Foundations and lead scraping

0:00 A few common problems with agentic workflows and how to fix them. How to actually build these things. So, idees, setting up your workspace, creating your first flow, the DO framework, directive orchestration and execution, claude skills, MCP and other frameworks, what each one does, when to use which and how they all fit together, how to test and validate agentic workflows, the best system prompts for agentic workflows, which I will give you, how to make your workflows self annealing, aka heal themselves when they air out, how to move out of the IDE and into the cloud.

0:46 I'll teach you how to create web hooks, schedule triggers, and more. How to run multiple agents simultaneously. I'll show you a sub aents and advanced workflow parallelization. And finally, how to troubleshoot agentic workflows when things break. If you don't know who I am, I build two AI based service agencies to $160,000 a month in combined revenue. I've also consulted for a couple of billion-dollar businesses with AI. And I tell you this cuz I want to make it clear.

1:07 Well, you guys are of course going to learn everything from the fundamentals all the way up to the advanced concepts today. This course has a business focus. My goal is to help prepare as many people as possible for what I consider to be the next stage of the economy. So what you will learn today is working right now. It is generating revenue right now and you can use it to improve your own and other people's businesses right now.

1:07 Please bookmark this and use the chapter feature to come back to it or whenever you need anytime. And I hope you guys are excited as I am to get into Agentic Workflows. Let's get started. This is a practical course. The whole point of it is to build and then use Agentic Workflows in real business environments. And that's because building is the most effective way to learn anything. When you build with your hands and get them dirty, you're forced to deal with concepts in a way that you guys never would have if you just sat back and passively listened.

1:51 That said, before we get into the building, and there will be a lot of building and a lot of demos in this course, there are some foundational things about agents and workflows that I'd highly recommend that you understand because if you don't understand them, you're going to commit many hours to this course and you'll only really be able to digest or extract a few percentage points of it. So what I want to do is I want to maximize the ability and efficiency of your time by helping you cover those concepts now.

2:13 And by doing that, you'll be able to absorb the rest of the course a lot faster and a lot better. So what do I mean by concepts? AI is currently in an overhang state. Current AI capabilities are very far beyond what most people believe, expect, or know how to use. If you guys graft this, what we have down here is sort of like the general public's perception of AI, okay? And their ability to use it. And what we have above it is sort of like the reality, okay?

2:37 You guys are going to see a lot of very crappily drawn lines in this course, so you might as well get used to them now. So this gap between the reality of the situation and then what people believe AI is capable of is called the overhang. The reason why this overhang exists and the reason why people are only squeezing out a very small percentage of the actual value of AI, large language models, agentic workflows and so on and so forth [snorts] is because right now most people are using them as glorified copy and paste tools.

3:21 They are basically trying to drink through the Pacific or Atlantic Ocean with a tiny straw. You know, they ask these galaxy brain intelligences. Pretty dumb questions to begin with to be honest. They answer and then all they do is they copy it from one tab into another, which is obviously a very low bandwidth, really bottlenecked way of working. They are not integrating AI into their business like I'm about to show you how to do in this course.

3:44 Instead, they're just dealing with it like a like an external sort of third party thing. Now, obviously, people are figuring out that AI is a lot more powerful than most people give it credit to, and courses like mine are helping them do so. But as they figure it out, the arbitrage window will close. And in case you guys didn't know, arbitrage is your ability to essentially produce some sort of beneficial outcome, revenue or profit, based off of a disparity in knowledge.

4:08 And so, if you know, you know, this and the rest of the market knows this, obviously there's kind of a gap there, right? and the market is willing to pay you to be somebody that solves that little tiny gap. Well, that window is closing because people are learning about how this technology works. But right now, it's wide open and you can make a ton of money with it. So, just as a demonstration to show you how powerful these models are, I'm going to have one in particular called Claude Opus 4.5 do a pretty straightforward task for me.

4:30 This task is to compile a list of five local meal preparation companies that deliver to around my area and then find their email addresses. I'm then going to send each of them emails with specifications from this email. I want uh you know 3500 calories a day, 200 grams of protein a day. I'm doing some big bulk. Do this entirely autonomously requiring no input from me. If you cannot find the emails of at least five, then keep on searching until you do.

4:52 Most people don't realize that models are entirely capable of doing this sort of thing for you and essentially acting as you know an extension of yourself. So it's starting off by searching for meal prep delivery companies downtown Vancouver BC 2025. If I were doing this on my own, this is probably something that I would do as well, right? like very straightforward and logical. You don't need to know how the IDE that I'm using uh works. You don't need to understand the interface or everything.

5:14 I'm going to cover all this later on in the course. And as you can see, it's found me a bunch of meal preparation services. There's Fresh Prep, Two Guys with Knives, Crave Healthy, Fed, Fresh in Your Fridge, K-Bop, and then WellFed. Now, it's finding email addresses of each of these. So, as you can see, it's actually simultaneously running a bunch of searches on their websites to look for email addresses or contact methods. A few seconds later, it looks like it could only find one email out of the four or five searches that it ran.

5:37 So, what is it doing instead? It's now broadening its search. It's going on contact pages. It's looking for alternative solutions. Okay, it's now accumulated the email addresses and like a temporary database. And it's just going through and sending emails. It does so through uh what's called an MCP, model contact protocol server that I've set up. I'll show that to you later. And boom. Now, it is done. So, we've sent five emails. Down here, you can see it said, "I asked each company about custom meal plans, pricing for higher volume orders, and their delivery schedule to downtown Vancouver." We also included the requirements.

6:00 I went through and I actually found the email that it sent. It was something like this. Hey, company team, I'm looking for a meal prep service that delivers to downtown Vancouver and that contains the following requirements. Daily calories approximately 3500. Daily protein approximately this much. Focus on whole foods and healthy ingredients. Interested in learning more? Do you mind letting me know? you know, if you guys offer custom meal plans, um, what your pricing looks like and how your delivery schedule works.

6:47 Looking forward to hearing from you. Thank you very much. So, I mean, like, this is something I realistically probably would have sent myself. Um, is it in my exact tone of voice, honestly? Like, it's really close. This is more or less everything that I would send. There's no AI isms. People on the other end of the line aren't going to know that I'm using AI to do this sort of thing. And it turned a process that realistically would have previously taken me maybe like 20 minutes into something that took me literally less than 15 seconds.

6:47 I mean, I wrote the thing, I pressed enter, and then I went. And what you'll see is with the use of other bandwidth improving tools like voice transcription and stuff like this, you can actually have agentic workflows become more or less your interface for the internet. And I should note that I didn't even use a defined agentic workflow for this. I literally just asked an agent to do something and it was super unstructured and it still did a great job.

7:10 Imagine when we wrap this in the framework. I also want to cover this idea of a river of value. The way I see the global economy is as a giant river. Okay. Now, capital flows to whoever provides value. And essentially what occurs is for many centuries that value has come from human labor, primarily physical to start, although eventually cognitive. And then the more value that people could produce, the more downstream little tributaries of this river we found.

7:57 And so this might be some person that's producing tremendous value, these might be other people and so on and so forth. The whole idea of capital is that as solutions arrive in the economy that are more and more effective, [gasps] they produce larger diversions of this stream. Okay? And so let's say this person Z is using agentic workflows. The idea is over the course of the next few years, he or she is going to consume more and more and more and more and more of that river until essentially he's getting all of it.

8:21 Those who position themselves as people like Z in this case will capture massive flows from the future economy because agentic workflows aren't optional. There's something that are coming and being deployed right now. The last thing I want to talk about is automation in the terms of a Gentic workflow. Now, a lot of people that watch my channel and are probably here are familiar with the idea of automation. They're also familiar with the idea of roles and they've heard a lot of things about how AI agents are coming and their whole fleets of teams that are being replaced and so on and so forth.

8:44 And this is kind of inaccurate. Rather than thinking about agentic workflows, which is what we're going to cover in this course, as being able to automate 100% of one role, I want you to think about it a little differently. I want you to think about agentic workflows as being capable of automating 90% of 10,000 roles. So as opposed to automating 100% okay of one, we're automating say 90% of 10,000 people in the organization. Now if you automate 100% of one role, that's actually pretty valuable.

9:33 Don't get me wrong. If I could automate a software developer completely end to end, if I could automate a marketer end to end, obviously that produces some value in my organization. But agentic workflows, like a lot of technology, have gaps. And so, um, the main issue is human beings tend to always have a little bit more context than these things do, at least right now. And so, even the ability to automate 90% of 10,000, despite the fact that it's not 100, is still tremendously valuable.

9:57 If you just do the math, automating 100% of one person's role is equivalent to basically providing one unit of economic value. Whereas, if you automate 90% of 10,000 people's, you're providing 9,000 units of economic value. As long as you structure your companies in a way to accommodate these things, these things are very powerful. Now, I call this horizontal leverage and it's very, very strong. Another way I want you to think about this is like the industrial revolution.

10:21 Back in the good old days, well, I don't know if they were really good, but certainly back in the day, you had people like seamstresses who would, you know, knit various garments and stitch various things together. And maybe one of these seamstresses could produce, you know, 10 pairs of a specific type of clothing per day. Well, after the industrial revolution, obviously we didn't do a lot of this stuff by hand anymore. We had machines that did this stuff instead.

10:44 So maybe a loom. Before a single seamstress could produce maybe 10 garments a day. After one of these machines could maybe prepare 10,000 garments in a day. That said, it the machine didn't fully replace that seamstress because that seamstress just transitioned. Instead of being somebody that worked with their hands on building the garment directly, they instead became somebody that was supervising whole fleets of machines that did it. Now imagine if in this analogy, not only can we build and use a loom, we are capable of rebuilding that loom in any configuration in seconds.

11:08 We don't have to, you know, smelt the metal and then hammer it and then construct it in a way and screw gears and all that stuff in order to build a machine. We could literally just use natural language. Obviously, that would be a lot more powerful, right? Well, that really is the idea of an agentic workflow. It is something that provides incredible horizontal leverage and we can reconfigure it in seconds to do more or less whatever we want.

11:30 And it's not an exaggeration to tell you that this is a phase change essentially in a company's ability to automate things. So if you guys are familiar with automation platforms, in this case this is N8N, you'll know that most of the time the way that we are currently building automated systems is through drag and drop nodes or modules. And so on the left hand side here, I have a simple system set up. I'm not going to go through everything because it's pointless.

11:54 The point is not to learn a specific automation platform. The point is to learn how to automate platforms in general, but I have a specific automation here that just responds to some emails coming in for a cold email campaign. And as you see here, we have these nodes and they do various things. Some of them do HTTP requests. Some of them do some data processing and and formatting. Some of them call a Google sheet. We have some AI functionality and so on and so forth.

12:17 They're all connected with these lines, which is basically the the flow of logic through a system. And this is hunky dory. It works really well. Well, the new version of that workflow on the left, which obviously requires a lot of time, energy, and understanding in order to be able to to parse and then change is what we have on the right. Instead of dealing with nodes and specific software platforms, we use the universal translation, which is natural language, and then just write it out in bullet points.

12:42 So on the right hand side I have the exact same workflow except I have it set for agentic uh systems and all it is is a list of bullet points. Hey when somebody replies to one of your cold outreach campaigns instantly should send a web hook. The system should look up the campaign in a Google sheet to find talking points and example replies. It should then research the person who replied. It should then generate a short friendly reply.

13:08 If they said something negative like unsubscribe or remove me, we should skip them. If there's no knowledge base, we should skip them. Otherwise, we should send the reply automatically. I want you guys to see that on the left hand side, we had to spend months, maybe years, becoming skilled enough to use a platform to be able to build systems that did this. And on the right, a toddler who has a a rough idea in mind of what he or she wants to do can write it out in natural language.

13:31 And not only can everybody else on a team interpret that, we can also change that at any point. If I wanted to add an additional step to my workflow, all I do is I click click on this, press enter, and then just write it out. and the agentic workflow builder and then eventually doer using a framework I'm going to run you guys through later on in this course will do it and it'll do it extraordinarily remarkably well.

13:54 So that's a very fundamental change in how these things work and hopefully it's clear to everybody here that workflows are no longer drag and drop sort of builds in the concept that we see on the left hand side. They're very much so just like basic logic. So why is all of this stuff possible right now? It certainly wasn't just a little while ago. Well, there are three main reasons. intelligence, tools, and cost. On the intelligence side, model intelligence just crossed a threshold and became very, very good, seemingly overnight, but really we've been working up to it for quite a while.

14:17 Frontier models like Anthropics Claude, OpenAs, Chat, GBT, Google's Gemini, and then a bunch of other ones have gotten really smart. They score around 80% on a benchmark called software engineering bench verified. And this measures real software engineering ability. This is not a crappy cherrypicked demo. It wasn't included in like the training data or anything like that. These are novel problems that are being solved in novel ways through models. And essentially, they are genuine professional grade work that are better than most software engineers.

15:03 Now, I would have considered myself a software engineer a couple of years ago. I'd say my skills have definitely uh deteriorated a fair amount since because I've been focusing more on no code tools and and making money and stuff like that. But this stuff is so far beyond my own abilities as sort of like a mid-level dev u that it's not even funny. Most people that learn about this and they're going to be learning about it pretty soon will think that AI went from, you know, intern level to some sort of senior employee overnight.

15:27 But this is just how knowledge works. Basically, anytime that you have a process and that process slowly gets better and better and better over time, most people don't see until we hit a certain threshold and then it almost looks like it went vertical. In reality, uh it's almost like the way that boiling water works, right? The temperature of water goes up and up and up and up and up and then eventually it boils and then it fundamentally changes state.

15:51 You know, it goes from over here where it's like a liquid to over here where it's a a gas. And although we're supplying more and more energy to this thing, we're not really seeing it change until all of a sudden, boom, it's producing bubbles and getting all over the place. So, I see model intelligence a very, very similar way. So, a lot of people talk about benchmarks. Very few people actually show what the questions inside of a benchmark realistically ask.

16:14 I think benchmarks are for the most part pretty artificial. A much better test of how good a model is is just how good you feel while using it. But it is important that at least we understand how benchmarks work in order for us to really put in context the capabilities of agents. So here's uh one from Astropi. It's a misleading exception message. And basically, these models are so good at coding. Like, like, I mean, I tried to look through and understand what any of these actual questions meant and how to fix them.

16:36 I'd probably be staring at each of these for like a day before anything makes sense. Um, let alone before I get to the point where I could realistically solve it. These models can do this sort of thing in in seconds. So, issue problem statement. Hey, removing a required column from a time series raises a misleading error message. The error claims the time column is missing even when it's present. Instead, the error should list all missing required columns.

17:00 Then it gives you a snippet of code with the actual class time series. Right? So looking at that, no idea what the hell that does. The bug, if flux is missing, error still complains about time. Error message is factually incorrect. You're fix detect which required columns are missing. Report them explicitly. So you actually have to go through and you have to do this with the code. Okay, here's one from sort of like a Panda style question.

17:22 Load CSV silently coerces mixtype columns instead of failing quickly which leads to incorrect downstream computations and then it like provides a list. So, we now have models that are basically capable of looking at a thousand of these and solving more than 800 of them perfectly. I mean, if you gave me a thousand of these, not only would I take like a year, I would probably get at least, you know, 50% of these things wrong. And I'm somebody that has some exposure to this sort of stuff.

17:47 Imagine the average person. And so what I mean to say is that we are essentially empowering every human being on earth or at least we have the potential to empower if we were to actually distribute this technology and if everybody were to know it to the level that you will know it by the end of this course with the powers of like a mid-level to even senior developer in many cases. Another important point is how fast these models can operate.

18:09 I mean this is me asking chat GPT 5.2 thinking to just reason a little bit about the meaning of life. Check out the stream of output that it's providing. But you can go way faster than that. This is an example of a diffusion LLM that it basically immediately processes and writes I don't know how many hundred words, but extraordinarily quickly. You see that we just click generate and then immediately after, you know, probably at least 300 words for instantiated.

18:32 These models can run these reasoning loops extremely quickly behind closed doors. In addition, providers like uh Anthropic and OpenAI and Gemini and stuff have all the compute necessary to run these things like 10, 50, 100 times faster than you are yourself. So just imagine what's going to happen when that level of technology drips down to the rest of the economy. Like to be clear, these models, the ones that I'm using to build agentic workflows, are already extremely powerful and have automated the vast majority of my day-to-day work.

18:56 They can automate the vast majority of your day-to-day work as well or any of the companies that you work with. But imagine the models in 3 months. Imagine the models in a year from now. That's why learning how to build these sorts of workflows today is probably one of the highest ROI skills that you can engage in. The second thing is tool integration is now standardized. So there's some protocols out there like model context protocol which standardizes how AI connects to external tools, databases, resources, and stuff like that.

19:19 I'm going to be showing you guys how to use model context protocol in pretty advanced ways that I don't think a lot of other people have covered in this course. I'm also going to be talking about some of the downsides of model context protocol like how initially it totally blew but now it's uh actually pretty good and well supported so it's it's worth us diving in. In addition to you know those tools through MCP there also some frameworks that have recently come out.

19:41 One is directive orchestration execution. This is the framework I'm going to be using to build and then use our agentic workflows throughout the course. There are also platform specific frameworks like cloud skills for the cloud family of models. these formalize tool calling and you know in case you have no idea what I'm talking about here LLM are really flexible okay which is a great thing conceptually it's great if you want to write poems and write do creative writing and help you respond to emails and stuff like that but a lot of business functions don't depend on flexibility what they depend on is the opposite they depend on reliability so in business we need to standardize and tools are basically just standardized little things that we can use in order to accomplish business tasks I like thinking of it like a caveman that you know, is hunting saber-tooth tigers or something.

20:27 If you're a caveman and you're hunting saber-tooth tigers, and every time you go to a saber-tooth tiger, you're completely empty-handed, what are you going to do? The first thing you're going to do is you're going to be like, "Holy crap, is that a saber-tooth tiger?" You're going to scrge around on the ground to look for rocks and pointy stabby things and, you know, sticks and anything that can buy you some distance and then maybe some effectiveness.

20:48 Contrast that with if before you had a little bit of foresight and you said, "Hm, I should probably build something that's kind of pointy and sharp." Huh? So, you you work all day and night and you put together a spear. Well, every time you encounter that problem of the saber-tooth tiger, okay, what are you going to do? You're just going to pick up your spear and deal with it. Just my really crappy drawn spear. That's sort of the same thing that LLMs use tools for.

21:11 They encounter problems. When they encounter them a few times, they then develop tools that solve them or use pre-existing ones through MCP. And then in doing so, we can standardize the solving of business problems pretty easily. Okay. The last thing is just cost economics and they finally make sense. When Claude Opus 4.5 dropped, it went from a cost of about $15 or $75 depending on input or output per 1 million tokens to five or $25 depending on input or output for 1 million tokens.

21:33 That's a 3x reduction. And newer models are even cheaper than that. The cost of intelligence per like effectiveness has plunged something like 40% in the last year. If I were to graph this, it would actually look like this. Now, I've been using models since GPT3, way back in 2020 when it was um initially released with a very small, you know, select group of people that could access it and so on and so forth. GPT3, which is, I mean, orders upon orders upon orders of magnitude dumber than this, costs more than this technology that we are dealing with right now.

22:19 It is insane how quickly the price of knowledge work has plummeted. It's already gone down 40 times in just the last year. I imagine it'll probably go down another 40 times over the course of the next year, maybe even more. What that means is we can actually send large volumes of tokens to these things to replace the work of like deterministic um old school automations like the NAN flow that I showed you without it running a business ragged into the ground.

22:41 There are also tons of price wars that are occurring between major providers and there's a lot of like geopolitical incentives between, you know, places in the east and then places in the west um to basically make these things as accessible and easily to use as possible. So to make a long story short, this is new. Very few people understand the capabilities right now. So there are many billions of dollars that will shift as the market learns and adapts.

23:03 It is much better to be an early mover than somebody that is affected by this technology uh without their consent or knowingness. What I mean is would you rather learn about this stuff now or would you rather learn about it in 2 years when your boss or I don't know some some client base of yours turns to you and says hey we no longer need you because we have aic workflows to do it. I would much rather be the person that helps them build those agentic workflows than I'd be the person that's now sitting on my ass because I don't know anything about them.

23:27 Hopefully, you are too. Okay, so now that that big preamble's out of the way, let's learn about chat bots, agents, agentic workflows, uh, knowledge tools, and then actually get our hands dirty with some demos. I like thinking about knowledge tools as evolving over the course of the last 30, 40 or 50 years. I always think about it sort of like the step ladder on the right where you have three rungs. At the bottom you have documents.

23:49 In the middle you have chats and at the top you have agents. Over the course of the last 30 40 50 years we basically transition from knowledge in the form of docs to knowledge in the form of chats over the last 5 years to knowledge and action in the form of agents. And I'm going to run you through what each of these look like now before actually using them in a real workflow. So documents are static knowledge.

24:11 Hopefully they're pretty straightforward. It's oneway information flow. All you do is you read the document, but it's not like the document can respond to you. We currently use documents everywhere in school and in business. We use them in legal agreements. We use them in training materials. Once you write a document, it obviously stays fixed. That's a feature, not a bug, because it's great for permanence. Like if you're writing contracts or standard operating procedures that are immutable, aka it should not change.

24:33 You don't want your contract or your standard operating procedure rewriting itself unless you want it to, right? In most cases, you don't. So, u that's great. That's actually a feature, not a bug. Chat bots, on the other hand, are not static. They are dynamic. Chat bots were developed realistically way back in the 1970s, but we were only starting to use them for real knowledge purposes and maybe like the early 2020s. And they perform two-way interaction. You read the output, but you can also ask questions back.

24:56 So, here's a crappy pass to GPT40 where I just said, "Hey, what's up? Hey, Nick. All good on my end. Quick check-in. Zero fluff. I'm ready to help if you want to chat. If you got a decision to make, whatever. What's on your mind?" This is now two-way knowledge interaction. the dreaded mdash. Um, this allows you to do things like clarify confusing points. You can ask for research. You can dig deeper into topics. You can also modify the knowledge.

25:18 So, you could upload, you know, a PDF or you could make some statement and then the chatbot now has some additional context. Uh, I just think of it like really smart colleagues who read everything you give them, but then they're also confined to a chair. You know, they can't move and they can't do anything with it. So, essentially all you can do is is talk. This is how most people treat models today as chat bots. They're dynamic knowledge, but they're still subject to this little window.

25:42 They can only be communicated with and copied and pasted through your chatgbt or through your cloud output. Now, contrast that with agents, which I consider to be dynamic action. To make a long story short, this is two-way interaction, just like chat bots, except this time it acts. On the right hand side here, you can see I have a flow that says run the thumbnail generator on a link. So, it's not just asking it a question about the thumbnail generator, and I'm actually having it do something.

26:05 And this is a real agentic workflow that I developed to basically build YouTube thumbnails like what you guys saw on my channel. What we see here is a fundamentally different interface. On the left hand side, we have some of these nodes. Green ones here are actions that are being taken. These gray little sections over here are thinking nodes, which are where the model reasons um extemporaneously, basically temporarily, and then discards these reasoning tokens. You can see that it's actually calling a script.

26:27 You don't need to know Python in order to like have the model do really cool things for you, but that's what's happening right here. And then down over here we have a bash output where it's actually ran. We have an output that we can then use and so on and so forth. So you're given visibility into the reasoning. You're also given visibility into the um planning tool memory reasoning and then observation loop. And I'm going to cover exactly what that looks like in a moment.

26:50 You also have autonomy, long execution times. Agents can routinely run for 5 or 10 minutes. Now yesterday night I actually had an agent run for over 5 hours uninterrupted to build me a really cool system. As of today I think of models like a mid-tier developer. They're 100K a year or so in terms of their like capability. But if you think about it, I'm spending 20 bucks a month for this, which is 240 bucks a year, which is over 400 times cheaper.

26:50 And not only is it cheaper, this thing works 24 hours a day, as I mentioned, or it can work 24 hours a day. You can do a lot of really cool things with models like this. So now is the time to jump on it. A point to understand is that an agent is not a chatbot, despite the fact that they look really similar, right? Now, the way I see chat bots is like a chat is just an interface, right?

27:14 It's just some specific thing with messages that go back and forth and then a little window down here where you can enter in your own information. The chat is just like the app. The agent is what lives inside of the app. If you guys are familiar with crustaceians or crabs or um I don't know, like cute little things that crawl around on the ocean subfloor. They often will have fine shells and then um discard them when they no longer fit their purpose.

27:36 Right? So, like a crustation that uses the shell of an older animal, an agent is just currently using the interface of an older type of knowledge tool, the chatbot. And I'm sure over the course of the next few years, it's going to discard this and we're going to have new interfaces that are even better. Okay, so let me show you guys just the difference between chat bots and then a really low-level agentic workflow that I put together that functions through an agent.

27:59 Um, down over here is a chat GPT desktop app. This is really simple and easy. You can download it on chatbt's website. Super straightforward. I'm just going to say um hey, how can I scrape, you know, leads from LinkedIn Sales Navigator. So, when you're working with models like this, the input and output is pretty bounded, right? All you can really do is you could just see what this model tells us. Hey, you know, here's the direct high IQ zero fluff rundown.

28:23 Use this, scrape this, use this. This is cool, right? I mean, it's nice that we're getting information on how to do this. And you know a few years ago this would have been revolutionary. Rather than just have a conversation with the model and ask it how to do things which is knowledge. I can actually force a model to action using agentic workflows. So in this case I'm saying scrape me 200 HVAC owners in the US. I want decision makers.

28:45 It then checks to see if there are lead scraping directives and execution scripts. This is just part of the framework that helps constrain the model's output which I've run you guys through a little bit more later. It's then going through and actually pulling a script together to do this thing for me. It then comes up with this idea of a test scrape, 25 leads. It's then going to verify some industry match, run the full scrape, upload to Google sheet, and then even go through and enrich it for me.

29:08 In this case, the model is performing a search. It's then comparing the results of the search with what it is that it thinks that I want. It's determining that there's a very low match rate. And so, it's now adjusting its filters on the fly completely on its own to find leads with zero input. All I'm doing here is texting a friend of mine on my phone. It's then verified, past threshold. Now it's running a full scrape.

29:31 It then went and it actually got us a Google sheet with all that information. I mean, it's pretty cool in so far that it's totally autonomous. It probably would have taken me a fair amount of time to come up with the filters and so on and so forth myself. This thing just did it entirely on its own. If you guys check the bottom right, we actually ended up getting almost 200 emails directly from this. We also got a bunch of phone numbers and a bunch of other really personal information.

29:53 So, what exactly is going on? There are five steps that an agent will follow every single time you send or receive a message. The first is planning. The next is tools. The third is memory. The fourth is reflection. And the fifth is orchestration. I think I called it observation before. My bad on