A (3:16)

I would want people to walk away with a few things. Likely one would be that when we are talking about the space that we're about to which can get technical, the foundations of what we are going to talk about are quite simple and how they can be used has infinite possibility. But I want everyone to remember and be grounded in. We'll be talking about real principles that are grounded in basic things we all know and understand well while they can become much more complex in execution. But their theory understanding is very simple. So my goal is to demystify perhaps or help makes concrete some of these things that people are hearing about right now about this space. The evolution is obviously crazy. So having an understanding that is oh I can get this and I can stay with this, I understand what's happening and possibly have some new things they can use today and understand how to

B (4:15)

go to so listen, if you're listening to this episode at any strata of the business, the CEO, frontline employee, it doesn't matter the access to be able to build some pretty powerful again automations, apps or agents Today just using natural language is something that's blowing me away. I I got my first account with Cursor in September of 2024 and it was cool. You could watch it build stuff but the stuff would break now today I am not technical. I'm the exact person you were just talking about. I'm able to do some incredible things because the tools have gotten better, but also because of some of the frameworks that I've learned from you. So I want to start. We've got. And I'll give you a perfect example. I was speaking to commercial property owners in Nashville recently. And, you know, one of the things that we kind of focus on when we're working with clients is kind of three things. One, they don't understand the risk. So we want to make sure that they kind of have governance in place. They're eager to get started, but they don't know where to get start. So we help them with pilots. And then the, the real kicker is we don't have anybody to help us. Right. So most of them were in that boat. Yet the questions I was getting were, hey, how do we build agents? It's like, whoa, like, slow down. Let's, let's make sure that, you know, you guys know how to do the basics. So this is a topic that is not reserved for, like the, the bleeding edge of, of generative AI application. This is something that everybody's hearing about. So maybe let's start with demystifying what is, what's really possible and what is not yet possible for somebody like me who doesn't have a lot of the background when I get in front of these tools.

A (6:03)

I think this is going to be a question that probably some people are, you know, it's going to be maybe polarizing. No, I'm getting. For some people, it may be interesting, but I, I believe that anyone, and I know this both from training people and also from, you know, out in the field. I have yet to see, and this is going to be, you know, maybe controversial, but I've yet to see a lot of the limitations of anyone who is resilient enough to ask the right questions and not find a way to what they're looking for right now. I think there is a lot of, you know, again, there could be lack of clarity on where you're trying to go, and it could be, you know, not knowing the right language to use. But really, I truly believe this, that we are in an age where anyone can pick up any of the tools. Now some of them are simplifying that into wysiwygs and you drag and drop certain things that people can quite literally connect all the way to asking a code agent to do it for you and having faith and hoping that it ends up in the right place. And when it doesn't, being able to work through those problems and finding it, I think that's the. The actuality is I think anyone can pick up this stuff and it really depends on the degree of the fundamentals are being taught the same everywhere. As you know, I've done countless certifications for places like GPAFSA.com, sort of from the chief officer program all the way to Google and working through IBM and the fundamentals and how they're being taught, they're all the same kind of infrastructure. Right. If we're talking about how these things work. So that's not changing. And I think anyone who can open a few blog posts can see how the main stuff work. But if the focus isn't on the outcomes or on what am I actually trying to accomplish and understanding the main components that lead to that, I think that's where people run into their biggest issues. Right. It's not about the tools. I think people can figure out the tools.

B (8:00)

Yeah.

A (8:00)

It's the foundations that will transfer. Here's a good example. Yeah, just one last thing on that, because I think it's important. Right. I mean the tools say open call comes out. Great. There was, there's any 10, there's like I said, lovable, there's this, there's that. And then paperclip comes out and wait a minute, what about now? Wait, do we change everything? Do we not do. But it's the foundations of building. A good agent can transfer to any platform. It doesn't change. It doesn't matter. If you can do it right in one place, you can do it right in another place. And I think that's the key.

B (8:33)

Okay, so you mentioned like building, quote, unquote, these things for somebody who's listening and they've just heard the term agent, but they're not necessarily clear on what exactly can an agent do. What are some recent examples of some things that you've thought have been pretty clever that you've either built for clients or helped them build?

A (8:52)

Sure. You know, and I'll take it one resolution higher just for those who are listening and are still, you know, wait, what's an agent? You know, as a whole, for a moment, the way I look at it is a progression of when you're interacting with any chat model, LLM, large language model, whether it be ChatGPT through OpenAI or Claude or Entropic, whatever and whatever message you send, that's a prompt, right. Just to make sure everyone's unclear, no matter what it is. Even if you're not structuring it or designing it. You have sent a prompt, a request from that model and the model has a set of instructions. It then replies to you from the knowledge it has. And you have a interaction with AI that is not an agent. Right. Where I think we cross that threshold and where most would benchmark it is when you now have a tool that is connected to outside tools. So if I'm talking to ChatGPT and I ask it to do something, it can also run say a report for me on my Salesforce account or can write an email for me in my Gmail account. And where you take it to agentic workflow is where there's a certain trigger that makes it do stuff for you automatically. So now say I have Claude that's connected to my email, but then I could say every morning I want you to check my inbox and create a list of the top priorities I should answer and send me, you know, that notification. Yes. Now I have tools, I have my language model and I have some type of automation of when a trigger that causes that to happen. Right. And that's when we move into more agentic where it has its own reasoning, it can make decisions, it can, it's going through a flow that you've determined for it. And it's not just you having to make this interaction with a chatbot that maybe just has knowledge but no tools or capacity for, you know, recurrence, you know, beyond that, if that makes sense. So just for that now? Yeah, yeah, if you want to, you know.

B (10:38)

No, I think the example that you gave is like very simple but applies to everybody listening to this. If I had something that could give me that morning brief of what did I miss yesterday in my inbox and my Slack and my teams and all that kind of stuff and can kind of give me the, the TLDR or the 8020 of what I missed if I started my day and that's what was waiting for me. So much more productive like from the get go. And I think that anybody could benefit from that. So that's the type of thing that you're talking about here?

A (11:06)

Absolutely. I mean the best example I have is very recent. I mean I was out of, I was out for a few weeks, I had to travel, right. And I came back and there's a lot to catch up on. Emails I have, you know, where, where are we on this project? I have notes, but also, you know, so I had an agent get, you know, essentially go out, check all the emails I got from that, from Particular project I'm working on, all the meeting transcripts, all the notes I have in notion, combine it all, look through it, see where the status is, see the communications that were happening while I was away to to give me a progress report and then on top of that, create me a plan of okay, so we are here, here's the next move. Right. And now I'm back in the flow as if I never left and I'm up to speed. I know where everything's happening with every one of my multiple projects. Right. So that for me was incredibly efficient way of using an agent to just be able to pull all of it together. Right. So that's another example for something simple. We can give more business centric examples in a moment if you'd like.

B (12:08)

Yeah. And you know, I failed to mention this at the beginning, but you are not a computer science degree. You're not any of that type of stuff. I mean you're a soldier.

A (12:22)

Previous soldier, yeah. Yeah. I mean I spent a decade in marketing and systems and behavior get an outcome out of people, but not a tech. Right. And that's the interesting part I think for me, but it's a no. Always been kind of in say I've been in SaaS from the marketing perspective, working with the tech team, always curious about and wanting to be and kind of picked up some code I needed for front end. You know, if I'm a marketer I need to change something on a page so I don't have to wait for a developer to change like move a banner somewhere. So picking up some stacks over there, marketing automation stuff. But no, only in the last few years is really when I, you know, started to went all in pretty much and my own education on this and you know, working thousands of hours and, and developing and building with AI. Right. Which I think is the greatest feature out there.

B (13:15)

Yeah. You know, but you've got, you had the luck not to say luxury. I mean it was a sacrifice for sure. You made the choice to dig in and focus on this like at an extreme level. Most people listening to this who are running a business, running a team, whatever, they don't have that option and they don't have anybody on their team who they can say, hey, take the next nine months or year and just go figure out this, this vibe coding, vibe engineering stuff. So for those people, what, you know, you mentioned that you've developed some frameworks and things like that that make it easy or easier. Where should they get started? I mean like hey, I don't know much about AI but let me go build an agent doesn't seem too reasonable.

A (13:57)

Well, I think that the best, truly the best place to start is to articulate what you really want. I think that's a. What am I after here? Right? Because it can be an agent, it can be other things, but we have a ability and access to a tool that has infinite abilities to give us a lot of great answers if we know how to ask the right question. So I think having brainstorming and stuff that I did initially with AI, sometimes I was just on a walk with my dog and I'm talking to it using the voice mode when it was originally using it, just asking, continuously asking questions, trying to figure out how can I use it and saying, well, what can I do with this? I think it's hard in general in workflow sometimes to look at ourselves and say, well, what could I use it for? And I know what I'm bad at, so I may try to optimize that. But that's not always the best path forward. Right. Unless it's a fully automated system, I'm not going to suddenly become great in my email communication unless it's fully automated because I'm not very good in my communication. I'm very good in my deep dive in work mode. So unless there's fully automated. So trying to automate what you're bad at isn't always the best first step. Sometimes it's good to enhance what you're really good at and make yourself exceptionally faster at it. Right. And then that gives you also motivation because you're trying to get good at something you're bad at. You're not going to get a lot of wins necessarily initially and you hate doing it anyway. So even working on that project will be hard for you is what I find.

B (15:20)

You know, that's interesting because it's contrary to like the strengths finder where like focus on what you're good at. Right. And what you're suggesting is that a great place for people to start would be actually building outsourced solutions, agentic or automated solutions for things you're already good at as compared to trying to make your weaknesses better. I think that's a counterintuitive, but makes a lot of sense.

A (15:43)

Well, I think the, if we look at the frameworks for like the best roi and I've seen, you know, and I've been working with different companies of different sizes, you know, but what we see is in the field is this desire to build the new and desirable potentials, which is it falls into Two categories like this shiny kind of new opportunity. Sure, that may be there. And a. Or the pain points of. Okay, this really sucks. I don't like this. You know, what if I never had to write this report again? But as an operator going into that place, you also notice that it's very hard to get them to actually give you the process of how they're doing it now or how they. It's like everyone loses kind of an dynamic. However, say you're a company who's running ads online and you can increase by a couple percentages your conversion rate. That could be millions, right? Depending on what the situation is. I mean, you've seen it before where we suggested a solution that could save hundreds of thousands and compress it into a few hundred. Not because it was an agentic workflow, by the way, but just implementing that intelligence layer, that AI into a middleware. But the idea is sometimes it's finding these simple solutions that could be big wins for something that's a pain, that's actually proper. And sometimes it's better to have a larger percentage on. Let's take what's working and make it work way better or faster. Or find little hinges where small percentages give big wins and have a budget for the new desirable and pain and the problems that I haven't solved yet in general that maybe AI can help me solve. Right? So I think that's at least currently where I sit with it and where I think the bigger wins are.

B (17:21)

So I know you've done a lot of training of individuals like me, like our listeners and that sort of thing. What can somebody who wants to learn this stuff, like what does that journey need to look like? Like, how long does this need to take before they're able to say, look, ma, I built this kind of thing, you know.

A (17:36)

You know, I think it really depends, right? I mean, you know that I run a cohort and I try to do the best that I can within six to eight weeks of the program for people to leave with everything they need at the highest level for it for people to just get in there and get something live. It can be fast. I mean people are giving templates for all kinds of places to get started, I think where like to know how to do it right, you really have to get your hands in there and you have to see the differences when you make a small tweak to the. Even the adjective that you're using in part of this personality or system prompts or that make these agents overall personality. I mean, there's the Core kind of four things that I think about with agents. And mainly it's going to be kind of the character, the capability, container and channel. So it's kind of these four Cs of sorts. Right. So it's kind of a who are they, what are they, how do they think? That's where the human mechanism of understanding how to get them to think right and work in the right process capability, what tools are they going to use to do this job? Right. What are they connected to? How are they connected? What knowledge do they need to perform this task? Right. The container is more so where is it built? And that could change the environment. Again, that could be an open cloud agent, it could be a cloud code edge, it could be an N8N or whatever you want to be. And within that the governance and how can I make sure it's safe and what am I following there? And then the challenge is just where do I interface with this agent which can be in many places that you can build design, but not just the actual interface, but what's the experience of that interaction to make it the best for your workflow. Right. So I think those kind of core components really travel throughout, you know, all the different spaces that it may fill for the agent. Yeah. So if you, if you say, where do we begin and how long does it take? Well, if you come from a background where you understand one of those sections better, say my marketing world and my interests drove me through behavioral economics and behavior shift and understanding models of behavior and influencing steps to create an outcome really helped me with the top two of those. Kind of the character and understanding kind of gave me all kinds of creative ideas initially with replicating different influencers and helping them with their AI journey to capabilities and understanding what tools are. What's the process of. In the whole marketing world, at least the one that I've been a part of for a long time is the funnel, the steps. Understanding the process of step by step, what needs to happen to get the outcome. So in the same way with AI, that works really well in the capability section, container and channel is really where I needed most of my, the technical chops to. Okay, so now where do I house this understanding of how to create this capable personality that can connect to things, but how do we house it and how do people interface with it? So I think if you have one of those, say if you're a developer and you understand a container potentially better, or have the interface chops of connecting a chatbot somewhere. Right. That might be, you know, a good place for you to go a bit deeper into and get your kind of roots of like, okay, I can get to get it set up. Now let me understand how to better design its process and how it should work. If you are more from the marketing, you know, product, you know, potential, maybe you start with more of the capability and character side of it and more, you know, if you're more dev backups, maybe the container is your main focus. So to, you know, so really wherever your experience is, I think the agentic, you know, if you look at those kind of Cs, it can help you find a place to go deeper into and how it ties into AI and as a starting point of your strengths that you may tie into better. You know, I've done work on all across and I can say that definitely, you know, master the ones that I really love and understand well and then capture those that you need to to just house it and vice versa. Or you can partner with people who know those other sections too. Also good. But I think most people can understand enough on how to launch this. People can launch and I've seen it happen where we do a session where after 90 minutes people have already something up and running. Is it optimized? Is it the best, you know, has it have all the things? No, but they see something that they can connect to tools that works and they can talk to. Right. I think that's pretty powerful.

B (21:52)

Yeah. So what's the difference between people? Because I'll tell you, I've been messing around with openclaw and it's been hellish. Get it built, breaks, add something, breaks again, fix it, breaks again. That hasn't been fun for me. What am I doing? And I would imagine that anybody listening, if you've had that experience, you know exactly what I'm talking about. What am I doing wrong that I need to learn from you?

A (22:18)

So without sharing screen, I'm not going to say that I know all the reasons what I might speculate because I've seen other people online having a lot of issues with things like that. And it's. And this falls into a category where I see a lot of the. And this is not on you. This is just in general, I see a lot of the challenges that show up and people run into these barriers. It's like, well, you don't say this because you see the technology and you've used it and you do amazing things with it. But I think there's a lot of people who see that and say, oh, open cloud is not all that or it's not that useful because it's stuck on something. Right. I think there's. If we look at the foundational pieces of what needs to work and go step by step, that's typically where you will find our clause. Right. So how are we connected to. There's the code that you actually have to use to connect to it and there's options for that. You're either doing it on your local machine or you're doing it on a server. And those could affect certain mechanisms. There's a language model you're using and which one it's better with or worse with. And then what's important, I think with say something like OpenCloud, it could be with anything really, is if we look at the structure or if we don't know how to look at the structure, we have AI analyze the structure and let me know. Right. But it can be in a way that I understand, hopefully. But if we look at it and we say, okay, well, there's these files in here and there's components. Is there something that's clashing? You know, did I accidentally add something in the soul file that should be in the memory file that's not in there? Did I add a skill that contradicts another skill? Did I get these heartbeats and set up an automation trigger that conflicts or issues? Because a lot of times it's these silly little, say, contradiction that happens in there that breaks everything, or if the model doesn't get picked up because something in a configuration went weird and there's not an update that you didn't do. So I think looking at each piece of this, say we look at the four Cs I made, even though it's not for. Not particulars, but if you look at the four Cs, it would fit where you would say, well, let me test a few things here. Does it, you know, it must be the container or the channel for the most part, because, you know, personality things aren't going to be the big. So what in the environment could be not working? Why is this, you know, is it working in one place and not the other? I know that's for, say, you know, Aton has one working. What the hell's going on over there? Right. So is the environment different because he's running it on a Mini Mac or a Mac Mini, where some people are saying don't do and some saying do. I don't know, I just bought one. I don't have a strong hold on that. Guys, do whatever you want. But I think there's an element of troubleshooting. So to know exactly what you're not doing, right, I don't know. But to go through the steps of each one and trying to understand where is the breakage happening again? And this is where the understanding the. Who would know? Who would know? Well, a developer who is really good at debugging stuff might know why is this not working? Where is the break happening? You may suggest adding logs to see where an error happens and it sends you a notification to see clearly where did the issue happen and what is it. So again, it's kind of learning, you know, I don't know that this is your situation, but I would say most people need to just add a few more tools or a few more words to their vocabulary that would help them get, you know, I'm not a, like we said, I'm not a programmer, I'm not a CS degree yeah, I didn't do those things. But I do understand that there's a process of, you know, qa, there's a process of debugging, there's a process of people who know how to do this. So I don't need to call those people, but I need to ask AI to become those people to help me figure it out. So I had a lot of issues at first, as you know too. It took me a few days where I just sat down, I was like, okay, I'm going to figure this out because what the hell is happening? Mainly the issue was the connection through Claude for me or figuring out the right model and it was trying to call the wrong model a lot of times. And every time I introduced a different model it would just right or no answer. So for me that was a big issue. But once I got that resolved, everything else started to flow. But the automations, you know, just looking at the components of what's in there and then trying to go one at a time and put your finger on it because I can't really tell what your issue is without looking at it. But I would say that's the approach I would take and suggest also and that people have seen me do live in trainings and things like that. All right, well, we need to figure this out. We need to research a bit more who else has this problem. I'm running into this issue using tools like Perplexity, using things like context 7 that has a bunch of code libraries and saying, oh, maybe someone figured out something there. Is there code examples, repositories where people fix this? Not that you have to do it, but you just have to know how to ask it and ask for what you want and see what you get back.

B (27:06)

Based on what you just told me,

A (27:08)

I think the issue was.

B (27:09)

So my. My Colby index is high Fact finder high quick and high Fact finder plays into. I'm on Twitter. I'm on X. Right. And I'm. The algo knows that I'm into AI. I see all that's fo central right there. Right? Because you think every way ahead of you in AI when you're in there. Because here's, you know, here's my lessons from setting up open claw and cowork and clogged computer use.

A (27:35)

Yeah.

B (27:35)

And I would save all those. Right. And they would say, oh, just. Just give this to your agent. Right. Let your agent read this.

A (27:42)

Yeah.

B (27:43)

I just crammed so much stuff in there. I think that it was finally just like, dude, stop. Just. Just.

A (27:48)

It's not. It's not you. Honestly, I've seen. You know, so I've demoed this live with people too, where I. I. Every day, pretty much, I. I look at something online and I demo it for myself and. Yeah. See if I can recreate it. Nine times out of 10, there's issues that they did not articulate in the demo online that you're gonna face. It's just gonna happen. They probably solved it, and then they come back and shoot it or whatever it may be. You know, it's like. And it's always gonna be an issue that they did not mention that you're gonna face. And it's like, wait, why am I the problem? What's going on? I guarantee you you're not the problem. Well, okay, I'm not gonna guarantee that, but you might be the problem in some cases. But a lot of the times they've done some troubleshooting before. They've done this before. They have it set up. They have their computer set up a certain way. If it's a local thing, there's always stuff that aren't mentioned. And it's kind of like assumed, but it's not really assumed because they know they had to fix it. So it's almost like. I don't appreciate when people do that too much because it's kind of like you just do this, this and that, and there's. That has never happened. Pretty much never have. I just taken a demo. I've always figured it out, like, okay, so we actually have to do this first or move something, or in my setup, at least, it's like this. But that's where it comes in. That's why I do it all the time, to troubleshoot, because that's really the skill. I think that's very helpful, you know.

B (29:03)

Well, while you were out of the office, I. I said, you know what? If I'm gonna build, I'm gonna work on building these things. I might as well, like, turn on Zoom and invite other people and just let them know, hey, I'm not teaching you anything. But if you want to watch my experience of kind of wrestling with this stuff and stumbling through it, do that. I think I did. Maybe I did a Monday and Wednesday call for an hour, and I was building outside of that for sure. But I would jump on and, you know, members of the chief AI Officer community would come on and just. Just watch, like, what I was doing and kind of explain it. And, well, I thought it was going to be a little more exciting, but it turned into, you know, every session was really like, hey, it was working, and now it's not. Okay, let's debug it. Here's how I debug it. And I'm again, listeners, I'm not some pro at this. So I was doing probably what anybody. Like, probably what you were doing a year and a half ago, just, like, stumbling through it until you, like, ah, something stuck. And I finally decided, like, this is not something I want to do alone, because somebody asked me, they're like, hey, man, you're a CEO of a company. You got other stuff to do than fool around with this stuff. And I was like, you know what? Like, yes. And I think that it's so important to understand this because of the impact that it's going to have. I mean, the quote that Jensen Huang From Nvidia @GTC a couple weeks ago, he suggested every company needs to have. I mean, he called it an open claw plan, but just in general, like, every company needs to have a. A claw plan, right? Like, this is a powerful tool. If you can get them build and introduced and think about how to use them, they can transform your business. Right? So this is not something that's, you know, with the name, like vibe coding or whatever you think, oh, it's just kind of like something you do casually. No, like this. This can be major impact. So, speaking of, I mean, I know you're working with some pretty impressive companies. Let me ask, are they asking for this solution specifically, or are they just asking for AI and you're coming in and suggesting what the solution should be?

A (31:06)

Well, I feel like we're right now in a place where people have a. At least in the size of the company. I'm kind of mid market at the moment and a lot of companies, you know, plus private equity as you know and all that. But seeing the. There's a lot of ideas and no one knows how to get there. Right. So it's open for suggestions on how you go there. And I think there's kind of the few camps I see is there's the, you know, I'm already in the process of evaluating these vendors who need support because it's going to be big contracts, you know, seven figure AI agent deals. Right. To evaluate and see what that system does versus a building one in a. And how do you make the distinction between when it's time to buy a solution versus build one and really the spectrum could be it's all across that. Like, let's look at. We know some of our problems. I'm lucky to work with some also really smart companies who've documented a lot which makes life very, very easy for the AI side of it. Or not easy, but a giant jumpstart. When you know the process and you have documentation and it's well documented. You're like, wow, okay, we can work with this. I think it's much harder when you don't know. So the range that I've seen now is companies that got to a certain size because they've been able to document and work through certain process and they have a good pulse on. They have the finger on this pulse of. I have an issue here. I feel like this is ripe for this opportunity and then I come in and suggest and we can build and then we build through there. So what does the solution look like? We may not know, but we know this might be a good opportunity for it. It's kind of where I find people right now.

B (32:49)

Okay, for people who are doing this, maybe somebody's had the experience that I've had because a few people on the, on when I was doing those zooms, they would be like, hey man, it's good to see I'm not the only one that's struggling. Right? They saw me doing this. So what are some of the mistakes that you think people. Probably the most common stuff because I know like you're, you're taking people like me and you're turning them into people like you who can follow the frameworks and are thinking about things through like an architecture or an engineering kind of perspective. But in layman's terms, like, what are you seeing? Like, where are people struggling, Sir?

A (33:37)

You know, what comes to mind is, is something that I said at the beginning of, of the, you know, the program around. But let's say it's often this idea of, you know, how there's definitely people who you've seen who are unbelievably successful and to yourself possibly say, how's that person doing? Like, how are they so good? They're so, like, they're not smarter than me. Like, let's be honest, a lot of, you know, a lot of people I see is like, they're not that smart. They're not that, you know, like, they're not. They're just not a person. And they're like, what the hell? How did they do that? Right? And you know, for me there was this desire to be, I want to be like that, that dumb person. For all these smart like tech people, it's like, how can I be that guy? Like, how the hell is this guy doing this?

B (34:25)

Nice.

A (34:25)

Doesn't have my, you know, he's have this. How the hell is he doing that? Right? And I think it's. It took me a very, you know, it took me a head in many ways because I was unaware of what I don't know. But my optimism or my hope of where I can get to is so high that I go probably past where a lot of people were experimenting with it because they said it can't do that or it won't be able to do that. But my side, I don't know, can it, you know, can it not? Let's see. Let's test and connect that to a problem solving framework and really just having an expectation of, you know, or, or this realization of if some dude figured this out, eating Doritos and Coke, you know, drinking Coke at like 2 in the morning with his hoodie on, headphones and barely sleeping, you know, they're sleep deprived and they were googling and piecing stuff together to try and make something work. And if they figured out code and built apps, it's like, man, I could figure it out when I'm sober, just trying to solve problems and working with a system that has access to all the stuff they had access to and more and is able to now the benchmarks are getting crazy every day. It's getting better at coding and things like that. If we just talk about that. But where we are in the field right now is we have this tool that can help us figure it out. We just have to have that determination. And you know, going back to kind of full circle to what you were kind of saying, what does it take to do that? I think the first step is this acceptance that you don't know, but you have support that will help you get there. It's kind of like I have a tool that can help me get pretty much what I want to know and do. And believing that even if you don't fully understand part of the process or what you're getting at initially, like any new skill. Right. I mean, I understand it, but I'll get there if I follow a process. Sure. Right. That's why, you know, the best systems in the world, companies like McDonald's, where you can bet any kid on the floor can flip a burger, knows exactly what to do, when to do it. That's why, you know, that's why people model them for certain businesses. Right. I think in this, it's also this idea of, I don't know why I'm flipping it exactly every 45 seconds, but I should. And then you start to see the burger come out, you know, the way that it does every single time, like, oh, that's why I do it like this. Right. And then maybe if you're an intellectual about it, you start to wonder why that is and you can learn even more.

B (36:48)

But.

A (36:48)

But at the very least, you get the outcome of a burger made well to what you need to. Right. And I think that's the case is learning the framework to work through, and that's a support the steps that you want to take. So if you can have the mindset of, I'm not going to understand this yet because I don't know it, but think about this. In what other world we've been able to just immediately have feedback and work with something that can also explain to us what we're learning as we're doing it and executing. Why did it break? Just having this back and forth that is so educational and helpful while you're trying to figure it out, there's nothing like it. I think by now it's probably a combination of who knows how many hours of learning how to do these things. I've received with this.

B (37:34)

So you've mentioned frameworks a number of times, and I know that you operate from those in this process. And that's been your ability to transfer that knowledge over to the folks who are in the community who are now doing this stuff. That's the stuff that they reference is those frameworks in particular. Can you take a minute and explain maybe some of the ones that you're leaning on the most? And then also, how did you arrive at discovering these frameworks? Sure.

A (38:02)

Well, I mean, there's Been a. I mean, I've spent a long time, you know, long. Not as long as some, but I spent, you know, a decade working in businesses. You know, took my. I was from a specialist to an executive and then went on to build my own. And in all that journey from working within the entities and kind of seeing where things are, I think it's important to note that because moving on from there and getting my ass kicked as the initial years, as everyone do, but also examining when AI came, who can I learn from to understand this particular game best? And as a solo kind of consultant in the world, I want to study who's doing consulting best. So studying and reading and, you know, certifications and like we said, and really collecting, but taking all of that and applying right away, which I really believe maybe is something that I do particularly different sometimes than others, where I take something and I immediately try it and see where I end up, right? Or apply it and just push through on it. And I think applying and taking the learnings that I've taken from all of these places like, you know, IBM, Google and all that, and combining it into a framework to understand how they execute AI projects. And what am I seeing when I've been doing the work I've been doing in my experience and what all these people I've learned from over the years and what I've been able to do on my own, but taking that and saying, okay, well, the first step typically is to understand and define the problem properly or the opportunity, so you can understand what you're trying to work towards, right? So my main framework, and I'll mention it here briefly, but if we talk about the main project framework, the Scouts framework that we talk about, the first stop is that scope the objective. So we say, how do we understand what we're after here and the clarity of the problem? Now, there's plenty of quotes on it, right? Like defining the problem is 50% of the battle and so on. And big consulting firms like McKinsey, they put a lot of emphasis on, that's the main initial part of the work. And the research and the data is all to define the objective that they have to the problem, right? Taking it, disaggregating it into the components that make it and will make it successful or not, or what do we need to consider? And then moving from there, we move into the C, right, To sort of collect data. So in AI, the data is one of the most important points that you can have. Does it have the information it needs to execute the job? Does it Know the procedure, the process, does it have, you know, what data do we have? But it also connects to the research we're going to do. What do we need to look for? Is there people who's done this before? How did they do it? Right? There's frameworks that, you know, that I provide with that and prompts and all the jazz. But in the overall concept of what do I need to understand about this? If this is the first time I'm building something here, what would someone who's done this already a hundred times know that I don't? Where would they look? Would it be, you know, what do I need to collect for AI? I don't have to understand all of it yet. I just need to collect the right information to do the work so it has what it needs, the clay, so I can mold it. Right. But if I collect the data, whether it be internal and institutional or outside or you know, from the world, moving into, outlining our plan, right, of what we, you know, in our O, of what, what does this all look like? What are the steps to accomplish this task? I have the data, I have the objective, what do I need to do with it? Right? And then overall to the, you know, you we unleash and we start to build and we move quickly to get a prototype of what it may look like so that people can test it in rt. So we want to build something quick because once we get it to people, they can test it. Once they test it and there's the steps there for function, for style, for governance is where we test in there, you move to shipping it, how do we wrap it, explain it, create the training materials for it so people can move from this idea or problem all the way to a packaged sop of how to use this new tool that they have. But within that, when I talk about the four Cs and all that, it could be specific. So when I say frameworks, everything pretty much sits within scouts and there's a lot within each pillar. There's kind of the best way to do it, but then specificity for, you know, if we're talking agents, as we are right now, but those things are core and haven't changed in a long time in a big world of products. So they're derived from best practices across and from hands on, you know, building over, I don't know, hundreds of chatbots by now and you know, many automations, multiple apps end to end with AI embedded in them. Solutions ranging in companies all the way from a billion to millions to whatever it is These concepts work as a whole. They just work where we deploy them, how they work within a specific tool or process that becomes less relevant. But if you grasp those concepts and you apply the right kind of methods to each thing, it doesn't matter what happens with the tools.

B (42:47)

How long does it take for somebody like me to know how to follow that scout's framework?

A (42:56)

Well, I mean, in essence, if you just, if you listen to what I just said and repeats for a few times, you might get it and just have a sticky note. You'll probably know. Okay. And you figure out. But as far as learning it again, you know, I think there's. I've seen it happen in as little as, you know, 90 minutes. And I've seen it happen after weeks of the seeing the same process being executed over different types of projects.

B (43:18)

Yeah.

A (43:18)

And then at the one project that's relevant to you, suddenly it clicks like, oh, I get it, I understand. Like, this is how I'm doing it. Because it's not necessarily linear. You jump sometimes research. Those pieces of the puzzle are always kind of there and may shift. Right. You ship something, you're testing it, you may go back to building. Right. So it's kind of a understanding. But often if you, once you get the framework and when I see this happen, it's honestly one of my favorite things is you start to see people just get it and they can get to outcomes they want to because they understand the. Oh, this is what this means. This is how I solve this. I might be missing some data, I might be missing some steps here on the plan and not seeing something right on top of that. I think the greatest. And this is. Okay, I'll throw in one last one thing here that's really, I think, I think the greatest. One of the greatest things you could do with AI is ask for the questions you need to be asking.

B (44:20)

Yes. So who is, who is coming to you to learn this stuff? Like, what's the avatar? Is it execs? Is it entrepreneur? Solo folks? Who is it?

A (44:35)

I would say that there's a wide range of individuals that seem to be. Want to have this skill, but a lot of people who aren't necessarily technical. I've noticed a good amount and a good amount that are incredibly technical. I think that really we are right now in the age of agents. I mean, that's why the market, every time Claude has a updates that their stock is going down because they're worried about mainly seats being taken. I won't go into the market. But the overall, I think we're in a time where everyone needs to at least be aware of how this affects them and how to do it. But we've seen people who have never even touched a line of code or seen an automation tool, orchestration tool, or move all the way. A lot of people who are certified GPI officers, of course, from the strategic and landscape, want to get the hold on that skill as well, which makes sense. I mean, if you understand the landscape of that and you want to possibly either be the closed loop on it or know how to effectively find people who know what they're talking about, then it's typically the people I see, you know, different environments, very few people who are on the, on the, you know, a lot of people who are high level, you know, operators out there who want to add a skill set that's, you know, the, the, the rise of where we are right now. I think that's the, that's the people I see mainly.

B (45:58)

So I saw a tweet from Cody Sanchez, based in Austin. Cody does a lot of talk about entrepreneurship and that sort of thing. And I've seen a few posts from her saying that what really transformed her business recently was she found some builders, some people that know this stuff. Right. And I, I don't know that she's necessarily somebody who's building things and I don't know that she has an army of them. But the impact that she said of having a couple of these, like, Claude coders or whatever come into the business to where the operator can identify, hey, there's a constraint, there's friction, there's whatever, and they can turn that over to the person who's like, okay, let me, let me go over there with Claude code and see what we can fix or whatever.

A (46:39)

Yep.

B (46:39)

She said that's been like a huge lift for her business and that any business that is looking to like, adopt AI. Yeah. Get your people trained.

A (46:51)

Yeah.

B (46:51)

Teach them how to use it safely, all those sorts of things, and make sure that you're not leaving out that builder, you know, cadre. You've got a couple of those people or one of them who can come in there and what's the problem? Okay, great. Give me a couple days and I'll tell you. You know, a perfect example, like my big aha with this was we were working with a construction company in Orange county and they had a, like, like everybody, you know, every company's got them. They had this big monster spreadsheet that was like just this. It was the only way they knew how to manage all of these elements related to ensuring, making sure that their subcontractors had appropriate insurance and it wasn't expiring and all that. And it was taking three people off and on. You know, they all kind of had their hands in it, but with replying to emails and following up with stuff, about 20 hours a week between the three people. And it just. Was it painful? It was just part of what they did. They didn't really think about it that way. Right. And I brought in a guy and I was like, hey, can you build an automation? Thinking like N8N was going to be the solution. And he's like, sure, whatever. 24 hours later he's like, hey, I fixed that. And it wasn't an automation that he did. It was actually like he Claude coded a Solution and in 24 hours he had created something that took it from 1000 hours a year of maintenance to 100 hours a year of maintenance.

A (48:10)

Right.

B (48:10)

And that was one task and, you know, one department in this company. And if a company doesn't have those people who can come in and, well, we just wiped 900 hours of, you know, like inefficient bandwidth off the table. If you don't have those people, and I do, eventually, like, it will start to show, you will not be able to keep up with that. That company that's got the builders or the company that's led by a builder or something like that. Right. So all that to say for the, for the listeners, this is a role that you need to be sourcing like immediately, that it's probably much easier for you as a listener who's like exploring AI to say, oh, I don't know about a AI transformation, I don't know what that looks like, but boy, we've got one of those spreadsheets. It'd be great to not have to do that stuff anymore. Right. And I can tell you that as a, as a listener, if you are that person, great. If you can find them. Hard to find. There's. There's not a lot of people out there. There's people who can build you like a little cutesy app or whatever, but they're not thinking about business through the lens of an operator like Aton is. Right. But even better is if you, as the executive or as the team leads, you have the skill and you can say, oh, you know what, I'm just going to fix that tonight. Right? Done, gone. Oh, and by the way, tomorrow I'm going to show my team how they could do this thing too. Like, then you don't have to worry about going to source those people out there because they, they're hard to find. They're one of the hottest commodities that are out there. Hey, just a quick break. I want to tell you about something that's worth paying attention to, particularly if you're enjoying the topic on this episode. If you've been listening to our show for a while, you know that I talk a lot about AI that's actually working, the kind that saves real hours, is cutting real cost in companies and is showing up on your CFO spreadsheet. But I get asked all the time, how do I actually build these solutions? And that's what I want to talk to you about now. That's exactly what the AI Agents and Automation Builder certification was designed for. It's a six week live hands on program taught by Eitan Pollinger right here in this episode. And it's built for people with zero technical background who want to come out the other side able to architect, build and deploy real AI systems for real businesses. You'll work through the same professional frameworks that we've been discussing on this episode and by the time you're done, you will have a fully built AI mini app, a portfolio of production ready builds, and a client ready offer that you can take to market. So our next cohort is kicking off soon, April 21st. Live sessions will be held every Tuesday and Thursday, noon to 1:30pm Central. So if you're ready to stop experimenting and start building, go check it out. The link is www.caio.cz agent. With all of our cohorts, the attendance is limited. So if this is something you're interested in, I would encourage you to go there, check it out today and enroll. The classes will be starting in just a few weeks anyways. Now back to the episode. So we're get, we're getting to the end of the episode, but I want to make sure I'll be doing everybody a disservice if we didn't talk about this cohort that you've got going coming on. And the reason that I did this episode now is because we're a couple of weeks out and they fill up. But this is something just, I see the impact. I see people that are graduating from this certification and like, like they're blowing me away with this kind of stuff. So can we talk a little bit about what that, what that experience is going to be like for those who aren't really. First, let's talk about what it's, what is it called?

A (51:39)

So right now we are working with the, you know, so we're calling it the AI Agents in Automation certification, you know, strictly to what it's meant to do, which is, you know, we're moving in this economy of agents and, you know, it's only going more and deeper into it. The goal is to take people from understanding how to approach a solution, build all the way to the strategic, you know, plan on how to execute it, to have the tools, the templates, their own AI chatbots and, you know, agents that have been trained on this, on these things, but take them from wherever they are now all the way to being able to create, execute, launch, deploy agents that would be helpful for their business or life or whatever it is they're wanting to do with them. We go all the way from truly the design and the thought to the elements of what makes a top 1% agent. And this is something that, this is really the differentiator. I think there's a lot of people who are showing how to do the basics of what these things can do. The results I've seen and that we see in the field are so instrumentally, you know, 300% performance increases and just applying one of the changes we do to how we upload a document, even to a chatbot, that increase drastically the results of how good it is. Right. So we go through the fundamentals that won't change and we make and we go through understanding how to execute them and give the tools to do them. And then in each step from automations to fully, you know, connecting different tools to that agent, conversation through chat or through some type of trigger, but really the A to Z, you know, how do we go from, you know, an idea and even evaluating a good idea to going all the way to the solution, design and build, deploy, ship and present. Now, for those who are in, you know, offering it as a service or within a company, I've had people from both in a program. We've just had a lot of success with it. But the main goal is not just having the education, but having tools that help you jump in faster and are able to see the outcome. From probing the problem to articulating the problem. There's pretty much a bot, I believe I gave like 30 something custom chatbots in the last cohort.

B (53:58)

Okay.

A (54:00)

Most of them are just for fun or, you know, or people can explore with them if they want to, but all the tools are there, templates to fill out, really as much as we could do to deliver the highest level of expertise in a kind of packaged way. So People can walk through it and the more you do it, the more you'll be confident in it and have the skill and the mindset of deploying these things well. So I'm very excited for it. This is the third cohort we're doing. It's been incredibly successful and if someone's listening and they want to, you know, have the operator mentality, you know, there's a lot of opera, you know, a lot of prototypers out there, like Chris was saying, you know, people who can do something nice on YouTube. But to be an operator, there's a few other elements that we require to take it to the finish line and to make it a business outcome that is great. So I'd be excited to have anyone who wants to, to join us there.

B (54:50)

So. So for the listener, two paths here. One, if you're, you know, the business owner or the leader, I would say just like with what Cody Sanchez was suggesting, get you a couple of these people, finding them is tough, especially like, okay, great, but what do you really know? And are you the listener? Are you able to judge that? I'd rather send somebody who already knows about my business, who knows our industry, like get somebody on your team, send them through this. Secondly, if you're so inclined, best case scenario, you know these skills, whether you're the leader, whether you're, you know, senior level management, whether you're somebody who's early in their career, if you have these skills, you are playing with, you know, a jet pack on your back and everybody else is lacing on their running shoes, you really are able to, oh, here's a problem, fixed, solved, agentic solution, automation, whatever that is. So there's no, there's no scenario that I can see for a business to maintain its economic viability where they don't have access to this type of skill set. Whether it's going to be you, somebody from your team or again, if you, if you can find them, if you can hire them. But I mean you're going to get the guy off YouTube. Yeah, I mean they're building cool stuff, but if they ever been inside of a business before, a lot of them, the answer is no. Right. So I don't want them necessarily saying, hey, they finance team needs your help. Can you go over there and you know, make sure that the month end closing is happening faster because of this stuff. So, so what we're going to do is we're going to put a link in the show notes for this and if you're interested, I don't know, there is a limit on the seats. I don't know that we've hit that limit by any stretch. But it's a certification, it's recognized by the International association of Chief AI Officers, the whole deal. So it's a, it's a real thing with a business intention taught by people who are doing this in like businesses. If you heard some of the names of these businesses, you would you go, oh, okay, if they're using this, I want to use it, right? So Eitan, outside of that, if this time's not right for whatever, anybody, how can somebody at least take that next step to start to not get left behind?

A (57:08)

I would say the first step would be if you're working within somewhere, truly take a look at your job, the deliverables that you're meant to do and have a conversation with AI on how it can help you and how you can build something to help you with it. And I would start with what's creating a prompt even that you use over and over again. If it's not a full automation, say what would be a good prompt, a good request I can make to AI that gets me a deliverable that I like a certain way. How would I build that and understand based off your role. Now if you're the owner and you're trying to figure out a bottleneck that you have, I would do the same. But more so from the vision of your how do I see AI playing out here? What are the places? And having a sounding board of working with AI to find and articulate, have it interview you say, ask me all the questions that you need to about my business and tell me what are the few opportunities that are currently proven to work with AI. Right. And it's okay, I know the visionary is just a proven word. It can go either way. But start with proven. Then you can go towards radical or whatever you want to play with there. But start with the, you know what is proven because things are happening in your industry right now 100% people are developing stuff, people are working, they're somewhere out there there's someone who got ambitious in one of these companies and they are starting to develop stuff because they want that, you know, they want the seed, they want to develop it. And it's going to be, you know, it is happening in a pace now more than ever and it is the time for it. So you know, it's a start with that though. Analyze if you're in a role, analyze your role, see what's property you can use. If you're a leader, have Analyze, you know, ask you questions, interview you on your business and see where the opportunities lie and what's been shown. There's already data out there, I'm pretty sure in most industries. So check it out, see what works, start what's working and then move from there.

B (58:58)

Awesome. Well man, thank you so much for taking some time out of the laboratory. I know you've got a limitless amount of projects that you're working on for all those clients. So again, I appreciate you and I appreciate you taking the time to help upskill, you know, people like me, the non technical business leader who is certainly intrigued by this stuff but maybe a little frustrated if they tried to do it themselves. So. And for those of you listening, if this is something, maybe it's not for you, but it's something that you know somebody who would benefit from this, please share this episode with them and any episode, obviously you know, any reviews we can get from you, any spreading the love. If this show has been helpful or just in general the episodes are entertaining or helping you with your, your learning curve when it comes to AI. The biggest thing that you could do for us would just be share it with somebody else. So thank you so much and as I always say, you know, go out there and use AI and I hope to see some of you in that cohort. I'll be joining it and we'll have a chance to work together on some of these agents, automations and apps. And thanks again, Eitan.

A (59:58)

All right, thank you so much. Great to be here.

B (60:01)

Thanks everybody.

A (60:02)

Bye bye.

B (60:02)

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