A Blueprint for Enterprise Agent Adoption

Today on the AI Daily Brief, a blueprint for enterprise AI adoption. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show Notes. Hello friends. Today we once again have a slightly different type of episode, but one that I'm really excited about. It is undeniable that the biggest theme this year for most enterprises is, or at least the most exciting theme to most enterprises is agents. I have a whole slew of theories around why I think agents have businesses thinking differently, even more than perhaps some Genai assistant type tools have. But in this conversation, I'm joined by Swami Chandrasekharan, the head of the USAI center for Excellence for kpmg. Rather than just a general overview of agents, this conversation comprises a part one of something of a blueprint for thinking about enterprise agent adoption, or at least testing. Swami shares his Taco framework, thinking about different types of agents broken down as taskers, automators, collaborators. We discuss the most common challenges that he's seeing among enterprises trying to adopt agents. And ultimately we try to provide some positive steps that you can take as an enterprise to advance your agent strategy. We certainly don't get through an entire blueprint for an agent strategy. We will have to have Swami back to keep going on that. As you'll see, Swami is definitely not your standard consultant. He has a deep technology background, working previously as an executive architect at IBM's Watson, among other roles. He holds more than 30 patents and has authored multiple books and articles on applied AI. And so, without any further ado, let's dive into this conversation. All right, Swami, welcome to the AI Daily Brief. How are you doing, sir?

Doing good, Nathaniel. Thanks for having me over. Big fan of your show.

Appreciate it. Yeah, we were just joking. So up until about, I don't know, 24, 48 hours ago, we were talking about the hottest topic in AI. I think a very brief moment that's been displaced maybe by, by deep seek and R1, but broadly speaking, I think, I think this conversation about agents still is pretty, pretty down the middle of where a lot of people are thinking.

Maybe.

Before we get into it though, I would love for you to just share a little bit about what you spend your days doing. And you know that that gives context for, for, for this conversation.

So I live in Dallas. I'm a partner at kpmg. I lead the AR and data labs for the firm. And what that actually means is as part of the large transformation program we're running called AIQ that Steve Chase runs the Data AI and data labs is a pretty significant part. The way I explain my job to my 13 year old is I do three things. When I say I, it's me and my team. We do a lot of experimentation. So for lack of better word, we don't have a full fledged R and D function at the firm. So we do a lot of experiments, innovation R and D around things that don't exist today but will exist tomorrow. Whether it is around how do we use language models or how do we build advanced RAG knowledge assistant techniques or even agentic frameworks or how do we evaluate these models. The second part of what I do is I help establish standardization when it comes to technology architecture and patterns for AI across the firm so we don't do the same thing five times. And the third part of is given my history in being in the advisory side of kpmg, I work with a lot of folks in co incubating new things for our clients. So I get closer to clients and understand problems. So I don't get too disconnected from what I do. So nutshell, I have I think the best job in the firm and a lot of fun and a lot of responsibilities as well.

Awesome. So perfect setup. I think a lot of the conversation today is going to be about the practical, factual, kind of where we are with agents. And understanding where you're sitting, especially relative to clients, is useful. Let's actually start there with that question. When you think about 2025 as it relates to agents, they are obviously a key theme. They're on everyone's mind. But where are we actually when it comes to agent adoption, particularly in the enterprise?

Right.

What stage are we at? And let's start there. And there's a lot of branching questions that I have from there as well.

Yeah, Let me quickly set the context. No pun intended. Right. But to get to every agents. So when large language models came out, we started interacting with it with prompts, ungrounded interactions. We loved it. And then we slowly started to bring in more context through longer prompts, few short prompting and so forth. Then thanks to Meta, we have this approach with retrieval augmented generation where we said, look, why don't I intercept the prompts and go to a corpus, bring back the relevant chunks and give it to the model. So we got our arms and ourselves wrapped around. Okay, now I understand the concept of RAG or what we call knowledge assistance in kpmg. But still with both of these paradigms, you were sitting and typing prompts, you are away, you're Doing it. You may end up doing LangChain, type chaining and those kind of things, but you're still typing props. Where is the action? So agents come agents. The whole concept is, can I have these machines go given a larger goal, can these machines go figure out and plan and go take actions? So whether it is researching on a topic or whether it is reconciling a balance sheet against my ERP systems, it's now starting to do things. So what fundamentally makes agents are how well you define your instructions, your goals expressed as instructions, long form prompts, how well those prompts are reasoned and understood through a planner into tasks that you have to perform, and to perform the task, what tools I need to do the job. Then there are things like knowledge, memory and context and a whole bunch of other things. So fundamentally it is giving the large language models not only additional tools, but the ability to do reasoning in the context of a goal or adjacent set of goals you're trying to achieve. Okay, Swami, you gave a very good theoretical definition. What does it mean? If you now look at what is possible today, all the things I've been explaining are possible in a way using frameworks like LangChain and Llama Index and others where you can deterministically train those steps. For example, if I want to reconcile a balance sheet, I may have two big functions. Each function may have a long form instruction. I make that execution of function 1 in Python, give that output to the second function and I can achieve it. There's nothing truly agentic about it because you are hard coding the steps. The true agent behavior is going to be where I express, for example, balance sheet reconciliation. What do I do as an expert? I say a balance sheet will have these following fields. I look for the following parts in the balance sheet input. Then I go to an ERP system and I do certain things. So you are expressing that as how a human expert would express. The question now comes, can any large language model even reason and understand what you're saying? Probably till like 6 months ago, or maybe a little before that, they were not. It was very hard for them. Every iteration of the language models that came out from all the big tech, the reasoning capabilities and more importantly, longer instructions, longer prompts, they begin to do pretty well. Even if you go back to three, two, three years ago, these longer instructions were impossible to achieve. Right now you can do it. So what you have the ability is better reasoning, better understanding of what you're saying through these long form instructions that are very critical for agents. That was not possible in the past, what does that leave with us? You can understand instructions well, you can break them down into tasks. Probably it now then comes to, are those tasks that are broken down and the tools that are used for those tasks, are they reliable enough for you? The ants, the juries out there, the jury's out there. In terms of the tools and platforms we've tried and worked with, it requires a bit of handholding. While the language models can reason the act of turning that into a set of tasks, instructions into go execute, it's getting there, it's getting better. But long story short, what we can do today is simple agents. I have come up with a simpler definition or a simple four ways to define the types of agents. You can do acronyms, taco taskers, automators, collaborators and orchestrators, which is multi agent orchestration. And one thing about TACO is people differentiate between. I've heard people talk about, oh, certain agents don't get to access all tools. My thing is, in the taco framework, all the Categories of agents, 4 types of agents, are going to get access to the same knowledge corpus. It's going to get access to the same breadth and depth of tools that the agents would need to create actions. It will have access to memory, it'll have access to the same algorithms. So all of those four are fixed. So what is different? The difference between the four comes down to planning and orchestration. The teen taco taskers, they're singular goals, one goal, but can break down to multiple tasks. They can be chained, easy to manage, easy to test, easy to roll out. When you go to automators, which is the next, they typically go to cross system, cross application. So these are end to end processes, order to cash, lead to cash, procure to pay, hire to retire. They touch multiple applications and multiple systems. So the goal may be similar, meaning ensure streamlined order to cache process execution. But they break down into sub goals. Each of the sub goals may touch different applications and different systems. So it gets a bit complex in terms of the scope of what it does. Gets complicated. Orchestration gets complicated. In the orchestration, you have to manage state and all these things. The third part is collaborators. This is where I've been pondering over the question. So there's this concept of can AI be used as teammates, agents be used as teammates. They're no longer you telling the agent to do something, it comes back, you work with it. It's like how you work with your team member on a daily basis. There is more skewness towards human collaboration, partnering with the Machine to get things done. It's there in the other forms of agent, but it is even more so in this. It's just predominantly built there. The last bit o in the taco is the multi agent where I have agents calling other agents. There is interagent collaboration. Of course the complexity becomes more with all of this. Like I said earlier, where are we today? I think there have been a lot of experiments, prototyping done with the taskers. People have been inherently because there are quite a few platforms, open source, commercial included, where you can build them quickly. We can talk about that. But I think those are in the year of agents. If 25 days I would see more taskers. That's my prediction.

Do you think it's obviously very dangerous to sort of prescribe one right path without the context of any given organization, but do you think that that that toggle framework actually is basically. Are they four separate categories only or they do they have some sort of linear relationship with one another? As you're thinking about adoption, if you're sitting in an enterprise where, you know it makes sense to start with taskers and then move to the next or you know, how do you think about that?

Yeah, this is not. I don't want this to be a contrived framework where we retrofit everything into one of these four. The framework is meant for a mental model mental picture. Look, how can I break down agents, not everything. Because the reason for this was everybody jumped into multi agent coordination without even thinking about the basics. So that is one second is more than likely. When you go talk to clients, they're going to talk about scenarios which will not only overlap but will require their focus may be more than likely starting with okay, let me do end to end process automation automators because that's where I need. I want to streamline my store performance management or I want to streamline my procure to pay process or when you go to another client may say look, I'm more focused on augmenting my human potential, so give me an AI agent that can act like a teammate for my ap ar, erp, finance kind of process domains. So yes, it is dangerous to put things into the bucket, but that's not the point. The point here is to demystify the whole agent ecosystem and how complex it becomes. And if you start to amalgamate and combine, that's okay, but at least you understood the individual's beatbox.

Yeah, it's interesting, you know, I think that one of the things that makes agent adoption fascinating as compared to for example, sort of broader Gen AI adoption over the last couple of years, enterprises moved very quickly relative to previous, you know, technology changes to grab onto Gen AI and try to sort of harness it. Now obviously there's still tons of organizations that are behind, feel behind very few organizations. I think we tend to find that the organizations who are the farthest ahead also have the greatest awareness of how much more they still have to do when it comes to adoption. So it's not like they're, they're sort of, you know, at the end state or anything. But I do think that because they've been watching agents come down the pipeline for a little while, they maybe have a stronger sense in general of how they want to eventually use agents. They to want the possibilities that have them most excited. And I think that, you know, it might be leading to some of what you're seeing around. They're jumping to sort of exactly what they would like out of an ideal state of what an agent can do. They're imagining even ahead of where the technology is rather than sort of just racing to catch up with what it can do now, which, which you know, can create challenges just based on, you know, what's, what's actually ready for primetime and what's not at this exact moment.

Yeah, everybody has a, an expectation and a notion of what this agent should be for them. If you go look in the customer service and marketing function, they say, my version of an agent is, can I put a, can I put a digital version of a customer or a software development or sales development representative? And it can talk to clients, it can negotiate, it can ask the questions, it can help close a sale and get paid a commission and come on. So they start thinking it like synthetic employees. You go into the enterprise, you go into the mid and back office functions. They think in terms of processes. There is a particular way in which I receive, review, approve or deny invoices as part of my larger procure to pay end to end process. So I have a conception of how agents should be in that particular ways. It is not one size fits all like you said. But at the same time, the key responsibility is when you go talk about them. You're not trying to take an existing technology and retrofit and say, oh, I have agents. So as an example, one belief I have is good old business process engineering. Like how you sat and designed business processes for end to end processes. It took a particular approach. Process engineering came out where he said decompose your domain, break them down into level 1s through level N could go up to level 7 and 8 where you kind of have a massive swim lane view of how your process looks like. That's how we represented processes. That's not how machines think. Now with the reasoning capabilities, I could express that same thing, almost like a long form instruction and you leave it to the machine to say, look, you go to find the process, the steps that are needed to execute it. So there is also a change in how we approach designing the agents. That is also essential and important. Over here, the outcome is the same. I want a better, efficient, leaner process, but you're approaching it in a different way. So the point being, the entry point for agents are different. They're all going to converge at some point in time. But given where we are in the stage we are, the expectations are widely different.

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Yeah, you, you kind of gave out three out of the things I was, I was going to say anyway. So first of all, why agents? I start with that question, what is the rationale? What is the motivation for agents? So first define, don't go to technology called agents yet. What is the problem you're always trying to solve. So if I'm a client, if they come and say, if they're a retailer, they come and say, you know what? I want better top line growth, increase in my stores, in my brick and mortar stores. Okay, what are you doing today? They say, okay, I have these things. But stores, the sales get affected because certain stores don't follow certain kind of policies and procedures. They don't take into account customer satisfaction or customer reviews and all those kind of things. Okay, Then we go in and say, okay, the goal and objective is to have a better, more tangential approach to how do you do store performance analysis so you can improve the performance and increase your top line and rework. So number one is what I trying to do and is agency even the right answer? So let's assume you've gone down the path of saying, look, I want to optimize my processes, reimagine my processes, at the same time optimizing my human resources. Then you talk about, okay, where is the data coming from? Do you have the data? Do you have access to all of the data? Have you even first of all instrumented the data if it has to be digitized, and is that data made is clean? And all the good things about data availability and readiness and everything. The third one is, I don't think you mentioned in the list, Nathaniel, which is, who is the human expert who can articulate what is happening today and what needs to change? How are we going to elicit that knowledge? Whether you pick a domain, you pick any intense domain, even if it is something as simple as customer service from the point a customer comes and raises a request for refund, what do you do? What is the process you follow? And what is the way to reimagine from that point onwards using agentic concepts? So human expertise is still needed to articulate. I mean, there are theories floating around. Can I go do simulation? Can I look at what humans being doing, learn from that? Yeah, you can, but they're not fully reliable yet. So why agents, data, human expertise, articulating the whole thinking process and how agents have to be built, then getting into policy that thinks, okay, are there things you want, how much of autonomy you want to give to these agents? It could be at a very broad stroke, principle level, saying, look, I don't want any decisions that have a financial implication to be approved without human in the loop. Maybe I want three stages of human in the loop. So there is a whole strategy around how do you bring in humans, where do you bring them in? Where is the level of oversight? What is the kill switch equivalent? What agents look like? What if you want to stop agents for a day? What is your fallback mechanism in case these don't start to work? So all of those policy trust, security, reliability aspects is one big bucket. The fourth important bucket is everybody. And this is a very opinionated topic I've seen with clients, is how are you going to build agents? Okay, everything fine. You got the data, got the experts, you got policies, you know how to build Them, where are you going to go build them? So today there are dime a dozen open source frameworks, the big tech, small tech startups, they all have their platform. So where do you go standardize and build? Again, my thought process there is till this whole thing settles down, you may have to remain polyglot and pick a few choices, be very opinionated and go build and try them out. And some are going to work, some are not. So you have to be ready for consolidation and margin. So what is the tool technology infrastructure that you're going to go to? I'm not even using because LLMs, I'm assuming they're going to get awesome. They are awesome. They're going to continue to get awesome. And the last bit is around skills. Do you have the skills to build this? And one more thing after this. Okay, you have the skills. Building agents is one thing. The day two plus operations is a completely different thing. How are you going to sustain? So we've talked about model drift and data drift. Now comes agent drift. What's the guarantee the agents are not going to drift? It's going to deviate away from what it was built for. How do you keep them, upkeep them? Is the data changing? How good of a feedback are you providing back to it for reinforcement. Those all come in the day two plus operations. So top of, top of my mind, I think these are the kind of categories of things I would look at.

And do you have. I think it's a really useful framework. How much do you think people, how much are you seeing people's first experiences being something that they're kind of rolling their own, you know, with one of these general frameworks versus trying something that's more off the shelf. I mean this is kind of only a question for the last few months as more off the shelf things have been available. But you know, working with a customer service agent or is this, does this have to do with which category of agent to use your framework they're actually thinking about?

Yeah. So if you double click into where are you building agents? I think it bro, it double clicks into three sub questions or sub areas. Are you going to build your own using open source? Are you going to take a commercial platform like a Copilot Studio or Agent Space? A third option is are you going to buy the agent? So you go to Agent Forest is going to say, okay, I already have a sales coach agent. You're just going to buy it, configure it and use it. The experience is changing by the month. What we have today is not what we had six months ago. Again, there's another one. The way I look at the whole agentic tooling space is there is low code tools like Copilot Studios and those kind of the world. Then on the far right you've got the pro code tools like the, the Langer apps and Crow AI and Autogens of the world. Then in the middle, I call them mid code, you can go back and forth, meaning I can write code, I can write in gui, drag and drop, so I can do both. Initially, people tend to go use the pro code options and they realize while it gives them a lot of flexibility, they have to end up building a lot of things on their own. So there's a lot of lines of code to write and maintain and manage. Brittleness starts to kick in. Unless you have a well coordinated engineering team, development team, you may end up recreating the same thing. For example, the same tools to do the same thing may get recreated multiple times. So there is that risk of having and you need to have a special set of skills and capabilities to do coding by yourself. Now if you come to low code, I mean I could get started quickly very easily, but I've seen roadblocks where I say, oh, I want to do this Excel comparison for one of the steps in my agent and I cannot do very deep Excel analysis because my Excel has multiple complex cells and rows and headers as an example, like I said. Right. That's why the whole polyglot approach is needed. Like you need to first decide what is my agent architecture going to look like, what are the tools that I need as of enterprise. Let's go figure out the strategy to build those tools in a reusable way. And then it doesn't matter if I'm building agents in my Pro code or on my local, they all access the same set of tools. So let's focus more on getting the task done with the same set of guidelines, principles and safety. And if you are ready to upkeep these agents from day two onwards, you make a choice. So I think the jury's out there in terms of not one platform has got everything you need. If you have something, then there is going to be something it does not give you or a friction point you get.

I don't know if I'll phrase this question right initially, but with Gen AI right now, sort of non agentic Gen AI, LLMs and assistant copilot style tools, a lot of adoption is happening, at least mediated by some central body in the enterprise that's tasked with thinking about AI transformation, right? So maybe it's a repurposed innovation group that touches all the lines of business and all the back office functions and all the things. It sort of just understands everyone's different stakes and who become the conduit for different use cases and different tools and things like that. So it's, it's top down, not in an aggressive kind of way, but in a, you know, still, still like coming through a central entity. Do you think that agentic adoption is going to mirror that? It's going to come from central groups analyzing all the different options or is this going to be a little bit more bottoms up where it's a specific department or a specific line of business or a specific area, you know, experimenting with something that's direct and purposeful for them.

You cannot stop innovation in the, in the grassroots. That's the reality. People are going to keep innovating and come up with new approaches because the role I'm in, I belong to that central organization. So fair disclosure, right? I'm providing my perspective with that sitting in that part, in that side of the world, I believe, helping standardize on the approach. The technology, the platforms, including safety that you incorporate when you're building agents will go a long way in helping folks in the departments and different business units spend their time and energy in building agents. Where I see a lot of time and energy being spent is trying to build your own agent platform or trying to make your own agent platform. This is like saying I'm trying to build my own, I'm trying to build a car. But I have four groups in the company and each one of them is building their own supply chain or the assembly line. Why even try that? Why don't we build one good efficient Model T, Toyota Tesla? You pick the best supply chain for the assembly line, including the supply chain that powers it, and you focus on Designing the Model 3 or the Toyota Camry or whatever your favorite car is. So standardizing, giving them the platform and providing the guidelines and let them bring the focus on the hard part. The hard part, like I was telling earlier, eliciting knowledge of everyday work and translating that into an agent, that takes time, that significant piece of work. So who's going to do that if everybody's focusing on I'll also build the platform and I will also build the agent.

So it sounds like a bit of a both and there's going to be functions that are relevant for kind of an org wide or at least cross functional discussion from an infrastructure perspective. In particular, while there's also a clear kind of purpose for what the individual units or groups are going to actually need and understand.

Yeah, yeah. And one other observation data point is we're already finding the individual groups heavily time constrained, meaning they don't have a lot of time to go to R and D. Pick a platform, evaluate a platform, evaluate choices. What kind of evaluations do I do on agents this versus another? They already kind of, they already have like things to go ship and build. So trying to take this as those as much as the way and have the central group help provide that guidance.

Let me go down even a level from that sort of department or functional or group level. How much are you thinking about, you know, individual level, employee level adoption and the challenges therein either when it comes to, you know, getting employee perspectives on which tasks are actually suited for automation or which things they'd like to have agentic support for as well as a question of employee attitudes and concerns around replacements and things like that. How much are you seeing that enter the discussion as companies are moving into this space?

So in one side there are tools, for example, KPMG is rolled out Microsoft M365 copilot to all of our employees in the U.S. for example, except for federal. So they have access to all of the tools, the ability to create what are called personal copilots where you can point it to your own SharePoint corpus and start to interact with it. So they can pretty much do this in a matter of a few seconds today. So there is that level of capabilities that are made available by big tech like Microsoft and made available for large corporations. The reality is they are made available. They are there. The next evolution in that is they're also going to say, okay, you can build your own agents to automate your daily tasks. So there is one theory from the big tech where they want to push their tools for more adoption, better adoption, where they're saying, look, you can build assistance agents on your own and it's going to be easy. My take is look, well, that is all good on paper, but imagine you're going to have hundreds and thousands of these agents all over the place. The kind of actions the agents are going to take, we have to carefully manage them. You don't want it to start doing things that will leak your ip, leak your knowledge, leak your data, put you at risk and be what? So one tool is people who are builders. The builders of agents will have to be certain types of people who have gone through not only skilling, training and other kinds of things, but Also understand the implications of building agents in a particular, particular way. So you're gonna, you're gonna start seeing personal agents. That is confined only what I do as work. So today in my computer I could have a shell script that can do things that is confined to what is happening in my own specific environment. Enterprise grade agents I think will take a path where it will be built by folks who have gone through a certain level of pedigree and steps. If I could say that I don't think either of them are going to stop.

Do you see a convergence of those at some point where companies start, you know, I mean one of the fascinating things about Gen AI in general is that the. It's the first time that shadow it has been while yes a concern also an area for innovation that they're actively trying to understand so they can potentially bring in.

Right.

Like you want to understand what people are using their personal Gmails for sign up for not only because you want them to not put important company data on those platforms without. Without your knowledge, but also because you might want to adopt those and given how much of a race there is to the personal assistant side of agents. Right. We're recording this just a few days after operator has come out. I can see there being a sort of a blend where enterprises start trying to adopt agents from a top down kind of way or at least sort of a unit by unit, group by group, function by function kind of way. And employees are bringing in assistants that have sort of started to automate their own personal processes at the same time.

Yeah. Since the board of operators. Let's take that as an example. I could build when operators may come available for everybody. I could build an operator that I could use for my for example my weekend planning or my, my calendar. Assuming I can log into Outlook on the web, look at my calendar and see overlapping meetings and come and tell me which ones I should consider canceling as an example. But that's me having unleashing an operator building and unleashing an operator that is happening in my personal environment space. Assuming 10 other people find about it and say that's a very good use of operator. That's a very good personal agent. Can you share that with me? So the point I'm trying to make is the personal agents. The scope of sharing is going to be limited if you keep it that way. It's not permeating across the enterprise. It's still being built on approved. This is not like somebody gone rogue and built their own agent on an approved platform. I'm still talking about approved platforms, but built personally but the scope of sharing is limited. I foresee a world where you're going to see organic innovation happening and somebody's going to crack the nut on oh, this is the most innovative use of operators or, or agents or Copilot Studio B whatnot that I think should be made available at the enterprise level. To go through that level you got to go through stage gates of testing, evaluation, safety and other things so you have proper governance in place. Because for the enterprise I see them no different than treating them as products. You're rolling out products in your enterprise. You're not just going to roll out things randomly on the fly without knowing what it is doing in your enterprise.

So I think the. I had, I had some idea coming into this what I wanted to do but what's become clear is that I think this episode will kind of stand and I'm going to frame it as almost sort of like a, an agent readiness checklist. But I think we just did part one. What I would suggest is I've maybe one wrap up question but then we should come back and do this, you know, maybe, maybe next month and do a part two where we get into maybe some more specifics around use cases and things like that. I guess until we get there. If you had one general piece of advice for the next month, you're not going to get to talk to these listeners as they're thinking about adopting agents in their companies. What's one thing you would encourage them avoiding or trying or you know, just you know, setting, setting as part of their framework to kind of maximize how they, how they, how they think about adopting enterprise agents in this year.

Yeah, one thing is always hard, but let me try. One thing I'll highly encourage is don't stop experimenting. I mean you have to do that. Only then you would understand what is right or wrong. But one thing I would highly encourage everybody to go do is talk to your respective transformation technology AI leaders. First question to ask is the things I've been talking about, what are we going to do about. If I have the next best agentic idea, where do I go build it? Where do I build it in a way that it is not throwaway work? Because that could be a rallying point for many things. Meaning what are the agents? What kind of agents are they? How do I build those agents? What data do I need to build the agents? Because I've seen everybody talk about thinking about agents, talking about agents, debating about them, but when it comes to rubber hits, the road of I need the data, I need to go build them. It becomes analysis paralysis. So we are in the mode. If we are very, if you all of us believe this is the year of agents then you should have already picked a platform. If you've not highly encouraged go think about where do you go and then everything else would follow. What are you ready? Are you, are you, are you? Do you have the skills to go build it? What else do you need to think about? They will all naturally fall.

Awesome. Well, like I said, I really do think this should be a part one and we should come back again. But I appreciate you spending some time with us today. I think it's, you know, everyone is trying to wrap their head around this particular question right now. So invaluable to have you here to talk through it.

Thank you, Natiano. Happy to come back.