ecosystem of DevOps.

So the first and the foremost thing is that it's building collaboration with data scientists, mops engineers, you know, they bringing data into the mainstream, right? So that is one of the biggest value add you would see when we talk about agent care assistance, right? Then comes, you know, system thinking.

You know, we are talking about agents working together. So there is agent, there is environment, there is protocols, right? So these agents will work together.

So there is a larger need for community to come together for systemizing this kind of, you know, agent, you know, uh, system architecture for example, right? And, um, if you think about how these agents will communicate with each other, so it will also largely depend on how standardization of CICD workflow will happen. How standardization of open telemetry or telemetry would happen, right?

In the system and so on and so forth. So there is a lot of, you know, positive value add from how we see the DevOps, you know, evolution happening. And as we speak, I also would also, so like to highlight that integration of AI aware observability, for example, is a greater, you know, value add when you think about agent tech AI assistance, right?

So there's a lot of, you know, work which needs to be done, and community is striving for that work. And there's a large change, which will happen from an observability domain perspective, because if you think about systemizing or productizing this AI agent systems in real time, then you would need common language, common protocol standardization. You need, uh, uh, semantics.

You need a lot of, uh, quality data, right? How do you build feedback loop as well as you would need telemetry, right? So these are all positive side of it.

Now, when we talk about challenges, and you know, obviously, uh, you know, uh, these claims, which you, we are talking about, there are two sides of the coin, right? So if you have a large legacy, for example, so I cannot claim that, you know, agent take care systems would help be helpful, and I would be able to automate 50% of my workload because I have to deal with legacy systems, right? If you're an AI native organization, for sure, you know, you have an advantage.

So that is another thing which we have to look at. Then oth other challenges. Like we have seen the debate in the community about MCP and IT way, right?

It's not new. So this also needs a lot of, you know, evolutionary thinking and community to come together. Maybe there's no one way of, you know, having a protocol or semantic language, all those kind of challenges needs to be nailed down.

And I can go on, uh, with other challenges, but I think I, I, I also want to hear from tally, like what the, yeah, No, I wanna hear from Tali too, because tally, not to throw it on you, but you, you, you are helping to run a product team here, right? Engineering, what did Garima laid it out? What does it mean to you?

I need a lot more than few minutes, but yeah, I'll try to, but, but, but I wanna relate to what Karima was saying about the data. This is so important because the inputs or outputs depends how you look at it that you get from AI are as good as your data. And in DevOps, we have a lot of fragmented systems, right?

You know, uh, sometimes an average DevOps could, could, you know, include something like 36, you know, uh, either homegrown or commercial tools. So I think the first thing, if you really want to, um, extract the positive out of AI in general, and iGen AI for sure is the data, right? You have to make sure that the data is standardized and consistent in a way, like you call it data lake, you'll, but that you can extract the value, right?

Um, so, so this is a very important thing, and there's, you know, one thing I wanna say about, like, it's very important if, I know a lot of, you know, numbers are thrown into the air, you know, of the, the productivity and, but it's very important that companies would, you know, make sure to define what they want to achieve, right? What are the business goals? Is it to improve engineering productivity, shorten cycle drive, higher quality and control risk governance?

I mean, there are a lot of, you know, I would say business values. And to apply or deploy iGen, you have to understand what you really wanna get out of it, right? And by the way, there is a whole conversation about how you measure, right?

And you probably know it, Karima, what are productivity metrics and may maybe in a, in another episode. But, um, but the one thing that is very, um, certain focused first on area. So for example, in our product, uh, we focus, um, you know, the first bit that we've released is basically on quality of and testing, understand the risk of a current release based on historical, right?

But also try to predict from historical patterns to the future. But also, you know, we have a whole, you know, parameters that you can identify which test you need to run or which test you need to generate based on the code that was changed, or the feature that should be tested. Um, and then, you know, you can say, and then you, it's, it's easier to, um, to basically measure this.

So I would say, you know, conquer the world, probably not, definitely in the first few phases. Focus, identify the flows or the areas you would like to, uh, um, you know, uh, deliver value on. And basically, you know, uh, define the flow.

And of course, you know, the prompt engineering everything, the whole communication between agents to agents and then be able to measure, because this is, I think, the important thing so you can scale, um, otherwise you will end up in a kill, chaotic environment of agents that you don't have any control of what you basically deliver. And I think that's the, that's the big fear of, of a lot of company and the trust, you know, circle that needs to be created here. I i, I wanna jump in here real quick.

So Talia, you said something, Garima mentioned it too, about agent to agent. So there are protocols that are growing up before our very eyes around this, right? MPC, all of a sudden, you never heard of MP C3, four months ago.

It seems the last three, four months, that's, it's become like a defacto standard MPC. Now, last week at the open source summit, the Linux Foundation, a announced a new project called, I think it's called agent to agent, right? A two A, Google, Google, uh, uh, contributed their a to a protocol.

Microsoft's involved, lot of big, lot of big companies involved in it. So the fight is on, not the fight the competition is on for what is the standard for agent to agent communication protocols here, right? How, you know, for me, I could stand on the sidelines and watch and see who wins.

Ali, you've gotta make a bet. You bet on them all. How, you know what, and I don't mean to put you on the spot, but what is OpenText?

Are they looking at MPC? Have you looked at this agent to agent, right? What, how do I mean, to me, this is more like APIs talking to each other.

Exactly. Yeah. Yeah.

And, and, and I think, I'm not going to touch on specific, you know, frameworks, but I think there are many out there. But I think it goes back to what I said before, it's understand what you wanna do and how you wanna do and what you need to deliver to your clients. And based on that, you decide on the framework.

You have to be flexible though, right? Um, and I think that's the, that's the big thing. You have to build it in an agnostic as possible way, because the, the environment is very, or the reality is very dynamic, and all of a sudden you get frameworks that can do this and can do that, and you have the other frameworks.

So, you know, everything we do, um, we do in a way that we can, I wouldn't say plug and play like LLM, but, you know, build it in a way dependent as possible so we can switch and be agnostic to the frameworks. Um, but yeah, we're, we're experimenting as well, by the way, and we're working with our customers, um, on, on the different use cases to be able to understand, and of course, we're working with vendors like SAP and Salesforce and Oracle to be able to connect with what they're using, whereby the way in our products we integrate with, uh, copilot, right? So I, I think, you know, uh, you have to be, I would say embrace of the ecosystem and use as I would say, generic agnostic way as possible.

So you can, I would say talk and interact with, uh, with the ecosystem. Remember, what, what if, you know, you, you get to talk to more vendors, Garima right in here. Is it a good thing if the industry sort of comes around to MPC as the, you know, the, the, the framework of choice, the, the protocol of choice, or, you know, something the Linux Foundation backs, or what?

I mean, in my mind, it's always better to have a standard, the many standards. So how standards are created, I mean, uh, we go to the basics, right? So standards are created by community, right?

So how well this is received by the community and the community, what it looks for is like the operational control, right? So if you are having a communication protocol for multi-agent deployment, and you know what important things is, how observable your protocol is, how standard data definitions you have, what kind of, uh, community backed, you know, uh, proposals you have, because, you know, uh, tally also mentioned that remaining flexible, right? So how do you embed flexibility into your standard protocols is by in inviting community contributions, right?

So I am like an open source advocate as well. So I lean towards, you know, if, uh, there is, uh, there is room for discussion and there is room for standardization, involve the community, right? Involve the community from the, uh, the very first aspect of data semantics.

How do you exchange, uh, you know, communication, uh, multi-agent protocols? And most, one, one of the most important aspect is security controls, right? So how your protocol, uh, you know, addresses security concerns.

Because think about this, in a multi-agent system, you would have, uh, intent driven, you know, uh, uh, decision making. Now, these intents should not conflict with each other. So there has to be guardrails, right?

So all this needs to be kind of, uh, developed or these kind of things needs to be kind of, uh, sim ified, right? So these are things which, uh, would lead to standardization. So history reminds us open telemetry, Kubernetes, how they become like the de facto standards, you know, through com, community contribution, right?

So I would say that, you know, there is a lot to be done in the coming months, and I feel that, you know, there might be a situation where you will make some decisions in the community that you, this is the, the standard way forward, or this is the outlook. I mean, I, I see a lot of vendors embedding MPC server into their, into their product. But to your point, Gary, look, if something, like someone like the Linux Foundation's gonna get behind a to a, that's a place where companies like an OpenText and an SAP and a Microsoft and a Google and, you know, name the big company, they could all co-opetition, if you will, right?

But cooperate for the good of everyone running low on time. I, I want to come back to a, a, a topic and, and make sure we, we we're crystal clear on it. com in 2 20 13, 12, 12 years ago.

One of the founding principles of DevOps, not founding principle, but one of the big pluses with DevOps was automation. We're gonna automate so we can do faster, take humans out of the loop, go faster. To me, the absolute distinction between just, let's call that regular automation and agentic AI is not the automation that's table stakes.

It's the autonomy. Yes. Right?

The ability to make decisions and do things in an automated fashion without a human in that loop. Now, my experience in technology over 30 plus years is some people that scares people. That scares people.

And, and then there's an adjustment before they'll, you know, kind of, they, they, they make their hands so tight that their fingernails dig into their palms. You know what I mean? 'cause they're holding on for dear life.

When do you, do you think we're ready? Are humans ready for that? Kareem, are you deal with humans, the humans of DevOps, are they ready?

If you pragmatically decide to onboard to these technologies, yes, we are ready. Because, you know, we'll have to think about, you know, how much we can expose our system at this point in time. This technology is unsubstantiated to a certain extent, right?

So if you think about C-I-H-C-D workflows, if you wanna inject this, this kind of a technology into that, pick and choose the non-critical systems, you know, make your goals in a way which, uh, which ensures that you have return of investment for these kind of technologies. As Tally mentioned, you know, uh, productivity, what is your strategic goal? You know, if application productivity is, or efficiency is one of your, like, leading goals, or, you know, you, you only hit on testing cycles, you know, you wanna cut down the lead time on testing cycles, make sure that you o you have like pragmatic goals, have systematic onboarding, ensure observability is in the system, and you have security controls if that is there.

And of course not human not, not human in the loop, human in the lead. Because at experimental stages, you need to have like these people who can ensure that this experiment, from this experiment, we learn and we take this technology forward. Holly, what do you think?

How, what are you hearing? So, so I wanna add to what Grima was, was speaking about, I think for any change, and we saw it through the years, it's not just the technology. I think the technology is more than ready to be implemented and deployed.

And of course, you know, we have the validation points and everything, but a lot of it is the people, and people are still resist. I mean, we see it, I don't know if it's like hesitation because okay, prove it to us, right? It's a, you know, trust issue, or it's also, you know, I would say in a way, resisting, resisting a change and such a big change because what it'll mean to my job, right?

Um, if I was, That's, you just hit the nail on the head. What Does it mean? Like, to Talk mean to my job?

Nobody wa likes to talk about it because everybody's are saying we're embracing it, we do it. And a lot, in, in many aspects, the management is like, okay, do it. Do it.

We wanna, uh, we we wanna see cost reductions and all of that. But, so I think, I think humans okay, people, it's culture and it's mindset. And I, I don't think it's about replacing humans.

I think you said it, Karima. I think it's about taking it to a higher level in terms of what we can do and what we can leverage the humans for, right? And humans, yeah, they're not no longer, I would say maybe like manual testers or even, you know, the ones that write automation, but they will architect, you know, the flow.

They will be, maybe this is like the engineering aspects that we all talked about it, but in the past, but then we became, you know, um, doing this and doing that and doing that. So I think it'll open, uh, their minds to do other things and to be able to improve in other areas. I, I want, I mean, there are, you know, specific things that yes, you know, AI will, will do things, but again, it's about, I would say adjusting, not just replacing.

Um, but, but I think you're right. Um, we see a lot of resistance there, and I think we have to overcome. So it's a lot of, I would say the psychological aspect of this revolution.

Uh, we now see and that, That's gonna take time. You, you can't rush that Right? Credibility, uh, to, uh, and, and to, and to say we're safe, okay?

The good people, the ones that you know, have the, the, the business context, the data that, you know, human will have a major role in this revolution. This is what I think. It's just, But there's always the imposter syndrome, right?

People, they don't want to admit it, but you feel like, am I one of the good ones? Yeah. Am I, am I safe?

Anyway, It'll get, it'll get us to higher bars, right? To, to, to Imagine more. I, I, I believe that everything I've never seen, that's how it works.

But it, it can be scary. Look, I'd love to, we're going to talk about this more, but unfortunately we're at of time for today's episode. Tali Garima will bring it back.

We'll bring some more friends and we'll talk more about this because this is gonna be the story of not just the rest of this year. I have a feeling this is gonna be the story of the next couple years and, and it's gonna be something we need to talk about. But we hope you've enjoyed this first episode, a peak into what we're gonna do at Control Alt Deploy.

Stay with us. We have more episodes coming. G Vital.

Thanks for joining us. Thank you to fintex their sponsorship. This is Alan Shimo for Techstrong.

We're out.