09x08: A Realistic Approach to Agentic AI with Nick Patience of The Futurum Group

Speaker A: Although companies are just starting to deploy generative AI, industry attention is already turning to AI agents. This episode of Utilizing Tech brings a realistic perspective on the agentic AI timeline with Nick Patience, VP and practice lead for AI at the Futurum Group. Welcome to Utilizing Tech, the podcast about emerging technology from Tech Field Day. Part of the Futurum Group this season focuses on practical applications of agentic AI and other related innovations in artificial intelligence. I'm your host, Stephen Foskett, organizer of the Tech Field Day event series including AI Field Day. And joining me this week for the co Hosting seat is Mr. Frederick Van Haren. Welcome to the show, Frederik.

Speaker B: Thank you. Glad to be here. So my name is Fredrik Van Heeren. I'm the founder and CTO of Hifens and we provide HPC and AI consulting services.

Speaker A: You know Frederik, uh, we've been talking quite a lot about agentic AI this season. I guess that's the topic, so it's no surprise. Um, but I guess uh, you know, we're still in uh, early phases of rolling this stuff out. I think that people forget how quickly this field has moved.

Speaker B: Yeah, I totally agree. I think people are still digesting what generative AI is. And guess what? Now we're talking about agentic AI and agents. Um, I think it just proves how fast all of this is going in the AI world to the question really is how can consumers kind of follow and learn about all these new technologies as they come out?

Speaker A: I agree and I think that's especially difficult for enterprise buyers who fear that um, there's so much news about this. They fear that they're being left behind maybe, but they're really not. Uh, this is really early stages of the development of this technology. And that's why uh, this week we've decided to bring in uh, one of the folks here from the Futurum Group who really focuses on this, Nick, uh, Patience, who is able to maybe provide a little perspective and a little realism. Where are we really when it comes to agentic AI? So Nick, welcome to the show.

Speaker C: Thanks Stephen. Thanks for having me. So, as Stephen said, my name is Nick Patience. I am the vice, um, president and AI Platforms practice lead at Futurum Research, another part of the, of the Futurum Group. Um, so I'm the kind of principal AI analyst here. Everybody's an AI analyst to a certain extent. But I really focus on um, the things that are, that are fundamental um, to AI and obviously Gentic is part of that. My history, my background, I've been looking at AI for over 25 years. I started another analyst company called 451 Research back in 2000. Um, and I was early on focused on, uh, machine learning and text analytics and all those things. And I've really just stayed focused on that. And um, and obviously the whole kind of interest level in the space has exploded since late 2022 when uh, ChatGPT was launched.

Speaker A: So let's start off there. Um, you've been watching this, uh, for a long time, as have we, and I think that sometimes we as well, uh, get sort of pulled into all the news and the announcements and the hype and forget that, uh, a lot of this is still off in the future. What is your perspective on the timeline, especially around agentic AI?

Speaker C: Yeah, you're right, we're, we're incredibly early, um, with agentic, when you kind of think we alluded to it just at the top of the podcast, the, the, the kind of compression of time, um, that's gone from, yeah, machine learning to, yeah, other kinds of predictive AI. Um, and then, you know, all we're coming up right up against the third anniversary of the launch of ChatGPT in November of 2022. Um, and then just, you know, so just less than three years later. We're also now trying to, um, ask enterprises to embrace agentic when they've only really, um, beginning to understand the, you know, how to operationalize, uh, generative AI in the form of, you know, chat bots and people writing prompts into them and all the kind of the interesting stuff and the scary stuff that, that, that ensued from that. So I think if, you know, if you're kind of thinking about an S curve, you know, we're on very much the very flat, flat bit of the bottom. Um, and, but there's a kind of, there's always a pressure on. And enterprises obviously, um, you know, used to be really exclusively the domain of, of the tech industry itself and financial services companies, um, that had, you know, larger software development teams. But really every company is, is embracing technology these days. So they're all under this kind of pressure. There's, there's fomo, there's a fear, the fear of m. Of missing out. Meanwhile, um, on. Every day they've got to run a business. And so this is, this is, this is what they're, they're up against. And there's obviously, um, you know, also the kind of the timeline between what seems, um, magical to them becoming normal, to them becoming boring. Um, used to take decades and now it sometimes feels that it Takes days. Um, so new models coming out almost daily, um, and then tools on top of those models. Uh and so it's incredibly difficult to keep up. That's why they engage um, analysts like us at Futurum, um to help them uh, get a kind of the big picture but also some you know, some specific guidance on it.

Speaker B: No doubt that it's early stage. What do you see as agentic AI applications that are being delivered today? And again we all understand it's early but do you see kind of a trend or kind of applications that are making a breakthrough?

Speaker C: I think it's similar to um, every kind of AI trend we've seen from back in the predictive days. You start with the things that are horizontal so they're not vertical specific. Um and every company has some sort of customer service um challenge ahead of them. It doesn't matter whether they're B2C, B2B or any combination um, thereof. So usually that is um, the first kind of opportunity. So when it was when we're talking about predictive models, talking about classification on of tickets and things like that, now we've moved way beyond that. Um, and now the ability to quote understand natural language um, is you know with, with generative AI has opened up a whole slew of opportunities uh for people to kind of least semi automate um customer service at ah scale and um, at a scale that they never, they never could. So if they, if they have only a handful of customer service people um, but they have um, thousands of software agents, you know there's, there's a clear opportunity there to be able to deal with people, enable them to interact with, in natural language um and then you know, hopefully you know resolve their issues or if not then escalate them to humans. Um and so we see, you know, we see um, see a lot of, a lot of that. I guess the other things um, we're working towards is you know we're working towards some sort of workflow automation but that gets very um, specific to each company. And so uh, that's more challenging I guess some of the more novel use cases we've had since generative AI came along. And let's be clear, obviously agentic doesn't work without generative AI is the ability to analyze um, ah unstructured data at scale and then search for hidden patterns, turn those patterns into some sort of actionable insight, um, and do that uh, over and over again. Um, like having you know, thousands of interns. And so I think you know, then you know, and then we've seen the kind of rise of um, copilot like things, whether it's the original ones like from Microsoft or other similar um tools that just sit alongside us at work and do you know, very simple tasks such as suggesting times for meetings and things like that and who might want to be in it and summarizing meetings which has now almost become standard again. Think about how quickly that's gone from you know, that can't be done to more or less every meeting is recorded and summarized. That's you know, matter of you know, a couple of years. And so you know all those, those kind of use cases there where we've got the, everybody, every company of any size has got that problem. And what I think will happen is similar to what happened with predictive AI is eventually it will get verticalized. So you know, if you're, if you're a car, if you're a car maker or you're a bank, you have quite different problems down once you get down beyond those initial um, horizontal use cases that everybody has. And that's because uh, AI is dependent on data and if you're the car company your data set is completely different than if you're the, the financial services company. More or less obviously there's, there's finance involved in cars and things like that. But you, you get what I mean. And so then the application um, becomes vertical specific and then almost company specific. Um but I think we're so we're very, very long way from that situation um, with agentic. And yeah we're really, really early and we've looked into um, some really narrow domains of uh, I say customer support, customer help. Um, so some of those kind of software companies and websites always have something.com help and we did actually a project where we looked at how many of those help services are agentic, um, or how many of them are not agentic in the sense that they rely on humans prompting them to do things um, all the way through. And it's amazing how um, the lack of true agents, autonomous agents um exist in those kind of environments. And bear in mind that is the tech industry itself and that is um, the help within their own domain. So this is not trying to solve um, a massive problem. This is trying to understand what's going on with their own applications and things like that. So it's, it's not a way of denigrating anybody for that situation. It's just that we are uh, you know the, the hyperwise is obviously going to be well ahead of the uh, of, of the Reality and um, you know, so part of my job is to try and keep our, our feet on the ground while also seeing where we're going to go in the next um, you know, three to five years.

Speaker A: I think it's really interesting um, I love that phrase predictive AI as a better way to phrase um what we've currently, what we currently have. Uh because in many ways that's really what um Most of our LLMs are used for or at least what they're doing. They're predicting what uh, you know, what the interaction should result in, whether that's generation of code or generation of um support answers, uh as opposed to agentic which theoretically would um, include some sort of tool use, some sort of external references and calculation. Um, I know that you, uh. One of the things I'd love to hear from you about is non generative steps in agentic AI tool chains. Um, you know, but it occurs to me as you were speaking there, I think one of the challenges that we've got here sort of in uh terms of vernacular is um, those predictive AI chatbots that are in the you know, company.com help uh URL. Uh those are called agents. In fact they're usually called agents. I interacted unsuccessfully with the United Airlines agent yesterday um using their terrible uh predictive AI and um, you know, I wonder if we have sort of a semantic challenge here. Is that part of your role as well, trying to help uh, clarify what we mean by all these terms?

Speaker C: Yes, definitely in part. I mean I think the, what we will get is a um. There will become, there will be more clarification with the reason I guess obviously these things were called agents is, goes back to that um customer service focus. And as you say your kind of experience is not um, atypical. And so I think the gradually over time when we get to the point where um you know the software is working behind the scenes so the agent, the agentix software is, is, is you know creating, taking action um without a human um necessarily knowing or a human having to any have, have an interaction with it. That's when you know the maybe the agent word might be more appropriate. I think it's you know at the moment an agent obviously comes from a customer service, a person, um, you know that, that, that nomenclature where that originates. Well what if it's um, executing you know, 1500 workflow steps um without you knowing and something gets done ah in the background then I think it's uh, yeah, that's the kind of goal we're trying to get to um, and also when you mentioned on some of the um, kind of uh, the generative AI and the you know, the, maybe the probabilistic and the deterministic aspect, you didn't use those words but that kind of difference. I think one, one thing I'd just like to point out that we're looking at at the moment is um, because of the early generative AI um use cases were humans typing things in um, and getting results back. Um, you know those, those kind of um, and that's probabilistic, that's using a large language model which has a model you know of, of you know scraped from, from the web. Um, and that's really useful for doing creative tasks and creativity doesn't have to be you know, literally artistic but you know that obviously is great in those situations. But creative obviously suggesting you know, ideation, you know, give me some ideas of what I should be talking, writing about here or you know we've got a meeting about this. You know, can we, can you produce an agenda on that? That's all very creative stuff. Um, and yeah they're pretty good at that. Um, and obviously there's a load of, you know there's hallucinations, uh, we know about quite a lot of them and there's some things that are just a complain incorrect and you have to work with them. But that's, that's great. Um, but there's also a need for um, agentic automation of deterministic processes. So the classic example is you know, payroll runs on the 15th of the month. I don't want a creative suggestion that says why don't you delay that to 19th, um, and then, and then that to cause an action that delays everybody getting paid. That's not, that's not useful at all. Um, and so those kind of things, there's, there's an element there of um, um, agentic automation. I think, you know this is, this is what you're trying to get to. This is what the agents are going to be doing. They're going to be automating these processes. Um, and on the side there's this, there's this fantastic generative aspect um, of it where humans can interact with natural language. But we kind of need to, organizations need to understand that there's a place for some of that and there's a place for the straight up deterministic automation. If you think back, um, you know I always like to think the history of the software industry is a history of automating repetitive human processes starting back in the 50s with mainframes in accounting and finance. Um, and they were literally number crunching um, and then we've moved along all the way through. We've always been focused on, usually um, been focused on structured data in relational databases and then data warehouses and then data lakes and things like that. Um, and we built up all these kind of software tools on top. Um, analytics tools, business intelligence, things like all those kind of things and then huge application suites and they were basically following rules and executing um, processes. But those rules had to be written by humans, they had to be overseen by humans and so on and so forth. What we're going to move towards is um, the more agentic uh future where there are some rules and then some completely probabilistic situations but the software is um, in some cases managing itself and executing on our behalf. And I think one thing, one kind of rule of thumb heuristic I guess for organizations to think about is if your problem involves a load of structured data, um, such as your customer records, your employee records and things like that, and that's where the automation is coming from, then that's probably going to end up in a fair amount of deterministic processes. Um, if the problem you're trying to solve involves a load of unstructured data. So we've got 30 thousands of PDFs and we're trying to extract tables from them and then turn that table into something useful um, that we can then use. Then you're going to end up with more probabilistic kind of um, challenges. Um, so it's just a way of um, framing things. But I think we're only in the agentic space, um, in terms of the software that's out there. Um, I think we're only really just starting to think about that. I know this sounds silly but you know, here we are in, in November. Yeah, we're only starting to think about that in the last few weeks. This stuff is um, you know, moving so incredibly quickly. I go to a lot of um, technology vendor conferences. Um, you know, this time, you know, this week is another one this Microsoft uh ignite. Um, but I've been to have been to many others um this year and we'll do again you know next year and I've been doing for obviously for a long time. So you see kind of, you see these kind of trends but you know, these days stuff is moving almost daily and uh, that's very hard for um, organizations to keep up with. Um, it's also hard for analysts to do. But it is our sole focus so at least we don't have an excuse of having to do ah, a whole bunch of other things.

Speaker B: Yeah, it's difficult enough to deal with and learn the new terminology, let alone learning the new technology. I ah, mean there's no doubt that agentic AI can help with automation. Um, the problem I think is that the technology behind AI is becoming so complex as time goes by that more and more people use AI but less and less people understand the technology behind AI. In some cases I believe people use agentic AI and believe that it's more trustful fool than in a human. Do we have a trust problem with agentic AI?

Speaker C: Oh yeah, I mean, I think there will be, yeah. I mean because obviously it's cape, it's so much more powerful. Um, because if you had to rely on a human writing prompts in to get things done all the time that's you know, useful and it's up to a point. But if you, you know, if we get to the point where software is executing, you know, you know, um, software then you can see how that could scale very quickly and become incredibly powerful and potentially dangerous. Um, so yes there's definitely a trust um, problem to be solved. Uh, I think we are still not really um, thinking about that at a deep level because a lot of um, pilots that are in enterprises now are just very much that they're pilots within sandboxes. They're not really dealing with um, with anything of enormous scale um, that where it could cause problems. But um, I think, yeah I think there's definitely, there's definitely a trust issue. There's an old joke, um, such as there are jokes in AI that um, AI is anything that doesn't work yet. In other words, um, this is impossible. Um, I'll do some AI and then once it works with AI people go that's not AI, that's just, that's just the way things work. Well it is still AI. It just solved the problem and it's moved on to another one. Um, so I think um, we're going to have a similar um, set of issues with agentic um, that we had uh, with kind of traditional machine learning.

Speaker A: Well you know your point there about software executing software I think is an interesting one because you're right that that's where the trust factor is needed most. But it's not just executing software, but it's software writing software and then executing that software and acting autonomously. And I think that that's really where um, not just the, the risk and the, the threat, the trust comes in, but also where the promise comes in. I mean if you, if you look at what these companies that are developing this technology are saying, they're saying that that's basically the promised land. So, you know, I will become truly AI. In fact, I've heard, I've heard a big backlash against that whole phrase AI. People don't want to use artificial intelligence to describe anything that's not verifiably, uh, and independently intelligent. Um, and they're saying basically that, that we will get there and we'll know we've got there. When it is truly autonomous, when it is um, taking action on its own, when it is creating um, its own motivations, when it's writing its own software, when it's executing things completely on its own, um, that's pretty, pretty, uh, concerning when it is such a black box, as you also pointed out. I mean, you know, people don't understand how it works. Even people very close to it don't understand how it works. And people seem to be enamored of it and already taking it as intelligent when it's, we really haven't gotten to that point now, um, what's the prognosis here, uh, for when this will be truly intelligent?

Speaker C: If you mean artificial, ah, general intelligence, the ability to do everything a human

Speaker A: can do, necessarily push you into that corner. But I, you know, when it's truly able to uh, to act on its own.

Speaker C: Well, it depends what it is, doesn't it? Depends on the domain and uh, depends on the problem you're trying to solve. Um, you know, you're talking about code generation there. I mean that's obviously been, you know, probably the biggest, um, apart from the kind of customer service stuff has had the biggest effect um, on um, organizations ability to you know, to automate something, um, in the last couple of years and that's, that's, that's taken off hugely. Um, and that's, that's. I think, you know, it's, it might be challenging if you're an entry level, um, um, you know, a graduate has just graduated, um, with a computer science degree looking for a coding job. I'm sure that is definitely an issue. Um, but when you think of all the legacy code for the people who no longer with us, who wrote all the COBOL and the, and the Lisp and all these other kind of languages that uh, are uh, relatively important but uh, aging, you know, there's enormous opportunity there to um, automate the, you know, the maintenance and regeneration of that code. But I think the. I don't, I Don't really um. I must admit on the kind of AI safety spectrum, I'm not particularly that concerned, um, that we are going to head to some sort of um, AGI oblivion anytime, um, anytime soon. I kind of think the, some of the people that um, push that um, you know, are doing that for a reason and that could be a kind of uh, you know, um. Can't think of the way to put it politely. Um, but there's, there's, there's, you know, there's a reason why people might want to say, you know, I told you so if something bad happens. But there's so many things that have got to happen. Um, you know, and you know, in order for software to have major real world effects, obviously, um, it does. And you know our airplanes use software and our trains do and our cars increasingly do obviously. Um, but I think, you know, uh, I think it's. So we're not just. I don't believe we're one, you know, one model away from um, Armageddon any one point. I think there's so many controls that will be in place. The fact that some of the people building the models don't fully understand how they work is real. Um, and that's genuine and I think that's um. That is a challenge. But they're working on it. And um, you know, I think it's one of those things that once this space matures as it, as it does in all forms of software, you will have governance tools and trust, um, tools in place. And I think you have to have. I mean that's going to be, that's going to be a major opportunity for the software companies that build those things. Um, but it's obviously a challenge for the enterprises um, that want to buy them and use agentic software. But I think, you know, I think we're a, um, you know there's definitely a, there's definitely a kind of. There's a platform shift happening and when platform shifts like this happen, um, you get a, you get a whole load of um. Some things become kind of features, you know, within, you know, the incumbent set of software and some things turn into companies and I think one of the, um, the really. Yeah, from my excuse, my, my point of view as an analyst, what's happened in the last five years has been incredible where you've actually now got pure play AI companies of massive scale like OpenAI and Anthropic, um, and the others. Whereas you never had that before. You always had um, you know, the same, more or less the same Names um, adding the features to what they had already and I think that's, that's where life is going to get quite interesting for everybody. Both, both in terms of the vendors themselves, the investors in this, in this industry but obviously mainly for the uh, for the enterprises and we'll be looking very closely at how that shakes out. So in other words how you know, how do you, how do the software as a service vendors um, that are used to selling package applications and sell licenses uh on a subscription basis and things like that, um to, to on number of seats used, um, that's gonna, you were already seeing um, the, the overhaul of the pricing for instance of software um, um by the promise of agentic that is already happening. Um, and we're now seeing flexibility being offered by the software companies that they always resisted doing um, before. So flexible pricing models and things like that. So um, that's a kind of. They're already looking at what they can think might happen in two, three years time um and having to adjust. So I think there's, there's definitely always um, a need for um, people to be somewhat cautious as to what they're doing. Um, but um, when you think of some of the kind of customer service issues that companies have when they're using agentic um, AI and that is they're not tickly, um, people are usually not going to get hurt as a result of those things. Um but obviously when we come to much more critical domains um, such as transportation or weaponry and things like that then that's where you have to have um, some sort of governance structure in place. I don't mean just software, I mean legislation. And I think that's, it's already happened in some parts of the world. Um, sometimes you could see it's a bit too top down and a bit crude. Um, but it will happen. Um, AI will be um, a regulated industry. Um, there's no two ways about that because it is very powerful and uh, I think that will happen. So um, it's as much to do with uh, trusting, voting in the right people I guess to get legislation right as anything else.

Speaker B: Yeah, dealing with software is not easy. It's very difficult to validate software. I mean at some point coding or writing software, you need a lot of knowledge from a hardware and a software perspective, an expertise in order to build clean and useful applications that people understood. Nowadays the software is being generated by software. Uh, I have to ask what's worse, an agentic AI agent writing software and running it or a human vibe coding, generating code and Deploying it.

Speaker C: What's worse? Um, I don't think one's worse than the other. I guess they're different. I mean the vibe coding thing is interesting because it's opened up, um, software development to so many different people with completely different skill sets. Um, and then obviously, um, generative AI writing code is obviously doing something similar. Um, you know, it's going to affect people whose job is software development. There's absolutely no two ways about that. Um, and it already, already has done. Um, but I think there's um, you know, there, there'll be so much, so much code needs to be written, um, because it's increasingly complex world and we can't manage everything, um, manually that you're going to need software to manage things that software currently doesn't manage. Um, so I think, uh, you know, I think you are going to need both the ability for um, um, software to write its own code. But also the vibe coding stuff is interesting because obviously the potential for that is you're getting the domain experts directly into the process. Now we've always had, as you know, in software development teams there's always been this kind of challenge to get the, you know, the line of business people involved at the right time. That's um, why we went from you know, waterfall to Agile, um, and all that kind of stuff. Um, and you know, that if, but if you had the actual domain expert being able to write their own little apps, um, then, you know, within a reasonable framework, a software development lifecycle framework that has um, you know, testing QA and governance in place, um, then I think that's quite an exciting notion. Um, I think there's a lot of people who um, would like to be able to um, you know, build their own apps, um, to some extent. Um, yeah, not everybody. Um, but uh, so I think it's. Yeah, it's an interesting development. Another one that's um, very recent.

Speaker A: Uh, before we go, one more thing I wanted to hit on. Something you brought up right at the very beginning was the fact that um, the world of agentic AI, uh, and processes and tools and so on will not just be chatbots talking to chatbots, that we will be looking at additional types of tools in these tool chains. Some of them may be deterministic conventional software platforms. Some of them may be, um, you know, data platforms and different ways of querying, um, structured and unstructured and even multimodal data. Uh, others may be, you know, generative AI processes. Do you see an entirely new um, type of software industry emerging here to Support agentic tool chains.

Speaker C: I don't think an entirely new industry. No, um, I suspect you're going to get um, um you know, some startups that are, that are you know covering part of the process, um, you know part of the kind of the, the software development process, the governance process, um, the kind of agentic ops process. So we had it before. When you kind of go back to um, there's a kind of category called application performance management that cropped up and this got nothing to do with AI at all really. I mean this is just how managed our applications are managed. Um and you know that, that cropped up and then you have um, you know when, when predictive um, yeah, when machine classic machine learning was started to come around you had MLOps machine learning operation operationalization, um, and then you, you know, then you, you have slight, slightly different problems. So when you've got a model, um, if you had a rules based model that only does the thing that is, it's programmed to do, that's fine and obviously if the thing falls over you can restart it and stuff like that. What the fundamental difference between um, that kind of software and AI is obviously the model adapts, it learns, it decays, it drifts, it does all these things. It's almost, you know, it's not. But it's almost organic in nature. And so that is where you have um, the you know, the challenge around that mlops was kind of supposed to um, create and then we had a load of specialist vendors um, and a lot of them are still around that cropped up to do that. I think we're going to get the same thing with um, agentic ops, if that's what the phrase is going to be. Um, um but some of those will get bought, some of those will survive and some of those will go by the wayside. But you're also going to get all the application vendors of any scale. So you know Salesforce, Oracle, Microsoft, Workday, ServiceNow, all these companies are obviously building out their own agentic um tools, platforms, applications. Ah, they all want to be, everybody wants to be the platform but not everybody can be um and then you've got obviously the hyperscalers doing their thing and then all sorts of other companies um, offering you know, agentic um tools. We are seeing a little bit of this bifurcation between um, the companies that are going after the, as we said earlier, the creative, the probabilistic creative opportunities. So those aiming at marketing departments um, have had quite a lot of recent, of, of strong traction um, because they're solving a problem that really could not be solved until generative AI came to LLMs came around, it was just basically impossible. Um, and then those that are dealing with more deterministic things sitting on top of relational databases, so CRM, ERP and all those kind of things. So we're seeing a little bit bifurcation there, but you're going to get some winners out of that I suspect. Um, um, that will either survive or get, or have a good exit, um, the financial exit or something like that. So I don't, I don't think, um, but then again, as I mentioned earlier, this is the first time where we've had pure play AI companies of any scale and OpenAI is certainly off scale and it has ambitions from the device to applications and everything in between. Um, obviously it's known as a model provider but you know, it's obviously trying to build chips, it's trying to build devices. You know, however, how well it gets on with that we don't know. Um, and you know they're obviously now influencing, you know, where data centers get built, um, how much energy is used. Um, this is, this is an incredible um, you know, development in the space of a, of a decade from scratch. And so you are going to get those kind of companies. I wouldn't, you know, you wouldn't necessarily. That's, that's peculiar to Agentic, but that's peculiar. But it is peculiar to generative AI which Agentic is based on. And so I think it's going to be um, really interesting to see how you know, that company and one or two others of massive scale, um, uh, you know, influence the way, the way the rest of the, um, the rest of the software industry goes. And I think yeah, we're going to have standards, we've got MCP servers and we've got A2A protocols and there's going to be more, there has to be more. Um, then it's the question of who becomes, you know, who's the platform, who's enabling layer within it that makes it work properly, properly who are just trying to sell apps and they will use anybody's agentic platform and tools vendors, um, and stuff like that and then obviously the chips underneath. And so companies are always looking for companies in the sense of enterprises that buy this stuff are always looking for options and looking for diversity. Um, and whether that be down the silicon level, where at the moment there isn't much diversity, um, or right up the application level, there obviously is. So I think it's going to be um, it's going to be a fascinating, um, few years, uh, in the uh, the agentic AI space.

Speaker A: Yeah, absolutely. And, and I feel like, like you said, that these um, massive AI companies, along with a lot of the traditional vendors, you know, companies like Oracle, um, Amazon, Google, are angling to build an AI platform. Really. And um, so we'll be definitely watching that. Um, we do have to wrap, uh, unfortunately, I think we could talk to you for um, many, many hours. Um, but unfortunately, uh, the time frame for this episode is done. So thank you so much for joining us. Um, before we go, if everybody else wants to continue speaking with you, uh, where can they find you and where can they find your uh, coverage?

Speaker C: We're@futurumgroup.com um, and you can also find me on um, Twitter x@nickpatience and um, on LinkedIn. And yeah, I'd love to, love to hear from anybody.

Speaker A: Absolutely. And um, of course uh, we will be continuing on as well with utilizing, uh, AI podcast series for Futurum Group. Uh, so folks should look for that in their favorite podcast applications. Uh, Frederick as well. Uh, where can we continue this conversation?

Speaker B: Yeah, you can find me on LinkedIn or on our website hyphens.com

Speaker A: and as for me, uh, you'll find me, as I said, on utilizing AI on the Textron Gang on uh, many other uh, platforms, uh, here within Futurum Group along with on social media as sfosket. Thank you for listening to this episode of Utilizing Tech. Uh, you'll find this podcast in your favorite podcast application as well as on YouTube. If you want to see what we look like. If you enjoyed this discussion, please do leave us a comment. A rating, a review. We'd love to hear from you. The podcast was brought to you by Tech Field Day, which is part of the Futurum um group. For show notes and more episodes, head over to our dedicated website which is utilizingtech.com or find us on X, Twitter, BlueSky and Mastodon utilizing Tech. Thanks for listening and we will catch you next week. Mhm. Sa.