09×09: AI Gets Personal with Agents Acting on Our Behalf

Ag Agentic AI is an autonomous system that learns, adapts, and uses tools on behalf of its users. This final episode of season nine of Utilizing Tech brings host Steven Foskett, Frederick Van Herrin, and Guy Courier together to reflect on the lessons we've learned over the last few months. Welcome to Utilizing Tech, the podcast about emerging technology from Tech Field Day, part of the Future Home Group.

This season focused on the practical applications of Agen, ai and other related innovations in artificial intelligence. I'm your host, Steven Foskett, president organizer of the Tech Field Day event series. And joining me for this final episode of Season nine are my two co-hosts from the season, Frederick Van Hern and Guy Courier, Frederick Guy.

Welcome to the show. Well, thanks for having me. I'm Frederick Van Hern, the founder and CTO of ens, and we provide HBC and AI Consulting Services.

Yeah, it's great to be here. Uh, guy Courier, I'm an analyst at the Futureum Group and, um, an occasional participation in, uh, participant in Tech Field Day as well. Absolutely.

And I'm Stephen Foskett, organizer of Tech Field, including the AI Field Day event that the three of us all attended here during the recording of this season of the podcast. And, um, going forward, host of the new, uh, utilizing AI podcast over on Techron ai. But of course we'll be back with future seasons of utilizing tech as well.

Uh, let's sort of, I guess, wrap up season nine here and talk a little bit about the lessons that we've learned rather than making this just a retrospective of the various guests that we've had this season. Uh, let's talk about some of the takeaways. Um, Frederick I'll start with you.

Yeah, I think we, we learned a lot. Um, I think Agen AI is still kind of a moving target in the sense when we ask people about a definition, you know, the definitions can, can vary a little bit, but I think overall people have a, a, a, a great understanding or a better understanding, uh, of what AI can do as far as, um, reasoning and thinking, um, and with large language models. And, and I, I think that's what we saw during the episodes.

I mean, we, we had some people talking about applications. We had some people talking about use cases. Um, I think overall, uh, com in combination with ai.

Uh, field day, I think we, we got a, at least I got, uh, a kind of a view on what people are doing on both sides of the fence, customers as well as vendors. I think, uh, one of the things that has most impressed me, um, is, um, how rapidly it's developing rapidly. Everything in AI is developing rapidly.

Um, in fact, it's probably changed significantly since we started this podcast, um, series. Um, so that's one. Um, I think from, as a practical matter though, there's so much that you can do just to step into, uh, to move sort of, let's say beyond, certainly beyond chat, certainly beyond basic use of, of of AI and into, into agentic, it's a question of connecting agentic to systems and to each other and doing a little design work.

It's not necessarily that far. You don't have to do the latest and the greatest, and there are a lot of interesting platforms and ways to, to do this. There are a few AI studios out there to help you build agents, um, some that are incorporated with maybe services that you already have.

Yeah, guy, I think that's a, that's a good point. I also think that applications and use cases typically came from vendors who, at least the view from vendors, I think at Gentech AI and MCP and like you said, the studios that, that people are delivering to the market are helping people that typically were not engaged, or at least not on the vendor site now, can build applications that are kind of very close to solving their problem as opposed to applications that are solving other people's problems. Yeah, that's a good point.

And, and that came across, of course on our episode with Articulate, but also throughout the season when it comes to Agen. If, if the idea is that you're going to make, and again, we, we spent a lot of time trying to de define AG agentic earlier in the season, but, uh, let's sort of roll with that. If, if the idea is that you're trying to make AI agents that can act autonomously, that can ingest and process data that can call other tools on your behalf, it really is important to make sure that they're up to the task, that they're not just sort of, uh, generic and that they are able to respond to the needs, not just of the business as a whole, but of the users, the people who are trying to make use of those agents.

And that came up, um, you know, many times throughout this, I see a really strong parallel between, uh, the process automation space and the agent AI space in that, in both cases, it's sort of, sort of a, a, a twist on that whole no code, low code concept, uh, or, you know, a way for people to make AI do things on their behalf. And, and it's funny 'cause during this recording of this season, um, a couple of things happened. One, um, as I, as I talked about at AI Field Day, uh, the Futurum group released a report on enterprise agent AI platforms, uh, where they highlighted some big companies, you know, Salesforce, Microsoft, ServiceNow, IBM, those kind of companies.

Uh, at the same time, uh, we also saw people really leaning into things like the perplexity web browser and, um, the new OpenAI web browser as a way to basically have a personal agentic system. And at the same time, of course, you know, apple is hopefully gonna be releasing more AI features on iOS and, and Google just keeps pushing Android forward. And all of these I think, reflect that that idea that that agen should be personal, should be usable, should be, um, something that people can really interact with or else it's really not gonna be able to, to, to achieve the goals.

Right. One of the things I definitely picked up from the series specifically was when, uh, when, um, I did the, the episode with, um, with ZA because, uh, one of the structures that, uh, uh, Luke Norris there provided, um, was around three different types of, was, was a view on three different types of agents. Because there are ones that are, and I don't remember the types, but there was one, there's ones that are responsive, there are ones that, um, you know, are autonomous or operate on their own.

And then there's a third type I'm not remembering right now. That was really helpful because, um, one of the mind shifts that I, um, you know, have experienced, um, since we started this, uh, was, uh, as to what, um, a agentic AI is, what an AI agent is, because, um, I, I think just, you know, kind of to be a kind of clastic about it or something, I started off by just saying an AI agent is just an agent. It's just got AI in it.

Um, maybe oversimplification, but seemed to me that if you just think of it as an agent that uses ai, then that helps. It helps you understand what it is that you would do with it, um, that was clearly wrong. Um, because the way you build it, the way you run it, how it works is different than a standard agent.

It's still an agent, it's a kind of agent, but it's different enough that it's not just an agent that includes ai, definitely not. Um, it can do things on its own. It can learn, um, it, or which is to say it can be self-trained.

Um, it can interact in ways that are, um, not just unpredictable, but, uh, what, what, what do they call it? Non determinative or something, or where it might, might do something different the second term. Yeah.

Um, that is different from every other agent we've ever computed, uh, or sorry, programmed and, and used. Yeah. What I think is nice about Agen AI is that we had access to large language models in the last couple of years, but the big challenge was how do you integrate all of these components with your own data, right?

Because in the end, it's your own data that makes or creates the value of an application. And I think that's one of the things that gentech AI and maybe, you know, maybe not necessarily a definition, but it's, it's kind of allows you to bring those different large language models together with your data through standards APIs, if you wish. And I think on top of that, that makes it very accessible to individuals.

I think if you look at people building applications to go around large language models, in the past, those applications were very static, meaning the, a large language model couldn't change. You couldn't interact with another large language model. You couldn't daisy chain large language models.

And today you do have these capabilities and, and it brings a kind of an interesting factor to the, to the foreground, which is an application today is, is more dynamic than ever. In other words, it's, it's not a, a, a, a finite state when you build an application. It's a work in progress.

And you can see that too with, uh, with vibe coding, where they're not suggesting to build or com create a prompt that defines your all application. They're basically saying do it in, in individual steps. And that's really interesting because I believe that that allows you to build applications that, to Steven's Point, are more personal, because you can iterate through it and you can start with the baseline and then add functionality.

To tie this with, with, uh, one of the episodes we had is that I, from a speech background, I always think about text as being the main communication piece. Um, there is multimodal, right? Multi this audio video, uh, text and this input and output.

What's really interesting, um, you know, uh, is this idea of, so, I mean, do we all remember when Google started issu, uh, um, uh, publishing services, new services of various kinds is going back 20 years or 15 years, whatever it was in beta. And the beta lasted forever. And this was kind of, may not have been Google leading the way, but it seemed that way.

To me, the perpetual betas, what you're talking about is true perpetual betas almost. It's almost like the, the, the agent, the software is never done. It's always going to be, you can prod it into evolving you, it can evolve on its own.

It's never static anymore. That's a kind of a wild concept. It's a little bit what Satya Nadella was referring to a year or so ago when he said there was not gonna be SaaS anymore, which is to say, you know, the creation of, of agents creation of software, and then it's their destruction or, or they'll scoff into a hole until you need 'em again.

Maybe you'll never need 'em again. Um, that's a very different way to interact with systems. Very different.

Yeah, exactly. And I, and I, I definitely see that dynamism happening here. It's, it's in, in a way, you know, and even beyond non-deterministic, um, it is almost, as you're saying, both of you're saying that, you know, that the, the application you use or the workflow you use might be different today than tomorrow, than the next day.

And it's interesting, right before we launch this season, um, OpenAI introduced chat, GPT five, and one of the hallmarks of GPT five, as I actually said back on the first episode, is that it is, well, I guess depending on how you wanna define it, is almost agen. And I've been doing a lot more work with GGPT five recently. And, um, it is really interesting if you watch the workflow there, how it interacts with you and to both of your points, essentially, you know, you ask it a question and it is calling, uh, specialty tools to answer your question.

It is responding to you by looking up data, doing a web search, using a calculator, you know, u and using Mathematica, using, um, you know, variety of different ways to produce what it is that you're asking for. And specifically, you know, I've been having it process, uh, to Frederick's point about multimodal data. I've been having IT process images and, um, and give me JSON.

And it is wild to watch that workflow because it is using, like I said, it's using Mathematica, it's using, um, other, uh, generative AI tools to process images and identify items in the images and it uses, um, you know, all these different things. That's, I think, what we're looking for here with these next generation of tools. It's not about making an artificial super intelligence.

It's about making a system that can really step through, you know, define the next phase, figure out the tool to use, use that tool, take that output, go to the next step, go to the next step. It's a very personal way to do this. Um, unfortunately, it's also kind of frustrating and it's been kind of frustrating for me as I've been using these tools because I just wanna shake it sometimes and say, no, you went in the wrong direction halfway through.

But at the same time, I feel like it's more likely to generate an answer than, than trying to come up with some super machine intelligence. Yeah. And that's a perspective from, from a user side, um, slightly off topic, but I, I watched an interview from the, the people that started, uh, clo uh, CLO code coat.

And, and basically it was kind of interesting to listen to them. It's, it's, everything is almost accidental, you know, they had no intention of building it, but somehow they found something and then they build it. And then, and another statement they made, which I found interesting, is that not only does Angen AI system learn from its users, it's actually also a learning from its own output.

And that's, that kind of tells me that we as consumers of agentic AI might not always understand what's going on. The people building agen, ai, large language models, even they themselves have no idea where it, where it's going. They're also being led by the large language model by itself.

I wondered if, though, just to, you know, spice things up a bit, if we're looking ahead, one of the issues right now in training, right, is, is in training foundational models, it, it's not quite an issue yet. It seems to be getting there is that there is a sort of a peak, there's sort of an optimal training level, um, in terms of quantity of data and number of cycles and stuff because of the amount of available data. Um, in other words, uh, we could run outta data as much as we talk about the explosion of data, we more or less run out of data in training the models and, uh, the use of synthetic data or what, what have you.

Or more specifically, there, we're looking ahead to where some of the data being used to train is actually output of AI that already used original data that was the output of humans, right? So that's that decreasing quality possibility. This could get accelerated with AgTech.

Um, what you're talking about, Frederick, is sort of systems of systems and where the systems get abstracted far enough from sort of the original human origin, so to speak. Um, they are still derivative, AI is still a simulation. Um, I tend to, you know, object a little bit towards like reasoning or thinking or that sort of thing, or even the word intelligence because of it.

And, um, I'm just, uh, I'm thinking ahead to how we utilize AI going forward in a way that remains productive, even if it starts to be a whole lot of AI talking to each other. Yeah, that's actually a really interesting point. I don't wanna get all philosophical on y'all, but, um, it's my job.

We already did see some, um, examples of AI agents talking to other agents and developing their own mechanisms of communication, their own vocabulary. Um, we are trying to use, I mean, I don't know about you guys, but I basically want AI to give me JSON if, uh, if I'm using it in any kind of application as a, as an application agent, assistant, that kind of thing. Um, I, I don't know that JSON is the optimal format for agents to talk to each other, you know, I mean, with MCP, you know, again, we're trying to impose our human will on these things.

I would not be at all surprised if, if future AI agents interact with each other in a an API and exchange data in a format that is, I don't wanna say completely illegible, but at least not what we would have designed, because it turns out that that's an easier, better, more efficient, or just sort of evolutionary sort of way of, of exchanging information. Um, because essentially if we're gonna set this stuff out there, doing things on our behalf, we've gotta let it do its thing. And, you know, we can't micromanage it and babysit it, Right?

I think as pharma formats are concerned, I mean, Jason is, is a very good format, and you can pretty much communicate any type of data you want. The challenge with Ja, Jason is, is that Jason is not really meant for large amounts of data. I think the challenge becomes when you want to exchange a lot of data in a small amount of time, like for example, uh, Dr cars driving and exchanging videos with each other, um, that's where I believe Jason wouldn't do so great, but yes, uh, you know, Jason or something else, um, there is definitely room for, uh, for some kind of more advanced format, but I I, we have been through so many iterations, I I don't think Jason is a bad format at all.

Yeah. JSO is, is certainly the worst format, apart from all the other ones got. At least they're not using XML.

You're channeling, uh, Winston Churchill, the for us, aren't you? Um, yeah. Uh, I think though Frederick Steven's point, as I took it, or maybe I'm taking it a step beyond, is, uh, these, these systems are gonna start designing their own interfaces to talk to each other, Right?

I think that's the, that's the next step, right? Is that the machines decide how to communicate with each other. I mean, the bottom line is as long as the, the, the, uh, the communication channels are authentic and, um, and follow certain guidance, maybe, maybe they will.

Um, who knows, right? I mean, Jason is still a, a human readable format, right? I mean, it's, the reality is, is those machines don't need text, right?

They need binary. And so they could talk in four bits as opposed to eight bits or whatever, right? It's, it's, Or in who knows what It'll be like reading a machine ballot in Texas, you won't know exactly if you're getting to vote for who you thought you voted for.

I'm making a joke. It's a reliable system, but I do object to using barcodes to vote there. I just said it.

But, uh, yeah, who knows how they want to talk to each other. They'll find some optimal way to them, or in fact, they might find a suboptimal way that really doesn't work, but that they came up with because they're non-deterministic. So let's, uh, kind of turn the page and talk a little bit too about some of the other aspects.

Um, you know, we talked earlier about the platforms, uh, that are being used. Um, part of the conversation that we had this time around was talking about, um, running, uh, agent agent applications on personal devices, um, to the cloud. Uh, you know, Frederick, uh, brought in the concept of multimodal data.

Uh, let's talk about some of those other elements that are evolving ai. And again, I feel like overall it's all about making it more useful, more personal, more, um, actionable. So, uh, what's your take on, I guess, the, um, the ag agentic platform concept?

Um, you know, we, we've heard the enterprises, um, embracing things like the Salesforce, uh, agent force platform. Um, you know, we've heard companies talk about a variety of, um, you know, really kind of nuts and bolts, uh, almost, uh, you know, VM manager kind of platforms for running these things. We, we've talked about as well.

Um, the different ways that Apple and Google are evolving their ecosystems to run agents, uh, or ai, uh, in instances locally as well as in the cloud. Um, where, where is that all going? What is the sort of the common through line that you're seeing in platforms to run AI applications?

Guy? Well, I think that you're, you're giving me an opening to rant about data platforms. I think there are three platforms we're talking about, and they can, they can integrate, you know, you, they can be presented to, uh, to the user, um, just as a single platform.

Um, but there's, there's, I, the word that strikes me in terms of ag agentic AI activity is dynamic more so than applications. More so, certainly more so than, than, than than, you know, model based inference, even including rag, um, the type of data, the type of access required, uh, the types of, let's call them queries or needs, data needs, um, for an agent, um, will vary considerably. Uh, and the data platforms need to be able to keep up with that, not just from a quality standpoint, an availability standpoint, and all that other sort of stuff.

Not just having the data in the right place at the right time, which is a lot of what they do, but from a security and access standpoint, that's extremely important, especially when it comes to things like, um, uh, defense against, uh, uh, bad AI actions and actors and, um, that sort of thing via MCP or via prompt injections or whatever it might be. Um, so that's, that's one of the three. The other, the other two, um, would be, uh, the, the, you know, platform on which you build and run and manage the lifecycle of the agents.

Um, and then the third, I think, um, I just forgot, actually, to tell you the truth, there's a third one in there that I'll remember in a moment. Yeah. When I, when I talk about platforms, I mean, platform is such a generic term.

I mean, I, when I talk about a data platform, it's almost like the lifecycle management around data, right? Because in the end, data drives it. It's, it used to be different, but today the algorithm is pretty generic as long as you bring the right data to the table.

And so lifecycle management is really important. Um, and I look at a data platform as the component that makes sure that your data is clean, fresh, uh, always up to date, um, and allows you to, uh, select data based on, on privacy and, and, and, and, and other regulations. And then you have the, and Also allow or not allow data depending on use and application user, and yeah, Right?

And I think that's, at least, and again, I'm, I'm not necessarily an infrastructure person from the ground up. You know, I started as a data scientist, so me, a data platform is, is the old storage market, right? Where people were talking about storage devices to me today, we, I don't talk about storage devices, I talk about data platforms.

So it's, it's a, a lot more than just that. And then you have the, the, the execution platforms, right? The, the, the, the super glues, if you wish, like MCP and those platforms.

You know, apple has their own platform, and it's almost like we have a standard with MCP, and then the, those platforms kind of use those MCP servers or concepts to, to kind of build their own world. Um, the, the only caveat I have with, with so many organizations building their own platform, is that innovation goes so fast that it's gonna be very, very difficult for people to, to stick with a particular platform. You know, some platforms are gonna disappear, and new platforms will, will, will, will, will be created.

I just ex I just asked myself from a consumer standpoint, what does that mean for me, right? If everybody has their own platform. Yeah, that's true.

Because in the, you know, cloud infrastructure space, um, we've seen very much that there was a proliferation of platforms, and now everything is sort of coalesced around Kubernetes, for example, to run cloud native applications. Um, and I think that a lot of the reason, one of the reasons that everything runs on Kubernetes is not because it's the best thing ever, but just because it's a thing that can run anything. And I wonder as well, do we need that?

Are we gonna have that? Um, my suspicion is that companies like OpenAI, uh, Microsoft especially, are going to be trying to position themselves as sort of the arbiters of those future, um, agentic platforms. And I wonder, uh, to what extent that will happen.

I mean, OpenAI has made a big bet to become the first mover to provide sort of the, you know, to be the windows, um, of ai. At the same time, all these enterprise companies would love to do that too. I mean, I'm sure that Salesforce and ServiceNow and, you know, companies like that would love to be the, the standard platform that you run enterprise AI applications on.

And, um, I, I don't think they would even argue with me that that was their goal. So, um, do you think that we will have a different platforms for personal versus enterprise? Do you think that, that there's, um, do you think there is going to be a windows of ai?

No, no way. This is worse than the cloud. I mean, the cloud allowed, now granted, there's the, there's been this force of gravity towards a Linux and cloud native and all that sort of thing.

Got it. But that has not made Windows obsolete in cloud native or in, in, in, in web application or in application development. Not to mention other, you know, operating systems.

What I mean by it's worse than the cloud is the cloud birthed the API and the so-called API economy, which is, you know, marketing term of art, but refers to, um, APIs everywhere all the time, such that you can plug things together. Now, does that, um, does that, you know, is that it's not magical, it's not magic pixie dust. Um, you need to do a fair amount of infrastructure work among other things in order to get things to work like you expect them to.

But this gets, the AI is so able to permeate every layer that, um, trying to be some kind of standard in any way for a, an AI stack is fool's game. It's ridiculous. I, I think Windows, or at least Microsoft in general, was trying to dominate the household, meaning that they wanted to run on the box in your house.

Um, I think in agen AI platforms, I don't think they have that goal. I think they, their goal is to give you a an API key and that you are heading their, their data centers, right? Because they can, they can provide that service a lot better, because imagine that we, you know, if we all expect fast returns, right?

We always expect that when you give a prompt that you get immediate results, if, if they would aim for something like Windows or like a box at home, they have no control over performance and, and latency. So I think platforms, agentic AI platforms are not more, not like Windows, but more like remote services, where you, you just use an API key to hit that particular service center and then get a, a fast response. So as we're nearing the end of our episode here on the end of our season, um, I wanna ask, uh, maybe a difficult question to the two of you.

And that difficult question is, we, we've been talking about Agen ai, we started trying to define it. We've talked around it a lot. We've, we've given a lot of examples and a lot of descriptions of where companies are going, where peoples are, people are going.

But is this really a thing, uh, you know, let's, let's, let's meet our applica or our audience where they're, where they are and say, is agen AI really a thing? Are we gonna be talking about this in a year or in five years, or is in, in 10 years? Or it's just this just 2020 fives thing and the 2025 way of talking about ai?

So, um, guy, uh, what do you think is, is AG agentic AI really a thing with legs? I think it is. I think we will be talking about it in a year.

I'm not sure about three years though. This development of the, the development of this particular wave of revolutionary tech is so fast. I used to say about cloud computing, that we would stop, call it cloud computing eventually, we would just call it computing.

I was kind of wrong because we just kind of call, call everything cloud now, more or less. But it did sort of disappear as a distinctive term. And I think that Agen AI will disappear as a distinctive term, and it will just be ai.

Um, on the third hand, we have a GI coming, which I do not expect to be what it purports to be. Um, but I, I think that we're just gonna start calling it all ai, including things that we don't call AI now. So Agen, um, in a year?

Yes. In three years, not so sure. Yeah.

To me, first of all, agen AI to me, to me is, is, uh, is a reference to the ability to use multiple large language models, interchange daisy chain, and bring in your own data in an efficient way. That to me is the gente. KI And so Will Agen is a term still exist in three years?

Almost guaranteed? No. Um, because the marketing, marketing people will come up with something else.

Uh, but technology wise, I think the question for me in the future will be, will large language models as they exist today, still be at the core, um, of modern ai, whatever it is in a few years? Or will there be something else than a large language model? That to me is kind of, you know, what will happen in the next couple of years.

But Hang on just one minute, because the second half of your question, Steven, was will we be talking about Agen AI in a year? Are we at the beginning or are we at the middle as recently as Agen AI came into the discussion? Are we at the beginning or are we at the middle of it?

If we're the middle of it, we might not be talking about Agen ai. Maybe we'll be talking about recursive ai, which is, um, something relatively new to me, which is, uh, ai that, that, uh, well, Frederick knows what it is. It's, it's AI that creates itself, creates other, you know, so to speak, creates itself.

Um, maybe that's, so maybe that's, so maybe we will, the agent AI will be so last year, so five minutes ago in a year's time, I think that the, if, if you were asking me there guy, I think that, uh, the, um, the analogy you gave of cloud computing is true. And that's like sort of, I remember having that conversation 20 years ago. This isn't cloud computing, this is just computing.

This is just how things should be run. And I think that most of the concepts have already been incorporated into everyday applications and, and basically what we call modern applications run on modern platforms. And I, and I think the same is gonna be true of ag agentic ai.

Frankly, I don't think that we're gonna be talking about agentic AI as sort of a capitalized proper noun. I think it's gonna be AI agents that operate on our behalf. And I think that that's what people wanted AI to do anyway.

And so that's gonna be the future of it. So we shall see, um, that's that for this season of, uh, utilizing tech. Uh, thank you guy.

Uh, thank you. Of course, Frederick, this is your nth season here supporting us on our podcast. It's always wonderful to have you, you know, guy, it's been welcome.

Uh, great to welcome you. Uh, before we go, uh, tell us, uh, where we can continue the conversation with you guy. com.

You can find my writings there. Um, LinkedIn is a great way to see, like, you know, when I'm engaged in a conference or a tech field day or something else, you'll see me there. social at Blue Sky.

Yep. com. And as for me, you'll find me at as FoST on most social media networks.

Uh, I do a lot on LinkedIn. Uh, I'm on Blue Sky and mast on even. Uh, and you would love to connect with you there.

So thank you very much everyone for listening. ai where you will find a new podcast called Utilizing ai. It's not the same format, um, but you will see these faces there, I guarantee.

We're gonna have Guy and Frederick join us on e uh, utilizing ai. Uh, we're recording that and, uh, publishing a new episode every Wednesday. ai, uh, or on YouTube or in your favorite podcast application.

Um, utilizing Tech Will Return, uh, we will return with a new topic. Um, we have so far, uh, talked about a lot about ai. We talked about data infrastructure.

We've talked about Edge. We talked about, uh, even some, some hardcore tech, uh, CXL for one season. Uh, again, you'll find utilizing Tech in your favorite podcast applications as well.

Um, also on YouTube. Uh, we would still love to hear from you. If you enjoyed this discussion, please do reach out, maybe a suggestion for what we should cover next season.

I'd love to be, uh, love to entertain that. And maybe we can welcome you as a guest on the podcast. Uh, this podcast is brought to you by Tech Field Day, which is part of the Futurum Group.

com, or find us on X Twitter, blue sky, and mastodon at utilizing Tech. Thanks for listening, and we will catch you on the next season of utilizing Tech.