Models, Harnesses, and Multi-Agent Systems

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Welcome to another episode of the Practical AI podcast. This is Daniel Wiedenach. I am CEO at Prediction Guard, and I'm joined as always by my co-host Chris Benson, who is a principal AI and autonomy research engineer. And on these episodes where it's just the two of us, no, no guest, we like to take a moment to really sometimes talk about just a topic we want to talk about and learn about, sometimes be more educational, dig into specific details. And I think that's more of the direction we wanted to go today, right, Chris?

It is. And actually, we've done several of these recently, and I think it's important to call out, if you've been listening to some of the recent episodes, there's so much happening right now that one of the priorities for us in the show is to try to help people, you know, kind of really stay up with what's going on, not just from a newsy standpoint.

In a practical way.

In a practical way so that you can actually go do this yourself in your organizations. And we kind of realized that while there are some, like we always have a huge funnel of guests to come in, we realized we needed to kind of pause and take a few episodes and try to just get people caught up to where they need to be because so much is happening right now. And so that's a big part of why we're doing this right now. And so we hope this is going to be helpful for a lot of folks out there.

Yeah, this is always evolving and the way people use terms is always evolving. So it's useful to even sometimes redefine some of those terms. And I promise we won't belabor this point too long, but I do think it's worth us defining a few terms here. Up front so we're just all on the same page, we're talking about the same thing. And some of these we've defined before in other episodes, like you said, but again, the semantic variance of terms changes over time and it's, uh, it, it's worth just updating and, and reminding. So the, the first term obviously is, uh, an AI model or a model, and very often And this actually hasn't changed, although maybe some of the things we refer to as models have changed, but an AI model is really, if you're more coming from like the technical side, you might think about this like a function or a software function. Maybe if you're coming from another angle, really it's just a transformation of data, right? Like I give you an image and you tell me what's in the image. It transforms to objects in the image. That's an object recognition model. Right? Or I give you a series of words and you tell me what word should come next. That's a large language model. It's a transformation of input to output. It's a function implemented in software, and these models generally do a, a single type of transformation. So a large language model does that transformation that I mentioned. It takes input words or text and then generates text output. A language vision model changes the input side, changes. So it might take language or text plus images or just images and then produces text output. An image generation model, as the name might suggest, takes in text and goes the other way. It generates an image out, out the other side of the function or data transformation. There's, you know, im— basically any combination just think of the model as that data transformation. And then you could have a lot of things on the input side, you could have a lot of things on the output side. And depending what type of thing, type of data is coming in on the input side, coming out on the output side, we refer to these as different types of models. So an LLM, a language vision model, an image generation model, a video generation model, a forecasting model, an autoregressive model in that case, an anomaly detection model, et cetera, et cetera. There's all sorts of types of models and not all these are neural network-based models. A lot of the ones we typically think of today are, but a model itself could be composed of guts that take a variety of forms and the way that that function does the transformation, that could take a variety of forms. But at the end of the day, it's a function or a data transformation.

Now, that's a great explanation. And it really— and like the architectures have gotten much more complicated over the years and able to do a lot more functionally. But at the end of the day, I think you mentioned neural networks and stuff is it's still neural networks, you know, kind of plus plus architecturally in terms of how that function is. But I really like the notion of a function because, you know, You give it an input and you get an output, which in this case we call an inference. And, you know, kind of keeping that in your mind as to what it is is pretty important 'cause it's not magic. You know, we get a lot of that in the mass media about AI. And so yeah, we're talking neural network-based architectures that are able to do transformations along the way. So, and then, and now we're building all sorts of stuff around it, like agents, you know?

Yeah, exactly. And maybe one distinction too that's really important with models is some are what's called open weight or open source models. Some are closed models. Good point. And so these models can take, at the end of the day, they're all kind of doing the same sort of thing, but the way that you can access them as a user might be different. And some people might have seen in the news, even just in recent days, NVIDIA and others making pleas to the community around the importance of open weight models or open source models. So what this means is if you're going to run that function, if you're going to run that data transformation, so you're going to take text in and generate text, or you're going to take text in and generate an image, some— there's really two components to that. One is you have to have software that runs that model and actually executes it. The other thing is that you need what are called parameters or weights and biases. These are just a dataset of numbers that configures that software function to operate in the way that it operates. And so you need both of those things. And sometimes you, you as a user may have direct access to both of those things for a given model. So let's take, for example, an an open model like a, maybe a Gemma model or something like that, you can go to Hugging Face, you can download the weights of that, and there is software that runs it, the Transformers library from, from Hugging Face, for example. And so you can have both of those components, you can run it, you know, on your own laptop, in your cloud environment, wherever. Other models are not released, either the software component or the and/or the data component of the weights are not released. And those are generally called closed models, often kind of just accessed through a managed API. So program— programmatic interface or a chat interface or other interfaces. And those are sitting behind a company's proprietary infrastructure. They're still running the software. They, they still have the same thing that they're running on their end. Just their version of it, and they keep that proprietary as their IP. So that's what we mean by open weight or closed.

And one more thing to throw in that, because it's relevant to the rest of the conversation, is these models can be of many different sizes. What we call frontier models, which are the largest, most complicated, and produce the most sophisticated output, often require hardware that is going to be prohibitively expensive., and so that is a huge investment if you're going to go that way, uh, on— yeah, and, and, and the cloud providers and these model— these, you know, kind of top-tier model creators, uh, have enormous data centers filled with, with server racks to manage this. Um, not all models are at that level. Uh, you can, you know, you mentioned Gemma, which is, uh, some of the smaller, gradually smaller models, uh, that can be downloaded and run locally with whatever hardware you have available, if they're open. Well, the Gemmas are open and there's others as well, both out of Europe. There's a lot out of China, which is kind of the powerhouse of open models now.

Yeah.

Yep. And so, and that's, and it's interesting that we're in a moment where if you're wanting to run a fairly sophisticated model on your own, you know, on your own server, you're probably most likely going to a Chinese model these days. Uh, or, or they're the best right now. Or, uh, uh, if you're willing to do APIs for closed models, uh, the, the, the American-based ones, uh, are still leading in that space. So that's just a, a general shakeout of where things are at this moment.

Yeah.

Yeah. Okay. So model, open weight, closed. What about, what about the agent and or the agent harness, Chris? What, what are we, what are we talking about there?

Yeah, we're like, if you, you know, just we've used this before. If you think about the model, that function that's trying to do something fairly sophisticated and its output is kind of being the brain, then historically we've had these chatbots and there wasn't a lot. It was more of the Oracle was sitting there and you gave it some input and it gave you some output. And we weren't, you know, it was still like, okay, I'm having a conversation. But the big change in the last year has been agentics. And that's where you have one or more agents that are— that is a type of software that is tasked to go do something out in the world. It might be on your system, it might be in robotics, it could be almost anything that you want it to be. And you can have one or more that are working together and they are managed by that agentic harness that you referenced, which is a software system I've heard it described as an operating system for AI, where it is accessing the models that you're using, it is managing the agents and orchestrating what they are doing and how they're working on that. And so it kind of gives you a body of software that you're able to use to get productivity done, to get productive tasks done. And so, agents are smaller bits of that that are in the harness, but it's all just software, including the model at the end of the day. And that's important to keep. It can, some of these can require sophisticated hardware, but at the end of the day, the model, the agent, the harness, all these are just, you know, different types of software that work together.

Yeah. Maybe one way to think about it is you have that one model, which is a function, an input output, and the amount of code and software surrounding that to do various things can vary, right? So initially, in the very early, or in the earlier days, and earlier days, I'm meaning, you know, a couple years ago or, or whenever it was, we, we had software that was basically just a chat interface, a thin chat interface. And to your point, there wasn't a lot of business logic between that chat interface and the model. Basically what you put into the chat interface, that was almost exactly what went into the model, and what came out of the model was almost exactly what came back to you. Now the amount of business logic that you can apply on top of that model can vary widely, right? And the, but that would be kind of like AI embedded in your software. I think the distinction of the agent, which is very interesting, is what I would consider an AI application or an AI feature within your software is really something that is meant to have a one-to-one interaction with you as a user of that software. So. A chat interface. I go in and I type something in and I expect a response back, and then I type something in, it's turn-based. Or maybe there's things like in financial software where you go in and there's an AI model that makes a prediction for a future, you know, cash flow variance or something like that. Right? And so those are AI features. What I think is different about the agent, again, it to your point, is not fundamentally different in terms of how you— it's still software that surrounds the model, right? But it, the, what the software is meant to do is to accomplish a goal and in many cases to operate with autonomy. And that's where I think the agent is different. So it's tied into systems that are within your company or within your context. So if it's a personal agent, maybe it's tied into your email, it's tied into your WhatsApp, it's tied into tied into your messaging interfaces, it's tied into your calendar, right? If it's a company agent, maybe it's tied into your NetSuite and it's tied into your Workday and it's tied into your transactional databases, et cetera. And you express an outcome to that agent and you, or encode a goal with that agent, and it operates at least with some level of autonomy without a back and forth with a human. To accomplish that outcome or, or maintain that goal or accomplish that goal. I, I think that's, that's really a, a key differentiator when it comes to an AI application or an AI feature versus an agent. It's more important than ever for businesses to move super fast with updates to their website, to their landing pages, to their SEO to their AEO content that is surfacing things in answer engines. And it's just not sustainable when a new landing page or a website update turns into a pile of tickets and handoffs. Our, our partner Framer actually makes that process move so much faster. Teams can collaborate in real time, iterate on the same pages, and publish in recently. Thousands of businesses already use Framer as a pro website builder, and I'd really encourage you to check them out. You can learn more and get more out of your site from a Framer specialist, or get started building free today at framer.com/practicalai for 30% off a Framer Pro annual plan. That's framer.com/practicalai. Practicalai for 30% off. framer.com/practicalai. Rules and restrictions may apply. Well, Chris, uh, that was, I think, a great, a great set of— hopefully, hopefully we didn't muddy the water with any, with any of our definitions of things. But I think that was, that was some good context setting when it comes to some of the terms that we're about to, to use. Did, did we miss anything? And what, what is your thought? What, what kind of kinds of questions have been on your mind recently in terms of clarification?

Yeah, I think you set the stage really well. And I think, um, in terms of like that connection that the agent has to kind of real-world things that, that a person's company, or it could be in their house, it doesn't have to be a business. There's all sorts of different contexts here. But, but, you know, you're, you're giving those agents access to things that matter is the bottom line, and you're giving them tasks to do on how to do that. And you were kind of addressing single agent moment, but the next level above that is to start having multiple agents that each have purposes, you know, to a task level, and then they are starting to interact. And when you get to that level, you really start that's where the autonomy of what agents are, the potential of what agents can do is really realized because instead of you as a human being the recipient of each thing there that the agent is doing and it's really almost an assistant to you, you can actually assign agents to go out and work together to accomplish multiple tasks in a complex environment and where different agents have different purposes and expertise. And so they are gonna be able to, to kind of focus with that. And then after that, they're going to be able to interact with each other and get this work done.

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So that was a great explanation of getting us kind of, you know, from basic models into kind of the world of human and model of chatbot and into the world of agents and what's different there if you introduce the agent into the process. After that, you can start multiplying out, you know, these resources. So you, by virtue of having that agent, you have the harness that the agent is working in. It might be fairly trivial if you're just barely getting into it, but you can actually start expanding those and looking at what different types of more complicated tasks you want to address, where you have not just one agent, but you might have multiple agents that are each addressing different kind of specialties, if you will, with different— and potentially each of those agents has access to different resources within maybe a larger enterprise where not one agent can go everywhere and do everything. But they have to start interacting to get job done. And you mentioned prior to the break that, you know, this, that as you get agents, you start moving into some level of autonomy and permissions in terms of what they can do. And so as you get multiple agents and you start deciding what agent roles are going to be and how those agents should interact and what they have access to, then you can really start getting a lot of productive work done through your group of agents at this point that is largely autonomous. And you can give the system instructions that have specific outcomes that you're looking for. And you can either, you can encode those into that and they're gonna go and work within the parameters, as we've learned with some of the security breaches lately. Within the parameters that you set, assuming that you set them effectively enough at this point, or you have agents doing that for you, then they're going to go do a certain number of tasks that are— that you've identified as productive with specific outcomes tied to those. And you can get lots of, of, of, you know, really productive work done this way. And this is kind of where things are right now. The one other thing I'll throw in before throwing it back over to you, Daniel, is that along with multiple agents, people are starting to recognize that different types of models can contribute. You can have larger models for some things, smaller models for others. Some are more specialized, some are more general, and depending on what you need to get done, you can start having an architecture with multiple models and many agents doing this. What's your experience so far in this realm?

I, well, I, I might have a follow-up question for you cuz I know you obviously spend a lot of time thinking about autonomy and even, you know, fleets or, or swarms of, of agents or autonomous entities. But I, I'm wondering, people may be very familiar with the, you know, some listeners may have used AI systems a lot where there, there was that turn-based thing like, oh, I'm gonna always have my ChatGPT tab up or my Claude tab or, you know, Claude code. And there's a back and forth going on with those things. What could you, do you have any examples in your mind of things that might be impossible in that mode for like a single agent, maybe interacting with a single human, but that would be possible for a fleet or a swarm of agents that's operating, you know, more autonomously in the background. Do you have any examples that come to mind or things that you can think of that people could grasp onto concretely?

Yeah, I'll try to do that. A little tricky because of the space that I work in, but I'm going to try to genericize this a little bit. You mentioned the notion of fleets and swarms of agents, and those are actually two different things. Agents. A swarm is usually a fleet, but a fleet is not necessarily a swarm. Um, and I don't want to get us off into an autonomy conversation about what—

there's a changelog episode about, uh, about this, so maybe we'll link that in the show notes.

We could do that. So, um, so when you have a lot of agents and you're, and you're, uh, you are right, you're giving them each certain capabilities and access to certain things, then you can start thinking about situations that you might have been afraid to use systems in before and places where you definitely want a person controlling everything because of the time sensitivity or care that they have to bring to bear. And so, we talked about cybersecurity in last week's episode. And that's a good place where if you're under an attack and your organization is needing to defend, it might very well be there are many agents as we saw in the, in the last episode that were involved, but your defense against that as well might be many agents that are operating either as a bunch of kind of individuals with some crossover or a swarm where there's a lot of crossover communication and task sharing between them. Because your humans that suddenly your systems are under attack, maybe it starts with a denial of service or something that you notice, But then you notice that there are breaches in different systems and it's happening very rapidly in 2026 compared to even 2025. And so at this point, um, it's very difficult for the humans to keep up with that, as we talked about last week. And so you can have agents assigned to operate your cybersecurity defenses in different aspects of your organization and each one to be responsible for different parts of that so that as breaches occur, you have different agents responsible for that. So that is, that is kind of a broad use case, uh, for the, because everyone needs some level of cyber in today's world. And if you're not addressing cyber, you're going to feel pain at some point. So that's, that's something that goes across industries. Uh, another quick example, uh, without diving too far into it is that, um, though most people are not used to pervasive robotics everywhere now, at least, at least in most Western countries they're not. Um, that's— we're right on the cusp of that. And as, and as production, uh, capabilities are getting less and less expensive to do it, and the, and the capabilities of what different types of robots and drones can do, we're seeing more of that. And I mean, case in point, you know, Walmart advertises drone delivery, as does Amazon. And, you know, we're seeing more of those. You'll start to see robots popping up around. I know you have already done that, uh, near your house. I remember talking about that, and this will happen more and more. And agents, uh, in robotics are incredibly useful because there's a lot of things that have to happen, both from a functional standpoint, from, uh, what I would call a mission standpoint, but you could call it an objective standpoint, and from a safety standpoint in terms of not accidentally doing harm out there in the world, which is easy because you have a physical device that's doing something and interacting with real-world things. Including people. And so there's a whole set of considerations and having agents that are designed to handle those tasks and having the resources on board so that they don't lose contact with it. Doesn't— they don't have to worry about losing contact with a cloud provider or something is, is, is becoming increasingly important and we'll see more and more of that. So these are all, you know, one is kind of out in the world with us and one is cyber, but there's a lot of places that I'll bet people can find in their own industry where, where the necessities may be real-time or near real-time, or just such complexity that having lots of agents interoperating together is gonna become necessary.

Yeah. And I think you can start to think through some of those, those flows in your own business as well. Like if you think in manufacturing, for example, you may get notifications from some of your suppliers that then create a trigger for a first agent to actually process that notification and realize, oh, we've got a vendor problem here. And then maybe there's another agent that does research for, you know, vendors to replace your raw materials that are now at risk within your supply chain, and that's automatically triggered and just happens. And then that generates something that is then output into orders or draft information that goes into NetSuite and interacts with that system. And all of these things could be happening in the background. One of the things that I'm experimenting with, Chris, and I forget if I— our listeners will forgive me if I'm repeating an example that I already used, but I've been experimenting with this, with this example in workshops and discussions that I've been having. Over the last, over the last few years, I've got into, to motor racing quite a bit. Not myself, uh, doing any of it, but just as a fan. And I remember always throughout my career, everyone would use the example of the F1 pit stop as in kind of like leadership discussions and team discussions about how a team should work together well. Right. And, and often how that would go is like, you'd look at how the pit stop evolved over time from like, whatever, 20 seconds down to 2 seconds. And then you realize, well, the, the way that they're able to do that is they literally have a single person do a very specific task and nothing else, like move the tire from here to here. And that's all, that's all they do. And everybody coordinates well with one another. And it used to be in those leadership or team building discussions, they would talk about, you know, how the team could work together well when it was people. Now I think what's interesting is every individual human working in a business, like me as a CEO or you as a research engineer, whoever that is, we're actually elevated from doing a single task really well in our position to, we're actually elevated more to the team principal or strategist level of the F1 team. And now each person can actually have a high-performing team of agents under them. Each of which do various tasks very well. And you're now thinking more at the strategic or outcome-based level, like that team principal, like that strategist, right? So I think actually, if you think about it this way, it does a couple things. One is it kind of empowers people to maybe think about all of those elements in their job where they might have this team of agents to help them and actually operate on their behalf. It helps you understand maybe like the level at which you should be thinking. And I think it actually elevates the human in the situation because now they're able to actually apply their domain knowledge, their strategy, their, their kind of outcome-based thinking towards, towards the goals that matter for the, for the business. Now that's the more positive side of things. Obviously you could argue, well, not everybody's going to be operating at that strategy outcome level, then how many like strategists do you need and do you need less humans and what is everybody's job? And there's a whole series of questions that could come from that, right? But I find it at least to be a useful, you know, a useful tool to think about how this might operate in terms of your immediate surroundings in your business.

That's right. And it's important to recognize that This is, you know, one of the reasons, like I said at the beginning of the episode, that we're doing some of these episodes addressing some of these are this is happening really fast in terms of the availability and but so is the understanding that we really want to bring all people along so everyone can benefit from this capability. But this is changing. This is changing, you know, global economics gradually. And so it's not something— it can seem very esoteric to a lot of people who aren't familiar with where agentics are today. If I sit down with my extended family at a family outing and try to have that, and they're like aware that something's happening, but they don't know it super well. And so we'll, we'll talk a little bit about that. But this is the kind of thing you really want to get on top of because it's moving quickly and it's creating huge competitive advantage in some organizations, while others it's diminishing rapidly. So I just wanted to draw the importance of kind of, of kind of grabbing what the agentic economy looks like as early as possible.

As you're listening to this episode, you can tell that there's a lot to think about. There's a lot to know when you're scaling up your digital workforce of agents. I'm leading a company called Prediction Guard, which provides an AI control plane to help you actually scale your digital workforce in a governed, controlled way. We help you look at your supply chain, including models, MCP servers, making sure that you know what's in your supply chain, that you can track that. We institute runtime governance over all agent interactions, controlling that behavior as agents operating in your environment, and we provide observability around what those agents are doing and any policies that they're violating. So you can be sure that your agents are under control and being scaled in a way that's governed. Please check us out at predictionguard.com and book a call with myself and my team this week to talk through how you can scale your digital workforce while applying zero trust and maintaining control. That's predictionguard.com. Well, Chris, I, of course, you know, it's probably no secret to our listeners. We're always using AI to help us do these interviews or do research or whatever. One of the things that I did in prepping for this conversation, Chris, was just get a few prompts and maybe questions that people are wrestling with right now for us to consider. And so I don't know how many of these we'll get through, but I thought that they were interesting prompts to, to us.

Okay.

Which, uh, one of, one of them, which I thought was interesting is, is multi-agent architecture. So these fleets, swarms, whatever you consider, is that really the future or are we just compensating for limitations that better models will remove? Meaning models seemingly will get better. So do we really need multi-agent systems or is the better model just gonna be able to solve those problems?

So I think it's an apples and oranges thing and, and that is, you know, we, we've talked about a third route. Yeah, it's, there you go. So I think there will be, there will be models will get better and there may be some models that can do quite a bit for you without deploying multiple models in your system. Depends on the nature of the system. Obviously, there will be other systems, especially, you know, as you're, as you're moving away from where you can host giant frontier models of the future to where your, your resources are more limited, where I think you're going to be dealing with lots of smaller models that are more specific on tasks. And at the end of the day, though, you're still talking about those are functions to your— at the beginning of the episode, you gave us a great way of looking about understanding what models are. And at the end of the day, those are functions and they got to do stuff. And so I personally, uh, am quite convinced that, uh, multi-agent architectures, uh, not only are where things are going, I think they will be absolutely and completely pervasive in the future, to the point where, uh, we won't want to talk about them at all because they'll just be so built into the fabric. And so I'm actually actively writing a book on this topic. So among other things. So yes, we are going into a world where this is not going away. I'll leave it there.

Yeah, I think that's a great perspective. And another prompt here, well, another one I found interesting is, do we think we're moving towards a future of more interchangeable models in these agents, or are we going to see a future where most things are very vertically integrated agent stacks. And just to kind of highlight for people maybe that don't, aren't, aren't as familiar with some of these dynamics happening right now, there's one side of things which maybe is, lives in the world of sometimes more open, not always open though, open source agent harnesses, but things like, for example, LangGraph or Pydantic agents or Hermes agent or OpenClaw, et cetera, et cetera. Name, name your favorite one, uh, framework or agent harness. And within these frameworks or agent harnesses, you can interchange the model, like the brain, as, as you mentioned. So that could be a closed brain model, it could be an open one, you could use multiple, uh, et cetera, et cetera. But there, there's a distinction there between that harness and framework layer and the, the model layer. Then, then you can look at other stacks, like maybe you look more towards, uh, inbuilt agent features in Anthropic or OpenAI's platform, but also others, whether it's like IBM Watson agent thingy, I, I forget what it's called, or you know, the hyperscalers, uh, AWS has an agent core thing, I think, like others. These are very opinionated approaches, right? To say, hey, we're going to do all the different things of the stack. We're going to do them in an opinionated way. We're vertically integrated. We maybe even have the compute. We have the models. We have the agent harness. We have the, the interface. We have the console, just like take everything in our vertical stack. Right. So we, we do see the both of those dynamics. Very clearly in the market right now. So what does that prompt in your mind?

Well, I think we're going to see all of them because we are already seeing all of them. And I don't think that this is a winner or loser side. I don't think one falls away. I think that they fill different needs for different purposes. And in my own life, I use all of the above. There are some It really depends on what my need is and what is the sensible architecture and strategy around what it is I'm building. And some of that is using closed frontier models that you would find at, you know, whether it's the Gemini and the Claude and the, you know, and the OpenAI models. And there's a place for them. And then others Uh, you know, I might be going for the Gemmas and the other smaller models that, that where you can run inference on a device, for instance, uh, or, or maybe an application that just doesn't need— I mean, why spend the money on, on closed model APIs when you can, uh, you can do a lot of what they do, uh, at much, much, much reduced costs and host it yourself and get the economics, uh, on your side? So it's an interesting question that you pose because yes, all of them are going to happen, but there's such an economic incentive for all the different versions depending on what it is that you need. And I think what we're starting to see now in recent days is really a drive. Like we saw the explosion of usage. We were talking about this a few months ago on the show about just token maxing and that is already like it came And it went because suddenly the cost of that caught up to people. So now what we're seeing is we're seeing people going, how do we get there at the most affordable cost for what it is that we need to accomplish? Which I think is a lot more sensible. I'm glad we're past the token maxing moment into more of a sensible, like, let's think about this architecturally and strategically going forward on what we need to do. So yes, all of the above with a sensible planning process. Guiding you.

Yeah. And the, you know, we, when we have agents that could be operating autonomously for hours or days or months, obviously then that makes a totally different impact to your, your cost of operating that type of system than just a chat back and forth, right? So that, that's certainly a part of it. I, I do actually think, you know, you mentioned that maybe both of these will exist, but for different reasons. In my Bottom line, just to draw out one of those reasons right now would be if you look at, you know, most businesses utilize some sort of drive workspace document ecosystem, right? Mostly Microsoft or Google, although there's others, right? So if you're primarily using Google Drive and Google Docs, then you're automatically going to get one of these very opinionated AI integrations across those various apps in a way that is incredibly integrated into that, and that's Gemini. But you would not be able to use that opinionated Gemini stack within Microsoft 360 and Documents and SharePoint, et cetera, because the one that is integrated in a very opinionated way there is, you know, Copilot, et cetera, across Microsoft applications. And so there's this suite of applications and obviously they have their opinionated, very tightly integrated way of doing those things. However, whether you're a Google shop or a Microsoft shop, if you're creating this, what I think of as your digital workforce of agents, right? You're not going to have that digital workforce of agents be run by the little widget in your Google Doc Gemini widget, right? And there's a whole variety of reasons that you wouldn't want to do that. One, one of them being, you know, just maybe cost and the, the how much it would take to build that out. But I think to use a different, I'm trying to think through all these examples right now, 'cause I love, I do love teaching and I'm trying to think of better examples as I teach more of this agent stuff. But I think an example that I could use here is that may connect with some people is that, most businesses or many businesses at some point in the life of their business engage with a consulting firm, right? Whether that's the big consulting firms like a McKinsey or an Accenture or something like that, or mid-size or smaller or whatever. Sure. Those firms have highly, highly opinionated takes on how to do things. They have their own systems, right? If you engage with them, they're going to do things in a very specific way. To accomplish your goals. But most businesses do not say we're going to staff our entire business and workforce through McKinsey. One reason is no one would have that much money to do that because it would be an ungodly amount of money. And two, like what is your worth as a business? Like what is your, what value are you providing other than the business entity? And that's where you can really build up the value of your business and your agility as a business over time, both from an IP perspective, but also your control, your flexibility, your ability to pivot and under economic pressures or under marketplace pressures or pivots or that sort of thing. You want to have control of that, of that main part of your workforce, right? And I, so I think that control. Longevity element of being able to build agents within your digital workforce in a way that is definitely more vendor agnostic, less tied into the strict opinion of a vertically integrated agent stack, I think does have advantages. Now, will everybody be able to do that or should everybody do that? I'm not necessarily saying that, but I, I'm, that's one example that I've been trying to think through as I'm, as I'm thinking of what, what some of these dynamics are.

I think that's a great insight that you had, and that is that, you know, that the having specific vertical stacks, because those are already, you know, developing. You named a couple of them. There are more out there that and they own a certain amount of the business. But I think the realities of business is that the ability to move across those verticals as well as into your own domain of your own you know, the models and agents that you are managing on behalf of your business that are very specific to your business is a reality that everyone's facing at this point. And every aspect of business is going to have both the different types of models and the different types of agents having to do those interactions. So I think your point is really well-founded that you can't think about just going and getting like, I'm going to be an all-in on Google, or I'm going to be all-in on Microsoft or some stack like that, because I don't think in most cases it's going to meet your full need. I'm sure those companies will disagree with that, but the realities of life are too complex. And so the key is to be— I think, I think this is the role over the next few years. You know, there's a lot of people out there on LinkedIn calling themselves AI strategists and such. And if you fancy yourself with that title, then you've gotta figure out how to apply these different resources to the needs of the business given this very complex ecosystem. And so I think that is where things are going and I think it's messy.

Yeah.

Yeah.

Well, I do think that, well, life is messy, so it's Everything that we deal with in the future is going to be messy, I'm sure. But there is some, there's definitely some patterns emerging that are helpful for people. I hope we've been able to highlight a couple of those today or even just clarify some terms as we, as we kind of get towards the end here, Chris, I think one question that might be interesting to just wrap us up here is how, how should people evaluate their agentic AI efforts as they dig in and try to accomplish things with agents? Is that, is that by, you know, uh, how do they, how do they measure, measure success maybe in terms of a successful agentic AI adoption or that sort of thing? Any, any thoughts?

I, I would start small, uh, with, with the expectation of rapid iteration, um, and, and just experiment your way into what's working. Be thoughtful about, you know, as we've talked about all these different options that are out there, be thoughtful about what makes sense. I see a lot of companies that are tied very tightly into specific vendors or vertical integrations, and all it takes is one of those vertical integrations to change something or to take something that may be a high-value thing that your company does, and suddenly they provide that and it can kind of crash a whole business. So like there's, there are times when looking at verticals might make sense, but maybe you should be thinking about how you can do it in a different, non, you know, not bound to a particular vendor so that your business survives as people are negotiating these things. So start small, experiment your way, and have a backup plan and maybe several backup plans as you're experimenting so that when you, 'cause you will run into problems and you will run into limitations and you'll run into models I run into models all the time on the Frontier side that won't let me do things I want to do because I'm trying to do novel things a lot of the time and I hit guardrails all over the place. And so when I'm doing that and I'll sit— the other night I was sitting here at home working on a side project of mine and that happened and I'm like, okay, well, Fable won't help me. And I started downgrading. Have backup plans, figure out what will work for what you're trying to accomplish. And see.

Yeah, great, great way to close it out. Appreciate your insights, Chris. And it was a fun discussion today. I hope everyone listening engages with us throughout the, throughout the week until next episode. We are on YouTube now along with all the other socials. So go check us out. You may or may not want to know what we look like, but you can now if you want to. And so, yeah, Yeah, we'll, we'll talk to you again soon, Chris. Have a good one.

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