This podcast episode is sponsored by ChiefAIOfficer.com, offering training and certification through the International Association of Chief AI Officers. Interested in a new career or leveling up your value in the marketplace? ChiefaiOfficer.com can help. Welcome to Using AI at Work. I'm your host, Chris Dagle. Each week we'll be learning how today's business owners, entrepreneurs, and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started. Hi, everybody. Welcome to the podcast today. This is going to be an exciting episode because of the topic. This is a hot topic in 2025. Obviously, AI agents, where they're going, what's possible with them. And uh the best person that we could have talking to us is our guest today, Jake George from Agentic Brain. So, Jake, before we get started, if you don't mind, just kind of share with everybody, in particular, what I'm looking for is your journey to becoming this uh agentic expert.
SPEAKER_00Yeah. So where I started is, oh boy, a couple of years ago now, it was like right when GPT 3.5 came out. So what I was doing before then is I would build and uh grow developer teams. So we were mostly working in the crypto space. We were with this uh crypto marketing agency and uh consultancy. And so I ran their whole development team because oftentimes what they would have is clients that they were consulting for would need smart contracts written, which is essentially like the uh software that makes the coin or NFT for anyone that doesn't know. So, right about that time, uh GPT 3.5 came out. So, of course, like everyone, you know, I hop on, start playing around with it. It was just like this light bulb in my head. Like I had I'd known AI had existed. I had used it like briefly before, but as you know, before then it wasn't really that good. There wasn't so much like chat with it. I'd use some of like the uh like Google models that would do some of like the the video and image rendering. And you know, back then they were like really like not that impressive. So it was like I had kind of heard of it and I was just like, eh, but then you get on uh GPT 3.5 and it was just like a light bulb in my head. And I was like, oh my gosh, like this thing is really legitimately smart, like it can figure things out. Um, and so then I'm like, okay, sweet, it has an API, like what can we do with it? And so, you know, it just starts out with just like, oh, you know, write this for me, create a marketing plan, blah, blah, blah, like write this little bit of code. Where's the bug in here? But what we started doing was building out automations uh for the internal processes and the sales processes for that company, and then seeing the um the results that it had for them, I then broke off and then created my own company, which was the one that I ran before this one, uh, where we did like uh workflows, AI automation. And even like at the point of GPT 3.5, agents were not really a huge thing, although technically like possible, there wasn't as much like tool calling, code running, all that sort of stuff. So we mostly focused on like automated workflows. And then once agents started to become a thing with like, you know, like auto gen, uh relevance, uh um, what else do we have? Well, like Langchain, um, which I mean, Langchain is just existed before then, but their agent abilities got better within. So then after, you know, I just always have been up to date on what's coming out. So then I saw agents and I was like, wow, this is the next big thing. Because the cool thing about it is you don't really have to plan out every step in the workflow. It can be completely dynamic. And you're like, all right, here's five tools and here's our end goal. Just do the thing. And so if you think about it that way, it almost sort of like builds its own workflow dynamically and intelligently. So that's where I really saw it, where I'm like, it's useful before, but when it's able to call specific tools on its own, reflect on what it's doing, have memory, and make decisions based on the information that it's getting while performing a task, that's when it really gets to the point of like this can actually act as a sort of like low-level employee. And then obviously the models get better, the tools get better, it gets smarter. We have reasoning models now, um, which are not, you know, not so useful in like agents yet, but like they will, you know, they will be. So yeah, that's that's really how I got started. And then um, I met my business partner who was unable to be here today, unfortunately, Frank. And um one of the biggest things with my clients is that they would, when I was doing Centoria Labs, my last company is that they would have these very broken internal processes. So like we want to automate our sales process. Sales was the main thing, but just seeing how it worked, and okay, like what's your close rate? And it's like 5%. It's like, you know, yeah, you can automate it, but that doesn't mean make you're not gonna go to 20% close rate because you automated this. Like AI does not close for you, it doesn't sell for you. If you have a good process, it can help you and eliminate that extra like manual work that's like can give you back more time to sell, but there's like a misconception that it would sell for them. So meeting my business partner, Frank, who's been a fractional CRO for the past 17 years, um, he just is immensely skilled at being able to like analyze and break down these sales processes and being like, yeah, we'll implement AI, but also adding a sales consulting side to it to be like, we can add AI, but these sorts of things need to change, which we can help you change and automate. And then also with like a little bit of coaching and consulting, can really actually improve their um their sales. And that's really at the end of it. Like, companies love to like save time with AI, have convenience, but when you can help them improve sales, that's when it really clicks and they're like, okay, I I love this. Like, that's the main thing that gets people that you know makes people believers, essentially.
SPEAKER_01It's a great tip for anybody who's listening that is interested in being like an AI consultant or anything. Uh sure they want help with you know repetitive tasks and things like that, but our recommendation is very similar, Jake. It's like start where they can see an ROI impact and then the rest of the stuff becomes easier to get a yes from. But if you can impact sales, they get very enthusiastic about AI. Um one of the I think I don't know if it's a misconception or misuse of the term for sure that exists is you know, uh I've been involved in generative AI as long as you have and agents aren't a new word, uh but I think the uh the definition or the expectation of what an agent is. In the past I've seen people that were uh building automations and calling those agents. What is your definition of an agent? I mean, obviously limited to the capabilities we have now, but what would be your definition of it based on the difference between an automation and an agent?
SPEAKER_00Yeah, so I would say the the main difference is being able to like dynamically make decisions um and call different tools without having a sort of planned out workflow. So it's like sort of the prompt, then in a way, is their workflow. So it's like agents are able to, they're they have memory, they're able to reflect, they're goal-oriented, and have tool usage. So these are all things that humans have. So if we go back to even before AI, there were automated workflows, you know, those that existed for a long time. But you kind of have to think out every sort of step of like, let's say you're using Maker's Appier or something like that. You sort of like think out the path that it should go through, and it can work really well. And especially when you're using, because we do a lot of custom software, you can get a lot more in-depth and help with the sort of like decision making uh through the workflow. But the main thing is sort of just giving AI a goal and it being able to like extrapolate, okay, if you're like, hey, uh, check on this lead and see where they are in the pipeline and then make the next step. That's pretty vague, but it's like you could tell a human that. And if they're trained on the job, they'd be like, okay, well, what should I do? Probably check the CRL. Okay, let me go find the lead. Okay, it looks like they were supposed to have a, they're on vacation, they're going to be back next week. The next step would be scheduling a call. Well, let me see. Looks like the call hasn't been scheduled. Okay, therefore I should schedule a call with them. How should I do that? I should email them, reach out, say, hey, uh, hope you had a great vacation based on the context of what went on with the lead. Do you, you know, do you let's let's have that, let's have that follow-up call. So you could say that to a human, but that would be very hard. Like in a workflow, you could make a workflow for that, but you would have to anticipate that, you know, that chain of events happening every time so that there's a way for it to go. Or it might just be very templated of just like, you know, you could do like follow up with this lead and it could send like a templated email, just like, hey, just wanted to see where we are. But it's like kind of being the goal-oriented, deciding what tools to use, and then being able to like dynamically shift its path based on the information that it's receiving back from them. So, like, what if it said check on the lead, they already had a call schedule, then therefore do nothing because that was the next step. It already happened. So that's kind of and then being able to reflect on what it has done, go out and find information, and then change its course of uh action based on that information that it has. So that in a very um in a nutshell is what I would consider an agent versus a workflow.
SPEAKER_01So any business owner or team leader or any anything like that that was listening to that, they might hear that sounds like a human. Right? And you're saying that that capability exists currently with the in the right hands to to build it out, obviously, but businesses could have that type of decision making and dynamic interpretation of next course, you know, next step in the in the activity automatically decided without the human necessarily needing to say, okay, now do this.
SPEAKER_00Yes, yes. And it's there, it's kind of the same thing because they're not the same thing, but there's there's two parts to it. There's they're like tools slash workflows, and then agents. So a lot of times what we'll do is because uh a lot of like agents will often call automated workflows to complete actions or just call straight tools, okay? So a lot of times what we'll do, like to lay the framework of like what we do for our clients, is we would start off with like usually AI automations to sort of be like, okay, let's make an AI automation that takes the context of a lead and sends an email, you know, just writes an email and Gmail and saves it. Okay. That's a sort of automated slash AI workflow. But then really what we do is like as we add more and more, we start adding them to like a, we'll have like a manager agent and then like a bunch of sub-agents. And usually we'll have them communicate through Slack. Um, it's a very good channel for that. A lot of people use it, and you can have your whole team in a group chat, and then you can just ping the manager agent when you want something done. So the idea is that we sort of start automating all of these parts here that they can use immediately. But then as we continue, we sort of tie them into more agentic workflows. So it's like this one, this one might manage the CRM and be able to make actions in the CRM. That's cool. It's an AI workflow. And you know, it might be an agent itself, but the cool thing is when they can all interact, and then the manager agent is like, okay, well, we got to find out what's going on with the lead. Let's query the CRM agent. Okay, cool. Now I got the information back. Now let's query the email agent. Okay, now they said that they're free on Tuesday at 4 p.m. Now let's query the calendar agent. So you can have like AI and automated workflows are the base of it. Then those go up to different agents, and it's better for agents to focus on like a very small amount of specific casts because although there are some like Google models where they have context windows of a million, two million tokens, in action that doesn't, that doesn't mean that you can fill it with that much information and it's accurate. It is proven actually that it's very inaccurate. And we can even see that just from testing without doing a million tokens. So then you have like your worker agents, and then those are best to go to a manager agent where the main job is it's pretty simple. It's just like, what's the task? Break it down, which agents do you tell to do that? And then they go and do the stuff. It queries all the agents, they go and do the actual work, let's say, and then send the information back up. So then at the end, like there's not really, and I think a misconception that a lot of people have is that you're gonna go, you're gonna have your own specific company needs and how it operates and everything, and you're just gonna go online and buy for $20 a month the business running agent that's just going to do everything for you, figure out everything, and grow your business. And it's like that doesn't exist now. And I don't think it's gonna exist for a while. So it's like it's really built on workflows, automations, and best practices, data, sub agents, and then a manager agent. And that's really where you can actually see the benefits of like, oh, okay, so this it does, it can exist, but you're not just gonna go online and click download business running agent. It's just it's not chat GPT, it's too too in-depth for that.
SPEAKER_01So for anybody listening, one of the things that helped me kind of understand what Jade just shared was it the agentic architecture looked like an org chart. And everybody knows what an org chart looks like. There's a level of decision making, then there's a level of action, there might be a level of action below that. With each individual on your org chart having a role. The agentic architecture that I've seen for some of those, it looks exactly like that. Like you mentioned, the manager agent. The do you have is there a specific lexicon for the different layers of the decision making?
SPEAKER_00Um probably. I just call them the manager agent, sub agent. That's just kind of the terminology that we go with. I've heard that one commonly used or worker agents. So yes, if you probably if you look it up, there is a technical definition of the official language. Yeah. I just kind of I always relate things back to business because it's like, and that's another thing with like working with clients and the business aspect of stuff. It's like, you know, to really see the effect, it's like you need to have like the technical knowledge and then like the business knowledge as well to see good effects. So I'm always relating things of like sort of translating AI terms into like how does this make sense and how can someone picture it in their head? Because if they can't, you know, if I use really technical jargon and stuff like that, they're gonna be like, I still don't understand it. I don't see how it helps. But if you're like, think if you have a manager and then the manager has all these employee agents, and that's what you're gonna get. And then they're like, oh, that makes sense to me. So that's what I call it.
SPEAKER_01So the activities that are occurring at the manager level, the subagent, and then I guess even a sub-subagent, what what is the difference between how they're behaving at each level?
SPEAKER_00Yeah. So the first one, it would be like, let's say that you're in Slack, okay, and you just want something to be done. Let's just use the same example, the check on this lead and make the next steps, okay? So you would then, through Slack, you'd be querying the manager agent. Um, and then from there, the manager agent would decide of like, you know, because you explain to it, you explain to the subagents what tools they have and what actions they can take and how they work. You explain to the manager agent what the subagents can do and what tools they have and what goals they would accomplish, but at a simpler level, because you can just say calendar agent takes care of anything calendar and scheduling related. You don't have to explain to it, it has access to Gmail or a Google Calendar create event. Like it doesn't really matter. So it's kind of you just break it up like email agent controls the email, CRM agent controls HubSpot, calendar agent controls calendar, that you know, and and kind of just break it down there and then just have it sort of come up with a plan and then be like, your job is to then query these agents. So based on the task coming in from the user and your knowledge base, which ones are you going to query? And then it kind of has to give them a short, like, hey, uh HubSpot agent, uh, check for this lead and see what the next step is for them. Uh, okay, here's the next step. Okay, Gmail agent, write them an email based on this context. So that's how it would work on the hierarchy of things. Another thing we're experimenting with right now and seeing pretty good results because in because um right now we're not using O3 Mini for like the actual agents, is it's not always necessary. I think that it would be very good for that. But right now it's more of like just for reasoning and not so much for like tool calling. But doing a step before so query comes in and essentially treat 03 as like the manager slash planning agent because it reasons very well, and then bump that query over to the manager agent, which then it kind of just relieves that one, which is which we're we would do on 4.0. Um, you could even do it on 4.0 mini because if 03 does the reasoning, passes it to 4.0, all it's doing then is tool calling to the other ones. So that's an interesting way to incorporate reasoning into it. For most tests, I would say it's not absolutely necessary as 4.0 is pretty smart to build out plans, but it's just, hey, it's half the cost. Okay. It's very fast and it actually reasons. So, you know, you might as well, and you get really good results. So, like planning and reasoning is like that's pretty big for the manager agent. And then when that gives instructions, you could have it also write structures like, so what should be done, and then what should be the instructions to each sub agent? And then it can just pass that on.
SPEAKER_01So for this manager, so first off, one of the things that that we do in our chief AI officer certification that people really love is we dive into process mining and process mapping. Obviously, the steps of a process for automation at least need to be very clearly defined so that that automation can deliver high integrity, consistent output as if a human would, if not better. Um So beyond the process idea, let's say, let's say it was a role for uh an HR coordinator. That's something every you know a lot of companies have, uh the HR person. When you're doing this, do you need to know what the HR, like do you need to have a process map or or to be able to do? So you guys work, that's a big part of what you build.
SPEAKER_00Yeah. So what we do when when we're working with clients, we always mix like consulting and AI because it's just like if you're not understanding their actual problems and workflows, then there's, you know, it you're just making guesses. So mostly we focus on the sales side of stuff, but that can also touch like client success, marketing, HR, somewhat. Um, so that's that's really where we we spend our focus. But yeah, so what we do, um, as you know, is like our our sort of scoping call where we sit down and we actually take like 90 minutes to like, okay, what like what's the biggest issue to tackle right now? And like how does that process actually work? And so then we would dive into the sales process of like, you know, how many SDRs do you have? Are they making calls? Are they following up? Are they doing pre-call research? Or, you know, are they are they actually logging this to the CRM? Are they using it properly? Um, and then seeing where kind of like where's the bottleneck, where's the inefficiency, and what could be more automated and done better? And then from there we have that whole process, and then we can analyze from there either like, is this process actually good and where could it be better with a few small tweaks? And then where should AI be added and where should it not be added? Because a lot of people come like, well, I want AI to do cold calls and close deals for me. And it's just like Me too. That's yeah, it doesn't everyone like, yes, I so we joke about it. We're like, yes, we have the infinite money printer AI, and we would just love to distribute it to everyone for a very low cost because why not? Why would we not you just use it for ourselves?
SPEAKER_01Uh yeah, this is interesting because I encountered the same thing. We talk to clients on the service side of our business, and they they're interested in AI enhancing their uh their operations, but before you can even start with that, you have to make sure that the process isn't wonky. Like you referenced that, you know, the 5% close rate example earlier. That's like fix something else before you introduce AI because that there's something missing there. So I think that that's a big takeaway for any of the listeners that uh are excited about deploying AI. Um, you may have to do some groundwork that has nothing to do, or not AI, but agents, you may have to do some groundwork that has more to do with like how you guys are operating your business than it does with anything to do with AI. That's a great point. So um, listeners, there's no miracle. You might have to do a little work here to get this agent to work.
SPEAKER_00Yeah, there's uh there seems to be a misconception. Unfortunately, some people see AI as a way of like, oh, I want it to, I want it so I don't have to think and I can be lazy. And it's just like, you know what, no matter how good AI gets, you're just not gonna be that successful in business if that's your whole idea. It's like, you know, it's more for like, oh, I want to like think of higher level tasks and don't want to spend my time on, say, customer service calls where I'm not really thinking too much and it's just a waste and repetitive and whatnot. A lot of times they see it as like, oh, I don't actually like know, know how to run this part of my business. I don't want to figure it out. I don't want to have to, you know, do trial and error and figure out a better sales system. I just want AI to do it for me so I don't have to think. And it's just like that's using it the complete wrong way. If you're using it to be lazy or not figure something out, A, you're not gonna get a good result from it. It's not gonna figure it out. It might try and you would have a very mediocre or poor result at best and you've really made no progress. So uh yeah, we try to avoid people with that sort of mindset and look at people who are like, you know, experienced, serious business people that are more like, I want it to do repetitive tasks that don't take a lot of thinking so I can do higher level work. That's kind of the idea.
SPEAKER_01So if if anybody pays attention to any of the social channels that have AI as as a topic, uh whether it's Instagram, TikTok, X, doesn't matter. We see a lot of people making posts about like, oh, this agent, right? They're loosely using that term. Um is is what I'm seeing on those social channels where somebody's got, you know, four or five activities or something, it's not terribly sophisticated. They're building it in eight and or make, or probably more like innate n is the ones I've seen that have had, I guess, uh the most robust capabilities. That's not what you're talking about, though.
SPEAKER_00Um I mean, what do you mean? Agents versus workflows again, or what?
SPEAKER_01The kind of things that like if a client says, hey, we need help with our sales process, we're we're getting good results on the process side. We just want to be able to scale more or scale with fewer people, uh is uh that's not the kind of thing that they can necessarily find on a five minute TikTok video.
SPEAKER_00Yeah, no. I mean, technically for those, because there's there's a lot of misconception. There's people that will confuse like AI workflows and agents, but then there are also sometimes people will be like, well, that's a workflow, but it's like Like agents don't always have to be super complex and technically will still satisfy the definition of agent because it's using AI and intelligently calling tools. Like anything like you know, the uh open AI assistance API, you give that a tool or two, like technically that's agentic because it's dynamically using AI. So it's like they're you know, I can't really come down too hard and be like, oh, those aren't agents, they're not agents. It's like, yeah, no, technically they are. If it's using AI to intelligently and dynamically call different tools and make decisions, that technically suffices as an agent. So, but yes, they are not on the level of like, are they going to be incredibly useful? Probably not so much, to be honest. If you, if it can be, if if you see someone make it in five minutes or less, I mean it's probably going to be a very simple agent, which not saying there's no merit, because like I was explaining, a lot of times you need to start with the simple and fundamentals. Like, you know, you a lot of times the clients, it's like they need uh part of their part of their sales process is sending emails. They need to have an email agent that can understand context and dynamically decide whether to read emails, reply to emails, draft emails. So even though it might just be AI using Gmail, it still suffices, you know, that's an agent, okay? But what makes it more useful is when you have the whole team of agents, which is very complex, and you will not see a lot of that in simple Instagram, TikTok videos. Usually they're looking at a very basic agent where it's like, this one writes my social media content. And it's like, cool, but literally you can do that with a fine-tuned GPT. Like that doesn't that doesn't need to be agentic. It could be, but it doesn't really need to be, and nor is it that like amazing and impressive. So really the combination of workflows, AI automation, agents all together, and then tied to one agent is where you actually see really good results.
SPEAKER_01So for everybody listening, I think that we've just busted the myth that there is the hey, AI, go and handle all of my marketing. Like that that agentic build is not for sale at this point. Um so it's interesting then. Tell me if I'm on the right track. I if I'm seeing something like maybe this guy's got one about uh writing social posts, and this one's got something about, you know, creating I don't know, a video through one of the tools and that sort of thing. Individually, they're kind of, oh, that's fun, that's uh interesting. Not a lot of impact. But what you're suggesting, and the architecture that you guys do is that let's say it was a marketing uh agent, that there might be like dozens or or ten or whatever of those. Here's my subagent that does posts, here's my subagent that does the uh images in ideogram, here's the but there's still that coordinator level that is taking the input from the sub agents and it's making a determination on how it either assembles or what it uses, or it's making like an additional layer of judgment, almost like human in the loop would.
SPEAKER_00Yeah, yeah, essentially. I mean, it's kind of like an agent in the loop, if you can say that way. And you can you can still integrate human in the loop to those as well, because you can have it sort of make it as, you know, it's like do part of this process, check with me on the results from the subagents, then continue. So that's a cool thing with the prompting too. You don't have to readjust the workflow. You can just be like, your prompt is essentially your your dynamic workflow. But but yes, you got it exactly right. Of like a lot of times, people sometimes people will portray these complex multi-agents in a very simple way, just for like, you know, views, attention. Like it's part of marketing, sure. Like I'm sure you've seen it. The biggest pretty much anyone puts out a YouTube video titled, I made an AI agent to do anything, and you're guaranteed like 100 to 500,000 views. And it's just like, and then what it actually does is it sends messages in Telegram and Gmail, and it's just like cool, like that's not everything, and it's nor is it that useful. It's just like it's certainly cool, but it's uh such a clickbaity thing. However, a lot of those, like if you take that concept, oh, the AI agent to do everything. So I think a lot of people like that, that's what I've been looking for. I just need one to do everything. But really, it's this whole team of tools, workflows, AI automation, and then sub-agents, and then like a main agent. So technically, yes, it is an AI that does whatever you make it to do, which is not everything at this point, but I mean, hey, good for views. But yeah, that's that's kind of the idea. If you see anything that's really complex, I would say 99% it's a multi-agent team working together.
SPEAKER_01Okay. If you're enjoying this episode and want to learn more about how to start using AI at work, we've made it easy for you. For just one dollar, you can have full access to the Chief AI Officer community, which will give you additional training, custom software, daily training calls on AI tools, using AI automations, getting more from your Chat GBT sessions, and the business of being an AI consultant. Simply go to chiefaiofficer.com forward slash insiders to accelerate your AI journey. Now, back to the episode. So these guys, because I've seen those, and it's like it was this you see the diagram and you're kind of like, you're like, oh my gosh, that looks but they're like, watch, I can talk to Telegram. It can check my email and tell me that there's an e like, even though it's very sophisticated looking, the application that they're using it for isn't. It's basic things that might certainly didn't need uh to be over-engineered to that level. So it seems like some of these uh experts out there, they've got the idea down, but they don't have the application down necessarily. Like a business doesn't care if they can talk to their uh telegram channel and find out if they've got an email and who it's from. But they do care if it's something like what you talked about earlier with the that sales environment or setting the appointment. Oh, I'm checking the notes from the contact record. It's saying this. Let me go to the calendar and book it for when they're on the okay. This is helpful. So as you're obviously paying attention to what's going on in the the agent uh chatter that's going on in the space, um where do you think people are having the biggest uh the the wrong assumptions about is it is it that that I can do something and it's everything does everything for me, or what what are kind of some of the things that you see people making mistakes with when they're talking or thinking about using agents in their business?
SPEAKER_00Yeah, so I mean there's there's definitely quite a few. I have to say, so there's not, I think a lot of people see AI as like, well, it should all be like Chat GPT where I sign up for a very low cost and it this should be more useful and instead solve all of my issues. But really, where it gets useful is like you kind of have to either build it yourself or have someone build it for you because it's just not to the point yet where you can tell AI to build a team of AI agents and it actually does it and they're actually useful. That's what I should make, that the next billion dollar idea, but it's uh it's not there yet. So I think that it's like, you know, when when there's expecting it to be a very simple answer to a very complex solution. And so, like you said, when people envision, oh, the AI to do all the marketing, technically it does and can exist and it can be built, but really it's just like either you use off-the-shelf tools and you get the same marketing strategy as everyone else, and it gives you very bland AI GPT answers, and you think that it's so great because oh, it made your whole marketing plan for you and did all that, but it doesn't really play out that well because there's no expertise in there, and you didn't even know if you don't know the process in the first place and want AI to do it, you're just gonna get limited results. So I think that that's like, you know, thinking that they're going to go to someone who's just going to be like, I actually do have the agent that does everything for, you know, chief AI officer. I actually have it right in my back pocket and I can give it to you like that. Just give me, you know, a hundred bucks a month and I'll just snap my fingers and it's installed and it's gonna do all of that. Like, and I think it's kind of crazy because it's like, if someone did tell you that, would you believe it? Like, I have an AI that will actually understand your workflows and do all of it, and it doesn't need any customization or building. So I think the idea of like, you know, when people like, oh, so here's my exact workflow. I'm using Zoho and Trello and Asana and Outlook and all this, and then it's like, and they'll be like, oh, so I here's what I want to do. And then we'll be like, yeah, can we we, you know, that's something that can be built. Like, here's the timeline, all that sort of stuff. And then, like, oh, you don't already have it? I thought you guys made agents. It's like, we do, but it's like you have to customize it. It's like there's a lot of customization. So it's like, and would you really believe us if we said we actually have that exact agent right here? It's very cheap and it will be deployed in one day. Like that it just doesn't make sense. But I think very non-technical people think that, like, oh, if you make agents, like you should already have the agent that I thought of before this call already built for me and ready to give to me. And it's just like that's not if you even with AI, it does make things better, um, smarter, more dynamic. But it's it's just not a snap of the fingers, like, oh, I'll just go, boom, type into Chat GPT, make me this, and then there it is. It takes a lot of work. It a lot of times complex problems cannot be solved with no code tools either. So it's like it takes software developers, it takes mapping out your exact process. Like, for example, so we have this uh client and they want an inbound lead agent. Seems simple enough. You can build similar inbound lead agents with no code tools, even. But then what it comes down to is these very specifics of like, oh, their form for the inbound leads is actually a custom HTML form. Okay, do you need to have developers connect it? Oh, their their channels that they communicate on are not really commonly used in the US. It's like Telegram, Skype, you know, stuff like that, where it's like, and then it's like, oh, Skype doesn't really have a very friendly API if you go outside of Azure. So it's like all these little technicalities, and then like, okay, then how are you going to store the conversations with a custom thing? So when we create a new thread with a new client, we have to pair the thread ID to the contact and hub spot. So you get down to all these little technicalities where it might seem very easy and like, oh, inbound lead agent, I'll just put it in this no-code chatbot builder and boom, boom, boom. But when you're actually solving very custom and complex problems, it's like it's it's not really that easy.
SPEAKER_01So uh you've mentioned prompts a few times. And when most people when people think about prompts, they think about going into Chat GPT and giving you a set of instructions. And are you saying that the agents, let's say at any level, really, are some of them running on what uh like a prompt as most people understand it, or are they running on uh like uh a coding language for decision making? Or what's what's what's that uh scenario?
SPEAKER_00Yeah, so you you still use like you still use prompts. If you're using AI, there has to be a prompt, even if you are doing it through code. So it's like, and that's another misconception, just to add quickly, that people will be like, when you explain like agents are just code and stuff, people like like their head explodes. And it's like all of it's code. Like if you use Zapier make relevance, it doesn't matter. It's all code. It might have a cool UI for you to look at, but at the core of it, it's all code. And really, code is the most efficient way for them to run, not through like UI, it's just for people to see it so that they can envision it. So, anyways, it it to answer your question, yes, they always need to have a prompt. However, the prompting is different for agents rather than like prompting chat GPT. And a lot of people will think, like, oh, I'm good at prompting chat GPT, so I'd be good at prompting agents. But it's a lot different because with Chat GPT, A, like it learns from you, it remembers stuff about you. So you can give it a lot less context and it can come to a conclusion based on you using it for a long time. Another thing is that when you're prompting agents, not that it's like one shot necessarily, but is like with ChatGPT, you could go back and forth five times and be like, well, I got a good output, I know how to prompt ChatGPT. But realistically, if it took you five times to do it, you're not as good as you think. Like, you know, it should be with agents, you need it to consistently and reliably execute a task in the right way every time that it's run. That's a good agent. And that takes a lot more in-depth prompting and thinking of like a lot of edge cases, really, is what it is. So trial and error, thinking of edge cases, your experience of building other agents and knowing what the edge cases and where AI is likely to go wrong, and then writing that into a prompt that every time it's executed will do the right thing. So it needs to be broad enough, like think of the prompt as your agent workflow. Remember, we're talking about workflows, but this it can be a more dynamic, but it still outlines the workflow of why it should make decisions, when, what it should do, what it should use. Here's an example, etc. So it needs to be long enough that it gives it a full idea of like when to make decisions. But also another misconception that people have is they go to Chat GPT and say, I want an agent to do this, and it writes out a prompt like this long, and it's just like so much fluff and so much like just like random uh markdown bolds and stuff like that. And it's just like you know, unnecessary. It's over-explaining, like, you know, the section for tone is like two paragraphs long, and it's like, dude, you don't need to explain an agent's tone for two paragraphs. Like it's you know, it that doesn't make sense. So that's kind of definitely a misconception. Like, if I'm good at prompting chat GBT, I'll be good at that. But what it comes down to, yes, if you're using agents, even if you do it through code, you do need prompts to explain because that's how you communicate with AI. So that never goes away. But you can you can use both, they can exist in the same flow.
SPEAKER_01So for anybody out there that's doing the back and forth and thinking, hey, I could I could give the instructions for an agent. It's not as easy as you going back and forth asking it about you know what can I have for dinner. So I'm particularly interested in the acceleration of the space. Uh six months ago, or well, I guess uh maybe June of last year, I started getting really interested in the concept of agents, but not necessarily I'm not a builder, I'm not uh anything like that. But uh the because you know, my my perspective is that when GPT uh GPT 3.5 came out, uh people were afraid that AI was going to take their job, that 3.5 was going to take their job. And then once you get under the hood and you start using it a little bit, you realize extremely helpful, but it needs me. Right? Like it it's not gonna take my job, it's gonna make allow me to do my job better, or somebody who knows AI plus my job replace me. That's more likely. So I think the people were expecting uh like you've talked about the one agent that does everything and that that's an AI, which in theory is, and in practice we're seeing, you know, some examples of for sure. But it's uh six months ago was a whole different world than it than it is now, and it continues to accelerate. What are you expecting to see happen in 2025? Because you'd look at any list about you know big breakthroughs in 2025 or AI's impact in 2025, doesn't matter if it's Microsoft, Wall Street Journal, they're all talking about agents. And uh you being, you know, neck deep in this stuff, you know what's possible and what isn't, but you also know what's being developed or what's being kind of hinted or promised that's coming. What do you realistically see agentic capabilities looking like, I don't know, by summer, by the end of the year?
SPEAKER_00Yeah, by I guess exact timelines are hard to say. I would say that the future is like how every software is integrating their own AI tools. So that's how it started off of just like, you know, simple, like, oh, it writes an email for you in HubSpot and stuff like that. And like, you know, very simple, like they're just just using an LLM essentially. So I see, I mean, we already know that like Agent Force is coming by Salesforce and um HubSpot will have one. Like I think that what will make them the most effective because you can't just guess everyone's workflow, and also a lot of these software tools don't really they want you to just keep everything in their tool. They don't want you going elsewhere and they don't really want it to interface. So there, I think that every major software is gonna start, you know, they have like even in Gmail, it's like, oh, help me generate this email. But I think what they're gonna start doing is then having agents within their own platform where it's like, if you know, HubSpot builds an agent for HubSpot, it's likely going to be one of the best for that because it's going, it's they're it's gonna have access to all their API endpoints, be able to do whatever you just tell it to do. So I think there a lot of these tools will go more to like chat with the tool. Like you won't go into Gmail, start a new email, put in the senders and stuff. You would just type or speak to it, just like, hey, uh, can you message Chris and follow up on my email from last week? And then it's like, okay, boom, let's find Chris. Okay, boom, here's his email. Let's do it with the email from next week. So that's how I see it going in the in the near future. Um that yeah, it there you'll have good agents in every platform, but they'll only really like control that platform rather than do like an entire workflow. Um, I think with the reasoning models and stuff like that, that'll be really interesting to see because that's kind of the main point of why agents are good, because it's like they do some of that behind-the-scenes reasoning rather than just like question answer. Like that's only somewhat useful. So reasoning models just take that a step further. So I think that using those, once you can use like, let's say, O3 Mini uh to call tools and read files and images and stuff like that, they make it like multi-uh modular um or multimodal. Um, it will be it'll it'll be extremely useful and it can handle more complex tasks. What those tasks are, it really depends on what you're trying to achieve. Um coding ones will get better. We use some of those if you've heard of like Devon. Um the yeah, we use that. Uh and it just I mean, the better the models get, the better the agents get. So it's like, you know, in the agentic framework itself, it's like, yeah, teams of agents are good. I think those will become less relevant as the models get smarter and have longer context windows that they can accurately pull from, um, and that they're better at reasoning. So I think that you could use fewer agents to accomplish the same thing. You could use the same amount of agents to accomplish more complex things. Um, but yeah, actual like end goal of like, oh, what is not possible today that will be possible then? I'd say probably the biggest one from my perspective is just like sort of the just coding in general, because like right now it's like we'll use stuff like cursor and Devon and stuff like that. So like Devon will actually run in a virtual machine, go open up its own, like, let's call it like mini computer, and it does, you know, okay, read API documentation, write the code, but it's not even as good at it yet, I would say, as like a junior dev. Um, so it's like, but as it there's more reasoning, longer context windows, et cetera, it will just be able to do more like one-shot stuff where it's like you see stuff like um bolt and what's that other one, like lovable or something like that. And so it's like make an app from a prompt. And it's like technically you can and it's cool, but really those are focused on the front end, which is like most appealing to people because oh my gosh, I can see it. I made an app. Then you look at actual functionality, the back end, how it's uh architected and all that sort of stuff, and it's just not not really that like commercial. Yeah. Yeah. And same thing with like Devin, you can't just be like, hey, make me this iPhone tracker, uh, habit tracker app, blah, blah, blah, one-shot it. It's just not gonna happen. So I think that there will be just be more stuff and like it'll go in baby steps, like, oh, next model, Devon will be able to do a little more, a little more, a little more, to the point where it's actually at like junior dev level, where you say, like, hey, here's uh, you know, you're you're in my GitHub, here's our project manager. So you understand the goals of what we're doing. Here's like an SRS document of what we want to achieve, ask any clarifying questions and then go do it. And then it just like can actually complete that and you know, uh do a full job rather than like parts of a job. So I think it will get there. And, you know, not that like, oh, AI is gonna replace everyone's jobs. I think that people that currently I think it's gonna make it more difficult for younger generations, unless you're very good at AI, to get a job. But I think that the people that are currently in the workforce that I don't think they're just gonna get fired because most people, I mean, let's admit, a lot of people, even like CEOs, C levels and stuff like that, don't know how to use AI at all. So they're probably not gonna start firing their team because they don't know how to use AI because they don't even know. So I think that it will it will make people, younger generations, more hireable if you do have that skill. And I think that there will be a large gap there because I think that rather than just firing people's whole teams, they're gonna start over the next five years slowly integrating AI. So they might just rather than fire people, hire at a slower rate. But that can also, in turn, if you have that skill and you go to a company and you're like, hey, I'm a data analyst, also I'm I can build agents and I'm an AI expert as well. Then it's like, wow, double there because those higher up older people, like, we don't even know how to do that. And they're like, yeah, I can get five times more done than you using AI and I can show you how to do it too. Then it's like, wow, massive value. So I think that's where the kind of the gap is gonna be, not that everyone's gonna lose their job, but it will be an essential skill to know, like like using the internet. Like you wouldn't hire someone that didn't know how to use the internet. Sure.
SPEAKER_01You know, so for those of you listening, like pay attention to that. I think that's a really strong point. And that if you're good at what you do and you also have AI skills or certifications or training or whatever, it's not just about you coming in there and doing your job with AI, but if you can teach others on your team how to do it, you're lifting the whole boat. And that is very appealing to um that the hiring manager or whoever's making that decision. Um one of the things uh you know, I we we hear a lot about uh with Salesforce and their agents and HubSpot and their agents. And I'm always skeptical um I come from a financial publishing background, so I know the Manigans that go on on in publicly traded companies. Um do you think that these companies obviously they know they need to do it, but do you think that right now they're that they're they're saying it more for um not necessarily capabilities, but more for hype about like, oh, we're agent forward or whatever?
SPEAKER_00Yeah. Yeah, I think it's more for hype. I've um definitely heard that, you know, although because agent force like exists now, but it sounds like there is uh some trouble in getting people to adopt it, essentially, and trouble rolling it out, which it's like, you know, is that gonna stop them? No. But I think of right now it's like a lot of talk and not a lot of like let's say case studies. Like this this isn't this isn't actually something that all these companies are running right now and like, oh, we're seeing amazing results. It's more like conceptual and it's like to be expected too. Like you're getting into a new sector of a new technology and stuff like that. You're not gonna immediately perfect it. So, you know, it's just how it is. Just like, you know, AI, like GPT 3.5 existed for quite a while before all these companies started to now add their AI tools into all of them. And it's then the same thing with agents, they're gonna exist first, and it's like six months to a year, and then we're gonna start seeing like, oh, okay, now all these have agents in them. So um, yeah, not that not like they're lying about it or they don't have it. I think that it's kind of just being worked on and more conceptual for a lot of companies rather than like actually in being used in practice and like deployed for customers.
SPEAKER_01So well, here's a question. If let's say I was on Salesforce, uh knowing what you know, would you say, and I would imagine that there's a premium being charged for access to Agent Force, would you uh recommend companies, I don't know, like you you don't you're not uh part of Salesforce's team or anything like that, but would you recommend companies uh be early in on this stuff to take advantage of even though it's gonna be bumpy and there's gonna be, you know, uh whatever uh uh misconfigurations exist today are gonna be gone tomorrow. Would you recommend that companies go ahead and get in early so that they can start to change the culture or participate in the learning? Or would you say wait till it's stable?
SPEAKER_00Yeah, I would I would say just start, start when it comes out, just like with anything with AI. It's like it you're not really gonna hurt yourself too much. Like, don't tell the agent to delete your entire CRM, which it probably would have uh parameters against doing that. But it's like as long as you're not like, if you don't overrely on it, think, oh, cool, there's an agent for this now, it's going to do all of my work. And then you're like, oh, why did everything get all screwed up? It's like, yeah, don't look at it that way, but kind of just test it out, see like what things is it good at. Because it might start where it's like it technically can operate all these parts of the CRM, but it's only really good at like pipeline management and sending emails. Okay, we'll use those, get familiar with how it works. And like you said, AI develops quickly. So next week they might have a patch and they're like, this works now. So we were actually just talking about this today on another podcast, my CTO and myself, where it's like, like I was saying about Devin, like it's you know, a lot of people, especially developers, like to like crap on it and be like, it's not good, it's not useful, blah, blah, blah, blah, blah. But it's like, you know what? When it comes down to it, it might save us like 10 hours a week on low-level tasks. But however, it's like sometimes if a senior developer is working on a project, if you're saving 10 hours of their time every week, that's still like you're getting well your money's worth. We're getting like four or five times our money's worth from that at least. So it's like, you know, you could be like, oh, it can't even do that much coding. But it's like, well, things like writing simple JavaScript and API integrations, like the senior devs don't really want to do that anyways. Like they they, it's kind of this like, I hate doing this. So, and then it frees up their time as well. But if you look at it, and then another thing, so um, another person on the podcast was saying, like, well, if you look at it this way, like, even if it's not that useful now, let's say it wasn't even that useful, say it's like five hours a week, just getting to know it and understand it and get better at using it and prompting it. Then when they actually make it better, they're like, hey, now it's using O3 Mini, and it's actually really, really good. Well, you've been using it for this long, you understand how it works. And sometimes it's like how you use it. You can be better or worse at using AI tools. So even if you're the tool isn't perfect that you're using, if you get really good at using it and prompting it, like I was saying about the agent prompting is different than GPT prompting. So things like that. If you get really good at it with a tool that's not really that great, when it is that great, you're gonna be amazing at it.
SPEAKER_01So you already got the learning curve out of the way.
SPEAKER_00Yeah. And there's a lot of stuff, even if they change the model on the inside, there's gonna be a lot of stuff about it that doesn't change and that remains the same. So if you're familiar with all of those and it just gets better, well, you're already familiar then with a really good system where everyone else that was like, oh, tried it, and like, oh, it's not that good, it's not useful, I'm not gonna use it, you know, I don't trust it, whatever, then they're then it's gonna be actually good. All of their competitors are gonna be doing amazing things with it and like, oh my gosh, I should learn this. And it's like, you should have learned it six months ago. Yeah. It's good advice.
SPEAKER_01So, Jake, we're we're getting to the end of this, but I want I want people to understand what they should look out for if they're interested in using agents, but they don't have the in-house capability. That was our situation, that's why we reached out to you um and your company to help us with uh architecting and developing and uh identifying opportunities for agents. What would you say would be some advice for somebody who is looking to engage with somebody like agentic brain, your company?
SPEAKER_00Yeah, I mean, I would say the first thing to think of is like we were talking about before, if you want something really complex done, the reality is just you're we hear this all the time. I could do it with ChatGPT. Okay, why are you here? Why, you know, it's like the thing is it's not going to already exist if it's super custom. If it's solving a high-level problem, it's probably going to take at least a couple months to make, and it's not going to be $20 a month. Like, you know, you kind of have to weigh out like, is your problem worth solving? Um, and then just be wary of people that do tell you that, oh, I have the agent that does everything and I'll deploy it this fast and only for a very cheap price. I would be very skeptical of that because it's like, how would you already, how would that person already have exactly what you want? I think that it's gonna be a long time until agents can build other agents to solve very custom and complex tasks. So that's that's kind of what I would say is kind of consider like, and if your problem is super custom and not that high value, maybe it's just not worth solving. Like if it saves you $1,000 per month, but is extremely complex and custom and stuff like that. Like to be honest, there's some things that just aren't worth solving with AI. However, if you have something that is really complex, let's say, for example, a client we just worked with, they're like, one of the things it takes 25% of their time because they're doing um like insurance per um like like condos, essentially. Uh, just data entry, moving it back and forth takes like a quarter of their time. So again, is it kind of custom and like I wouldn't say insanely complex, but a little bit custom, a little bit more complex than you probably do with no code tools? Yes. However, to get back 25% of your time is massive value. So it's well worth it. So kind of look for that ratio of like if it's you know, determine how custom and complex it is, and then look for things that are going to provide a huge value to you, then go look at getting custom solutions made for them. If it's very simple, not a high value, maybe you know, just find a tool that can already do it. Um, that would sort of be my advice. But if you're yeah, looking for looking for big complex processes handled, that's when you would want to actually have custom agents for yourself. So do you guys have bandwidth? We do have bandwidth, yeah. We're we're growing our team.
SPEAKER_01Okay, awesome. And for everybody listening, uh, full transparency, I engaged with Agentic Brain to do this same process for our own business. Um, we just got through doing kind of a proposed roadmap and everything. And the uh suggestions are very exciting as far as the impact that it would have on our business. So if somebody is interested in exploring this for their own business, what's the process for them to uh connect with you guys and do go basically basically go through what I'm going through?
SPEAKER_00Yeah, so you can either go to our site, agenticbrain.com, or you can always contact me on LinkedIn. Um, if you want to drop my uh LinkedIn URL as well, somewhere connected to this as well. Um, that's the best way to get into contact. There's a calendar on our site as well. And um, you know, what we really do on the first call is help people determine like, because to be honest, we don't want to work with everyone and that kind of ratio that we're looking at. If your job is very expensive and not high value to you, a lot of times we, you know, we can we help determine that in the first call of just like you might have better luck using these tools instead. Custom agents might not be for you. But then also there are some when it's just like, oh wow, this would have such a huge impact on your business. It's definitely worth moving forward. So that's you know, that's kind of our process of what we help people determine on the very first call with us.
SPEAKER_01So for those listening, like I'm writing working with a whole lot of agentic companies, but I don't know that we need to. The uh experience with a gentic brain has been what I was expecting and what I was hoping for. So um endorsement for reaching out to Jake and his team, especially if they have bandwidth. I think that if your experience is like mine, uh this won't be a mystery anymore. You'll have a very clear path forward. So, Jake, uh thank you. So I'm obviously, you know, I appreciate it. Sounds like your busy podcast day, but I appreciate you taking the time to share with the audience here using AI at work. And um, we're gonna include obviously the contact information for the site. Um, I want people to follow you on LinkedIn uh and see the type of stuff that you're doing. And uh for those of you that are in our community, um, I'm gonna be talking over the next few weeks about some of the suggestions that they made and just to kind of give you guys an idea of uh what a business like ours, how it can benefit from you know, agents in the hands of experts like Jake and his team. So, Jake, thanks again, buddy. Thanks for having me on. We'll see you guys on the next episode. Thanks for tuning in to using AI at work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor, Chief AI Officer, for empowering businesses with AI education and training. Visit their website for free AI readiness assessment and AI strategy guide to help you get started using AI at work. That's www.chiefaiofficer.com. Well, thanks to our producer, Evan Destaunier, for making this episode possible. Follow us on Twitter at the handle using AI at work and visit www.usingai at work.com for free resources to help you harness AI in your role.