Founder’s Guide to AI Using Agentic Workflows to Automate Repetitive Tasks

Welcome everybody to our 4th workshop, AI for Founders. I'm working with a buddy of mine who is a futurist and he writes books about what the future will look like. And that title I've got here, Every Human Will Be a Manager, is currently the title he's workshopping for his next book about AI and agentic work. Because I think everybody is going to end up having a fleet of agents, probably heavily customized to them, that they use in the future to get their work done. And to kind of showcase how this is going to show up, let's ruin my team's week. I have at CrowdTamer a whole agency. There's 9 of us here. And I can say, hey, I want to give work, not just agentic work to the AI itself. You can use this agentic work to do stuff with team members, with contractors for collaboration. I've got a prompt I prepared here. And we can talk some more about this and about how, why it's structured the way it is in a few moments. But basically, I have done 3, 4, 3 workshops now, we're doing the 4th about AI for founders. And I have right here, in Obsidian, the full outline from workshop 2 about how to build MVPs as a non-founder. And I can tell the AI that I'm working with right now and give it a series of tasks to perform. And what makes this AI as opposed to just a sheer automation is there's bits in this process where decisions have to be made and something has to be done, not just data has to be moved. Let's go here into Cloud Code. You could also do this in Cloud Desktop or you can't really do this in anything that's not connected to, in my case, some MCP servers, which we'll be talking about shortly and how to set them up. But basically, the workflow I've got here is go read on my hard drive a file, go create some blog posts, publish them as a draft to my website, publish them into my Notion space, and then tag my team members who are responsible for delivery of pieces of work after we've written this content and tell them to do their work. So I get that going, and then while we're going to be talking, this is going to be running. It's going to step through all of the pieces to make this happen, and I'm not going to have us sit and watch while this slowly churns through the to-dos, but One of the real powerful things about Claude Code, unlike most other agentic systems you may have, is it starts by building its own to-do list. So I gave it like 7 things to do, and it's now made a full to-do list out of them. And I've kind of left a lot of this up to itself because I'm not even telling it what the big themes of the content that I created for 2 weeks ago for the minimum viable product outline is I told it, go identify the big themes and then write about it. Now I think when we look at what we're building with AI and what good agentic AI looks like, there's a couple pieces that make this successful. I'm going to keep this running kind of on the side while we're paying attention to what we're talking about here. Right? And today we're going to talk about a couple different examples of what agentic work can look like. Then I'm going to show you how to connect MCP servers on your computer. You could also connect them in the cloud if you set up a Google or Amazon server and like we're running cloud services there. That's really not something I expect a non-technical founder to do. So we're going to run these things on your local machine. We're talking about a framework about what you can do with AI. I'm gonna try to wrap up this entire workshop series with some discussion about what I hope you have learned over the previous workshops. And if you haven't watched them all, give you a reason why you probably should. But before I go into all of that, as a reminder for folks who may be new in this channel today, I'm not speaking here purely from theory. I run CrowdTamers, where I go to market agency for startups, and I've helped hundreds of startups build their marketing engines successfully. I have built and sold an AI startup 2 years ago. I am building 2 AI startups right now, and I have my agency. So I'm in the midst of all of this, helping build and run and make startups succeed. So what I'm telling you here and sharing with you here is based on what I am seeing working. Like, some of these slides are— the content in them is new from this morning because it's the the most cutting-edge information I can bring you. And based on that, hopefully you will find this useful in what you are doing. I think the first thing to answer though, because a lot of people have heard the buzzwords, but what does it mean, is simply what is agentic AI? What does this mean? Well, there's a couple pieces that to me define something that is agentic using AI. It can look a couple different ways. One of them is you can ask, and I have ChatGPT, you can just ask ChatGPT, hey, do, do deep research and dive into something. And there's a tool in ChatGPT if you're on the paid where there's deep research and web search and Canvas. And there's like some of these tools are, I'd say mildly agentic, right? And it will go think and you can see all the steps that went through, all the sources it pulled from. From all the information it could get, right? That is one example of agentic work. That's kind of the, the minimum example, in my opinion, because what you're seeing there is it's all running in the cloud. There's no real transformation happening except of some data. This is the first step. Anybody can just do this in their browser, no tooling required, very easy to set up. If that's all you're doing for agentic work, I think you're really missing out. Agentic work can look a little more complex. You can build stuff. This is N8N, which is a no-code automation tool where you can put steps together that can include things like not just pull data from here, but pass it off to the AI, let AI do something, and then produce a result. I'm going to talk a bit about Hayvale and how Hayvale works in the backend. But a big part of how Hayvale works is built in NA10 and its agentic AI workflows built there. That all sits in the cloud and it allows you to do a whole lot of data transformation, moving place from one place to the other and make decisions all automated with AI. You can do that. And then finally, agentic work can look like what we're seeing right here. What is the AI calmly doing? Well, it's written a blog post now and added it to Notion. So we've probably already pushed them all to my website. They're probably already there as drafts. And now we're going to start to make more blog posts and eventually it'll begin to assign this information out to teams, right? So while I'm talking to you, I'm also doing content marketing. Now, if you saw the very first version of the workshop I did for you guys, I talk about how AI slop isn't the answer. And there's two ways why what I'm producing here isn't really AI slop. One of them is this is based on content I made. So I didn't ask AI, hey, go figure this out on your own. I rather said I had these original thoughts, now go draft it. The second thing is, as this task is assigned, I have a human going back through and editing it to clean up the text and make it read like a human Actually wrote it. Now Claude Sanna is pretty good at writing like a human when you give it good guidelines. It's still always handy to go in and clean things up a bit from there. So this, while it's working and making this content marketing for me, I'm doing other things, right? This is, I'd argue, fully autonomous agentic AI, but it is still limited. There's human input at the beginning. There's human input at the end. And even as this goes into Notion and creates tasks and goes into Slack and makes posts and all of these things, the AI part of it is low risk. Because as you're thinking about what makes a good agentic AI system, automation is the big idea, right? It is, what if I automated some workflow, but I didn't just automate moving stuff from here to here. I put some kind of decision in the middle. Because AI with doing precisely the same output every time on small tasks is actually kind of bad at it. If you ask AI to fill out a spreadsheet with the exact same data copied from this cell plus this cell into this cell and do it for 1,000 times, errors will show up in a way that they wouldn't if you just had a piece of software that automated stepping through this. A decent example that I was struggling with just today is I had a pretty big text file that I wanted to remove a bunch of empty spaces from. In the process of removing those empty spaces, the AI ended up— I asked AI, hey, can you clean this up? Where is it? Here we go. Give you a second to open this. I have this file here, right? All of these spaces. And I just said to AI, hey, can you, can you remove all these spaces? And it couldn't. It couldn't figure out the right way to do it because it would get bored, context would wander off, the result wasn't good. Now, I ended up being able to get this result out, but I did that by having the AI write a piece of code that then went into this text file and removed any instance of more than 3 spaces. Right? So something that simple where no decision is being made, that's not really agentic work. That's something you automate. But once you say, hey, so I've got all this text here and I want to do something with it. Now we put AI into this, into the process. That's what makes this not just automation, but agentic. There's a few things you need. You need to have discrete steps. And we can refer back to what I have here in my sample prompt, right? As this is stepping through the blog post and making its way into— this may have actually crashed out from the look of it here. I ran this previously on the first workshop and it worked fine, but it may have actually given up. I've given it very specific steps, right? Identify a theme. Do this, do this, go through all this. And because these steps are so discrete, it gives it firm guidance on what it should be doing. Now, there are still room, there is still room for the AI to make some decisions and have some opportunity for input. That's what makes it agentic as opposed to just automatable. So indeed, I believe we are crashed out here. Hey, there we go. Sometimes the Notion MCP server just gives up. And what we may have to do here is you will exit. And then if we do Claude resume, if you spell it right, it helps, Trevor. Resume. That will let me resume my previous conversation. And hopefully continue with the— whoa, this one. Please continue the— through— continue to teach her through the steps in your outline. We'll let this keep running now. And we're back to this. So these discrete steps here are what let me also go back and say, hey, you appear to have errored out. No worries. We'll resume with the instruction I gave you. Right? There need to be clear resources. That's the other thing is AI's context window is not phenomenal, even when you've got 200,000 tokens, like you theoretically have here in Claude, it ends up wandering off relatively easily. So instead, I have to make sure when I tell it what to do, I don't just say, hey, go find the content blog posts DB, because that might end up taking it 20,000 tokens to go find. If I tell it exactly where to go, then I preserve context window for the stuff I actually want to see it do. So precise resources, I did give it something more generic, right? Quit writing like AI and AI hooks prop library. Had to go find those. I didn't give it the link. I could have, but I wanted to show you you can do both. But Obsidian locally is much less data intensive than when you're running in the cloud. Frequently you query the cloud database and Claude will find it gets 500,000 tokens of response in one query. So, okay, be specific when you need to. And room for creativity, but not for error. And by not for error, what I mean is when the error is high stakes, we want to make sure that doesn't happen. If I write a bad blog post here, right, my editor will catch it. If we have a task improperly set up in our tasks DB, a human will catch it, and that human won't even be a client. And if we publish a bad message to Slack, then the team will see it, but not a client. I'd even say, you forgot this step, because the last step was publish this to Slack. There we go. All of these, the error is low, right? Or I should say the threat from that error is very low. I would never be like, hey, connect to my bank and do stuff to it, because the outcome of an error there could be catastrophic. So make sure that when you have agentic AI running without human oversight, you don't let it just do insane stuff. I saw just this week, I think Monday, someone mentioned that they had Replit running and it deleted their entire production server database, just poof, off the internet, gone. Now, they were able to restore because they had a sensible CI/CD workflow, but I would argue you probably shouldn't have your AI working on a production server at all because that happens sometimes. This example of AI's potential for error is that. And then you want some kind of feedback loop in place. You want to have it where, hey, I got this message posted, right? And this is saying, hey, we made all these tasks for this bit of work that we just created live while we were talking, right? I could ask my team, hey, give me a thumbs up if you found this a useful message. And then that feeds back into, did the agent do the right work? And if it didn't, then what do I do at this stage to make a better prompt or Or when I make a repeatable prompt, which I'll show you how to do in a few minutes, how do I make the repeatable prompt something that we edit and keep up to date time after time? So that's the framework of what a good agent's process needs to be. You have to tackle these things in the right order. I will remind you, if this is your first time watching a workshop, hey, good news, as I'm talking about workflows and models and prompts and showing you what outputs look like. I have all of these available for you in this URL. Go to crowdtamers.com/ai4founders or scan the QR code and you can download not just the resources from this workshop, but from all 4. That way, if you go watch any of the videos in the past, or if you weren't able to get the resources before for some reason, I've now, to the best of my ability, removed every technical blocker. Everybody should be able to get inside, get the resources, and start working with them. There's like 20-something files tucked away in this set, so there's plenty for you to start to use as a founder. So, all right, let me tell you a bit about examples of agentic workflows to, to give you some ideas on what you can do. Before we continue into the next step. The first thing to talk about is actually the shirt I'm wearing today. I put the shirt on on purpose. Let me show this to you, right? Ask about our €100 all-bound marketing campaign, €12,000 value. I printed up a couple of these shirts when I went to a web summit in Lisbon last November, and I also used this marketing message And I was trying to book meetings with founders of startups at the event. And what I had was people were paying me €100 to meet with me. I don't know if you've ever gone to an event, but it's pretty rare to charge somebody to have a meeting with you before you ever talk to them. We were able to do this because we had, this is, you know, what, 7, 8 months ago now. We had a very kind of baby prototype agentic AI workflow where what we would do is ingest everything from a client, or a lead, I should say, from their website, and then work through a series of prompts manually. We had humans as the agents here, but the AI did most of the lift to say we were using a software called ChatGPT, and this let me share prop libraries and different resources with my team. So we could have, like, we need to make Google Ads for somebody. Cool. We've got their product name. We've already used AI to make a description of their product and their features and their audience. You just do paste, paste, paste, paste, paste. Apply the prompt and away you go. I'll give a quick example. Proud. Actually, we'll do hay bale. Haybale.ai. There we go. Product description. I actually have that handy from my chat with ChatGPT. I can also do this real quick. If I say, hey, I want features overview, sure, we'll say this is the feature, this is the product. Well, hang on, this is the features overview. Do like this, paste this here. Uh, what? Audience, who do you use this for? Right, these are similar to the prompts I was using, but I'm using on a brand new case here. There we go. I'll pick one of these audiences. I don't like that audience very much. We'll do that. And product description is— I actually got it. I scroll up high enough here. I wrote one. This. Like this. And paste. So now I've got a prompt prepared. And so for this offer, we'd prepare Google Ads and YouTube ads, and we'd prepare content interview questions, and we basically prepare everything you needed to run and execute ads, content, outbound messaging, emails, everything. We'd put it into a zip file, hand to the client and say, there you go, there's your entire Got workflow. Now they would go, hey, uh, you gave us a bunch of resources, cool, how do I execute on this work? And then we'd go, ah, funny thing, if only you knew an agency that had expertise in all these different types of marketing. And that was us. So having that set up, right, people paid us money, and out of the I think I had 7 people prepay. And I also, walking around the show floor, had 2 people say, hey, I like your shirt, can I pay you €100 right now? Of course, give me your website URL, let me meet with you again in an hour, we'll have everything ready, right? Away you go. Having something like this as an offer where we gave people a bunch of work and then they went, oh, I don't want to do all that work. Well, then we landed, I think, 2 clients out from that, right? Where you're charging them whatever, 5, 7 a month to do all of the work for them. That's an agentic workflow, right? Building even back before we had Claude Code. Claude Code didn't exist then. Before we had OpenAI agents, which just launched last week, right? These things, they weren't around. So we had to have humans kind of do it, but AI did all the heavy lifting, right? That's an example of something you could build really quickly. With no automation or agentic AI put in, just kind of using AI to do creation. Another example would be, uh, HeyVail. This is a much more complex example, right? HeyVail.ai. So this product is extremely complex agentic AI held together with a service agency, because CrowdTimers is a service agency, on delivery. So there are definitely tools that will do things like, hey, if you put in a phone number, these guys, bland.com, right? Put in a phone number, get a phone call. That is easy to do. But then what happens after you do the phone call? Yeah, good luck. You've got to automate everything. And with HeyVail, we said, well, what if we automate everything for you? We made it where the solution is just, you say, hey, I need something to come in here and be my Revenue Partner. And what connects it all in the backend is our own server with a bunch of— actually, we're migrating off of NA10 right now, but we're using NA10 server to do things like, say, uh, here we go, we're going to begin by the webhook. So right now if you go to the website, hey.ai, punch in your phone number, it fires this webhook. It then will— this is not AI yet, this is just automation— lock a record in Supabase, check the time zone, set everything, make a call, is the phone valid? Like, all of this is purely automation. But then at the end of the call, we have to decide what happens, right? Once this call is transcribed, Is this person a good fit lead? Do we want to reach out to them again? Are they a bad fit lead? Did the call not even connect? Well, we use AI then to create our post-call workflow. Let me just pop out here real quick to make sure I'm not showing anything I shouldn't be. And then we have our after-call workflow where we fire the next webhook We pull the text out. We basically do some transformation of the text to make it usable. And then there we go. There's a couple opportunities here. One is somebody said, yes, I would like to talk to you. In the future, we book on a calendar and something goes wrong, or we just want to analyze, is everything going correctly? And then we release the phone number, post the message in Slack, go on our merry way. Right? This is agentic. This is if the call didn't book successfully on a calendar for some reason, right? Go look through what happened and then manually redo the booking. Or worse comes to worse, alert somebody in Slack, hey, I couldn't rebook this, right? There's several failovers for how this all works, and AI takes a decision in the middle of these steps. That's an example of a fairly powerful AI workflow. And of course, Here's another example of one where we saw, we created tasks in Notion for a team member where this is assigned to, uh, nobody right now. I'm guessing I didn't specify the assignee properly enough for the agent to take the job, but here we go. Stop waiting to be technical enough. And I would note, if I go back here and say, hey, hey, those tasks you made in Notion don't have assignees or links to the blog posts in the CrowdTamer blog post. It can go back through and iterate and improve on the task I gave it. And I now know next time I create the prompt, how do I improve this? All of these pieces can feed together. I'll give you one last example of what you can do with Agentic AI that maybe is transformative for you. I'm going to come to this and go look at this. So one last thing you can do with Agentic AI is ask it big questions. Ask it to look at a bunch of data and ask it to see things that maybe you wouldn't see. So for a given client here, we are running some ads, May to June, on a couple of their channels and looking to see how they performed. And what we wanted to do was not just look at the ads themselves, 'Cause ad click, ad registrations, all these things are fine, but there's always a broader picture when you're doing marketing, when you're doing all-bound marketing, like the shirt says, there's a bigger picture involved. What happened on organic search? What happened on time on site and sessions? What did we see on the actual content platforms for view rate and watch rate? This involved pulling together, I think it was 8 different spreadsheets. And then I put all the context into Claude Desktop, and I basically started to ask questions. I said, hey, what are the top performing channels? How did it make this result? And then, hey, what are some keywords that did well for you? Okay, tells you what keywords got clicked, what the click-through rates were, where do these clicks come from? But then we begin to look at other things like, if I scroll down here, Well, we ran the YouTube ads, we actually saw a huge lift in brand search. Right? So ads on YouTube didn't necessarily drive direct clicks, but they drove people searching the brand name and then going to the website. That's pretty cool to know. And when you start to see the full picture on what's happening, as you invest in one type of marketing versus another, Would I have found this? Probably not. Right? To hold in my head all the ad campaigns, what channels they were, are, what Google Search Console is doing versus YouTube ads. It's too much unless you spend a day just diving into the research. But you ask a couple questions. Hey, are there correlations between any of these different ad platforms and other metrics that changed? And agentically, it's going to go look at everything and present you some conclusions. Now, it'll lie sometimes, and you have to validate these conclusions before you present them to a client or make a decision on them. But there's another example, right? From this data, I can now make slideshows. I can go dive into data and look more deeply and begin to understand what I can accomplish. So the three different examples of what this agentic AI enables. Now, I've been giving you guys teasers on what you could build. Let me talk about how you do this. The first thing here is AI can help you do this properly. You don't have to be a genius at using AI, setting up AI, any of these things to make it all work. So I've been talking about agentic AI, and the other word I've been using is MCP servers. What is that? Well, let me give you the idea behind what this is. Web 2.0, if you're old enough to remember back then, and I am, that's when I got my start in marketing 20-something years ago, was the idea of all web pages are applications. They all have an API, an application programming interface. That's when suddenly things aren't just a static page, but like right here, right, this slideshow, I can make changes to it. It's an application. Anything I want to do within the context of making slides is doable in the browser. If you're old enough, you can remember when that was a big change. That was a crazy thing to do. That was 20 years ago. And then, yeah, 8 years ago, we see Web 3.0, right, decentralized crypto, all of this. But the idea of what if we put pushed from a centralized API and centralized applications out to individuals are empowered to influence and interact with software the way that they want to. Now, I don't think Web 3.0 ever became as big a thing as it hoped it would be, unless you invested big in Bitcoin back in 2018, in which case, good on you, you're probably wealthy enough that you don't need to be trying to start your own business. But The idea of this decentralization, I do think, is very relevant to where we are right now, which is with AI and with AI's expertise, we can start to build customized workflows for anything we want to accomplish, as long as we do a few things correctly first. AI agents. Are what's going to enable the next stage of software utility for most people. And all it requires is setting up some AI agents and doing it in a manner that works for you. So I have a prompt that's going to walk you through how to set up a MCP workflow. And it begins with what is it that you really want to automate or to make easier for you. Now I'm not going to walk through that bit because I know what I want to do. I'm going to walk you through if you had downloaded and installed Cloud Code, right, or Cloud Desktop, either one works. If you have Claude code, you can go right here. And you can install this. Just copy this and open up. If you're on Windows, you open up PowerShell. It'll take you right here. If you're on Mac, you open up Terminal. If you're on Linux, you probably already know how to do this, but you open up Terminal. And you're going to paste in this, this piece of text. And then when you're done, you run Claude for the first time. It'll say, hey, do you trust the files in this folder, fi-test? I say yes, I do trust them. And it dumps you right here. Now, at the moment, in this folder, I don't have anything set up. For MCP servers. This is just plain code, Claude code, as bare bones as you can get. And I can decide, oh, I want to set up the 3 things that I just used to make all of these tasks for my team. I want to set up Notion so I can read and see what's in Notion. I want to set up a Slack connector so I can post and read what's going on in Slack. And I want to be able to read and write on my local hard drive. Now if you install Claude Code, it can already read and write to the local hard drive. Like I can say, can you look at doc slash papers Obsidian vault and see what's in the AI— I forgot the name of the own folder here. This is a task and prompt subfolder. And then because this is the first time, right, this is going to say, sure, I could look there, but you have to give me permission. Now, you can either say yes, that's the first choice. And then every time it looks anywhere in that folder, it'll ask you again. Or you can say yes, and then don't ask me anymore. Just assume you're allowed to look at this, right? And if I say that, as it goes and looks and reads, I don't have to approve it again. It's going to go look in there and just tell me what it finds, right? So I can read my local files without any special instruction if I'm running Cloud Code. If I'm running Cloud Desktop, which, by the way, you can go find if you just search Cloud Desktop. And I have— that's going to be— takes you down to Cloud, like install on Windows, same way. That's going to take you to— this looks just like Cloud in the browser. But it runs in Electron on your computer. And you can install connectors here. Now, Claude Code connectors are in some ways easier than MCP connections, because I can just say, connect to my Gmail, right? I can say, well, if I can scroll down fast enough, browse connectors. Connect to whatever I want to connect to. Right, they even finally have a native Notion connection instead of my MCP server that I set up. So if you're less technically comfortable, go download Claude Code and connect to the pieces you want to connect to here, right? I don't think they have Slack yet. They don't. You just have to manually set up Slack, but they have Google Calendar, Google Drive, Gmail, Notion, like, and they're adding more all the time. Either of these is acceptable. What you want to be able to do is start to put AI in between moving data from place to place. Since I find Claude Code is easier to use because it's less clicky and more just typey, I want to keep walking through there. But keep in mind, if you'd rather, you could always use Claude Desktop as well. So, okay, this is set up properly. I want to ask it, I want to connect to a Notion MCP server. I know that's what I want to do. I'm going to pull up this and say, this is what you need to do, right? We're on step 2 now. There's a couple things MCP servers can be a security risk. Because you didn't write this code, anything could be happening there. So we want to make sure we do is properly say, okay, I want to connect to a Notion MCP server to create tasks automatically for my team. But paste in this bit of the prompt, and it's going to go do some research for me. That way, it's going to try and see if this is going to be a security risk. I will note, you take this all— all the risk for setting up anything that runs software and automations is on you, right? I'm giving you the best guidance I can on how to do it safely, but things may go wrong. So this is, you know, caveat emptor. But what this is going to do is make sure it is getting the right information to connect to the right MCP server. I'm gonna answer these in turn, right? Um, I need to connect to Notion and to, uh, I mean, I think there's a calendar. Notion calendar to, uh, read and assign tasks to it, then Ctrl, unless you hit a soft enter there. No other platform. Oh, Slack, actually, yes. I also want to push alerts to Slack. And then 3, there is no 3. There is 3. I want to read messages in Slack. And then 3, I want to write, so I want to push alerts to Slack as well. Cool. Now it's going to go find out what the right MSRP servers are. I would note there are tons of places you can go, right? MCP.so has lists of hundreds of servers. I could search here, Notion. Once I've proven to it that I'm human. Oh yeah, there we go. Oh, it helps you spell it right. Anoche Notion. There we go. Find me. Notion servers, N-O-T-I-O-N. There we go. Right? Bunch of these servers, which ones are safe ones to connect to? That could be hard to say, right? This is going to try and figure this out for me. So it's going to go look at these servers and look for how many, how popular they are, how many stars and downloads and polls and comments they have, and then look at the code of it too and say, does this look like it is safe and secure? It is not perfect, but it is better than just picking a random one off this website and going, yolo, hope I'm not going to regret it, right? So By following through the prompt in sequence, you lower your risk a little bit than just searching here, unless you're savvy enough to search here and feel confident, in which case go ahead and do it. So this then going through and saying, okay, I know which one I want to do in a few more moments. And when it does that, it can then set up and install its information on its own. Now you will need something from basically every MCP server. You'll need API keys, you'll need bearer tokens, you'll need authorization because the software wants to act as you, or at least as itself on your behest, in some other software. You can't just randomly do it without some security system setup. That means you have to go find out, you know, if I have Notion find my Notion API key. Right? Now I'm going to tell you how to do it. Now. I'm not going to go look up my API key, because if I show my API key on screen, yeah, any of you could use my API key. But if you go into Notion and follow the steps here, or into Slack, or into wherever, you Google this, you find the API key, it's going to be some long nonsense that have letters and numbers. And when you're setting up the server, see which one they recommend here. Most trustworthy, the official Notion MCP server. That does look pretty good. Notion is probably not going to screw you over any more than they already are by just being somebody who has all of your data. So let's install the official Notion MCP server, please. Now, this is going to go through all the setup and then ask you, what is the API key you want to attach? In this case, I'm not going to set it up because I don't want to be sharing my API key on the streams, I've already mentioned. But all you'd have to do is put that in. And then be able to enable all the functionality like you see here. Whether it's in MCP to SO or natively right in Cloud Code or in Cloud Desktop, because I can also go here and say, what's the tool that I haven't connected? We'll go back to this and say, hey, is there And, um, um, CP server for Trello, right? Can you install it for me, please? Like, that could go look and find what opportunities there are for a Trello server. I don't use Trello, but if I did, right, this could 100% set it up. And in any case, you can run it in desktop, you you can run it cloud code. Either of these is acceptable. Okay. So first up, you have to document what you want to buy, right? Which is the first stage of this. Is this something that is an actual agentic need? Or is this just something you want to connect Zapier for? The next is investigate the server, make sure it looks legit enough so that you're probably not at risk as you set it up. And then finally, set this thing up. And then lastly, you can set custom commands, right? Look into my— I showed you guys this last workshop, Obsidian Vault, for the find— find tasks slash command and tell me what it does. I'm not going to run the find-task/chan because I've already run it this morning. But last time when we were workshopping how to use AI to be more productive, I walked you through how I set this up. I do run these every morning now. Indeed, I have from today— that's not today, that's earlier in the week— but I have from today Hey, there we go. You can see my intention is to nail this final workshop for Founder Institute. Cool, right? This pulled this information and will let me build out a custom workflow and pass custom information on to my team from here, right? I've taken a multi-step workflow here. I can actually show you all the way up here for Find Tasks. Multi-step workflow: estimate work complexity, validate overdue work, see if it's done or if it's not, and then fill this all out, right? Like, that is now systematized. And this is one of the reasons why I like Claude Code is I can, in Claude Desktop, say, hey, go read this file and go do the specific steps this file says, I can. But in Claude Desktop, I can just use the slash command and invoke it directly and not have to actually give it any more background info than just do this thing. So when you're saying all this stuff properly and you've got these custom commands, you can now easily tell your AI, go do this thing repeatedly. But what can you make agentic? What's something that you reliably can say, hey, I want to do this over and over again? There's a framework I have, and this is one of my downloads, for what you can reliably make agentic and what you maybe should either just set up as an automation or maybe not automate at all. Right? If it has multiple steps, it can be really hard to automate. But AI can be good at filling in the gaps where the pieces don't fit together perfectly. And AI can move around until it finds the right way to do it. Has to be low risk if you get it wrong, right? If it's internal-facing and you get it wrong, yeah. External facing, that might be a big problem. Do you know what success looks like? Can you very clearly lay out for the AI, do this and not this? If you can, excellent. And then how do you make it better? This find tasks slash command that I have, I've been iterating on for maybe 3, 4 weeks, and it's almost there. Now, and with this almost there, I can also start to say, hey, right now I'm the only one using this tool. I could make this a command in Slack, where in Slack it goes through all of these steps, hits the cloud for Claude, and all of my team members with a Slack connection to calendars and to Notion could have their own week, day, whatever planned for them just by executing these commands. Right? That may be something that I end up doing for my team, is I make it agentic not just for me but for my entire company to speed up everybody's planning every single day. Okay, there's a feedback loop in place for this. I'm able to keep improving it to where I might want to share that through, right? So this walks through kind of what you have to do and how you have to think if you want to make something agentic. But the possibilities are kind of endless, right? Like this twice, I have this twice. If you want to grab that framework, my prompts, anything from the last 2 weeks, 3 weeks, go ahead, hit that URL and see. I kind of ran my workflow in the wrong order here. Now we just set this up while we were talking right now. And in Here, it's going to ask— after it does the npm install, it's going to ask for API keys and other things that I don't particularly want to be sharing live on stream. But it's important to note, right? And here we'll wrap this up. You have to be part of this. You cannot leave anything 100% up to AI. Unless you want to have low-quality AI outputs. You will see, I can find 200 of them on X.com or on LinkedIn right now. Someone saying, look at my insane 700-step N8M setup that I'm selling for $10K a pop. I'm a genius. You can have a faceless, effortless TikTok channel that makes you $10K a month instantaneously with no problem. This is not true, right? You're not going to have, with no human input, good enough quality output that people are actually going to consume this. You still have to be part of this. You still have to be adding your unique bit to it. But AI allows you to ship so much faster than you used to. And if you think about this, You're here, you're a founder, you want to build something. I presume you're part of the Founders Institute. I basically showed you over these last 4 workshops, right, how you can take away $40,000 a month of expense and run it all for $100 a month, $20 a month, maybe if we're being really lean. And get a content agency, a development team, a project management team, and your COO who takes care of everything. And all you need is maybe a grand for the whole year to make this happen. Whatever vision you have, whatever idea you have, whatever goal you want to build, AI unblocks it. If you let it. But you have to approach it with the right mindset. You have to understand, left to itself, AI will produce middling quality work, and it is your job to elevate that to something that is distinctive and uniquely you. But once you embrace that, once you're ready, you have everything you need. Go build something awesome. Don't wait for permission. Don't wait for fundraising. Don't wait for anything. All the barriers are the lowest they have ever been for a founder to do something incredible. So right now, it's Wednesday. By Friday, go do something awesome. I challenge you. I know you can do it.

Awesome, awesome. There you have it, Trevor. I hope you can see all the emojis on your screen. A ton of love.

Yes, yes.

A ton of great feedback in the chat as well. I think one of the really important things that you mentioned was human in the loop, and we've seen it when trying out AI tools. Trying to build things as well, quality control essentially comes from you, right? And like it's from the input to the actual output, like you need to have some level of contact there.

You really do. And that is a lot of people want to give away, I think, more responsibility than you really can, right? I do heartily encourage if you're using AI and you want to be agentic and you wanna have it be able to do stuff, particularly on your local machine. Like, Obsidian is free, right? This is just software you can have, and it is infinitely extensible. There are a bajillion modules and plugins and downloads you can add. But fundamentally, all this is, if I go navigate to the folder in my documents, is a whole bunch of Markdown files, right? And Markdown is a completely portable, infinitely reusable— anything in the world can open this file. So you're not locked into one file type, one software, one anything. Claude can read it, Gemini can read it, any AI out there can read it. And this lets you say things like, you've seen all of my prompts, they come with, hey, go look here. Like, go find this thing for context. That's the human in the loop part here. I don't have to write my prompts with all this detail up front. If I already wrote something useful, like, hey, here's a hooks prompt library, right? The AI can just use it. I don't have to copy paste that again. It's stored locally and it can inform how the outputs come. Or if I have Quit Writing Like AI, this is all human written in this case, right? All of this. And this document here, Good Internet Writing Qualities. This is something I've used for a decade on telling people how to write. So like, this is before AI was even a thing. I've been telling people write this way. All this feeds in, right? I've got 15, 20 blog posts that I wrote 4 or 5 years ago before AI was there to teach it how I write, to show it my style. All this context sitting where I can just reach out and grab it means the human in the loop can be really easy to import if you set yourself up for success first.

Awesome. So what we'll do is we'll get into the questions from the audience, but I want to kick it off by asking you, because you have, like you said, you know, there's a ton of people on Twitter selling their AI agents, all that stuff. How do you determine like whether or not a task is worth delegating to an agent? Like, what are some things that you absolutely do not want an agent to be doing?

So it's actually really similar to how I choose why I delegate to team members. There's an XY axis I can actually— hang on, this is in my CrowdTamer's company guide. And it's the same logic. Either way. Let me pull this up real quick in Notion. And I'll just show you here, there's an XY axis, right? And it is how risky is the decision? And how hard is it to walk the decision back? If it is a high-risk decision that you cannot walk back, do not automate this. If it is a high-risk decision that you cannot walk back, do not let a junior team member execute it. AI is effectively right now about as good as a junior team member would be. So if you— here we go— if you treat it like that, and just like a junior team member, it needs context, right? I could go find— where is my decision matrix here? Probably here, scroll down. Hey, there we go. Right? Easily reversible, low impact, the task owner or the AI, just do it, automate it, give it away. If it is high impact of a bad outcome and irreversible, like the CEO has to do it, right, or a human must be involved. That same matrix works well for delegating the decision to your team. But AI is part of your team, right? Every human will be a manager. What do you imagine the AI— give it away if you can.

Awesome. I've actually never seen this, you know, this matrix, this framework. And this is actually super useful because we have a lot of people who want to essentially delegate tasks to AI. But anyways, yeah, moving to the questions. One of the things that you mentioned in your presentation, and Mike asked this, you mentioned we're moving off N810, right? What are you exactly moving off to? If you could maybe explain, briefly explain some of the tools that you're working with.

Sure. My apologies there. I spend so much time in them, I tend to forget people don't know what they are. So N810 is If you know what Zapier is, Zapier is an automation tool. Basically, as part of Web 2.0, where everything is an application and they all have application programming interfaces, APIs, came the capacity to say, I've got something sitting in this API and I want to move it over to this API. And then you end up— very well known is Zapier. I will do this here real quick. Zapier is the 800-pound gorilla in the room. They're like the Salesforce or the HubSpot of— oh, that's interesting. There we go. I've literally never seen Zapier error out before like that. That's fun. Super cool. Hope that's just me and not that their whole website's down. I just go to their app, app.zapier.com. Apparently not. Okay. But also, there is n8n. Spell it right, n8n.com. There we go. And they are a lot like Zapier. They have build workflows, and then the thing thing they have that's different than Zapier, although Zapier does have this now as well, is you can plug AI agents into your decision tree. So if you think about this, this is basically, I have data here. In this case, let's say somebody signed up on a website. And then AI agent would be, hey, something like evaluate if this is a good lead for my business. And then is this a manager role or better? Cool. Update the profile and add them to Slack. And if not, then do something else, right? Like, there's a workflow you can build. Or on sales, hey, get reviews, grab everything in, automate away questions from this, and then drop the information into a Google Sheet. Like, this basically just pulls data moves it around, transforms it, and then you can plug in AI to do something and then pass it somewhere else. So these tools allow you to do very complex things. There's two things about this. One is, if you don't know how to build workflows, you're going to have no idea what you can build in Zapier and A10. The problem is too big to solve. So if you start with, hey, Here's what I want to do, right? Here's the business process I want to automate. And you put this step 1 prompt into Claude or ChatGPT or any of them. It's going to ask you, what are all the pieces? And if it can't be done with just automation, it will tell you you need AI in the loop. And if it can be done with just automation, it'll suggest maybe just use Zapier or Anytemp. Right? So this will help you understand without having to know, click this, run a JSON object cluster to a to-do list, whatever the heck that is. Like, I don't know most of this stuff and I use an agent, right? So use an AI tool to help you understand how to use the tools that are available to you. The other reason why I like Na10 is we're currently running the at Crowdtamers, our own hosted version of it. So you can see here, right, the URL nate.crowdtamers.com. It's running on my servers, which means a couple of things. One is it's cheaper. I'm not paying Zapier $50 a month or more for the— I'm doing like, look at the overview. We sent 20,000 executions in the last 28 days, or sorry, last 7 days. So I'd be on the probably $250 a month plan for Zapier. I'm thinking I'm spending $7 a month for a server on Hetzner, right? It's just way cheaper. But are you comfortable setting up a server, installing Redis, putting in anything like— it's more work. You can also use n8n hosted on their platform. I have no idea what their pricing is because I use it self-hosted.

Awesome. So we have a question here from Thomas. It's a two-parter, but you answer— you kind of answered the first one about like delegating to humans versus agents. But the second part is about security, particularly Cloud Code. Does installing and using Cloud Code on your computer keep the work local and not in the cloud? A lot of people have asked similar questions. If you could maybe give us like a general overview of French, all code.

So another reason why I like Claude is, well, I like OpenAI and I like Claude for different reasons. So OpenAI has basically memory. It just remembers stuff you've done and what you've asked it for and a bunch of things. So I actually depend on that sometimes to ask OpenAI, hey, I've been talking about this, remember it and help me with this next thing. Claude doesn't do that. Anthropic by default is dumping all this information. Now, of course, there's some transformation happening with stuff in the cloud, right? I guarantee you they're training models or something on your data. They have to be. But it's not being stored long-term. So if I run something in Claude code locally, here's a decent example, right? This is reading these GitHub repos and then analyzing them. The analysis has to happen with Claude's actual servers in the cloud. That's going to be pushed back up to Anthropic's GPU cluster. Claude's going to make up its mind and then say something back. But a command like, npm install is running on my computer. Nothing's happening in the cloud there. When I ask Claude to write a file to my local computer, like, what is the AI for Founders stuff, like any of these, the composition of this was done by Claude. That happened on Anthropic's GPUs. It's only saved here. The only context, if I want to come back to this, is here. I can't ask Claude, hey, go remember that conversation we had. Within some limits, right? When I booted Claude up and I went, give me a second here, Claude, here I can do Claude resume, right? This is going to say, hey, there's a lot of different conversations I I could resume from. But all of the session information is stored locally. If I deleted my Claude folder on my hard drive, I wouldn't be able to resume any of these conversations. So that's kind of the difference here. If you're working with HIPAA data, financial data, secure, like any of these things, you really can't use Claude because Claude is still pushing stuff back up to the cloud. But you can use different command line tools to run Llama locally or Qwen locally or any of the local LLMs, but then you have to have a computer good enough to run them. And my computer is not cutting edge enough to load up a 17 billion parameter LLM and run it locally on my hard drive.

Okay, so kind of moving our focus from cloud to OpenAI. We have somebody here in the chat who asked on OpenAI agents, how do they differ from traditional chatbots or APIs? Because there's a lot of confusion about like what qualifies as an agent, right?

Yeah. So if you go back to what I was referring to earlier, right, an agent takes automation and adds a decision into it that is AI-powered. That's what makes it agentic. So if I think of, I mean, the example I have of, as I mentioned, kind of the minimum example of what an agentic AI is would be something like this, where I say, hey, this is a little more advanced than a chatbot. I have asked ChatGPT, go research a bunch of pages on the internet, go out and look at stuff. And it's going to crawl the internet and pull together a tremendous amount of research here, right? This is basically saying, I'm searching for all this information, right? Here's a website I have read. Having read this website, I'm ingesting the information and using it to make future decisions, right? So this is where the decision comes in, is I could tell a tool like Scrapy, Go scrape salesforce.ai and give me the text, right? That's automation. But the AI agent is, go read salesforce.ai and then tell me how similar and how different you are to Hayvale, right? There's a decision that happened in here. So ChatGPT's agents are kind of like what I was showing here. In Notion, where I've got data here, data here, pull the data, make the transformation, decide something and do something else, right? So this is a next level of automation. A chatbot runs locally on some data, right? It may be in the cloud, but it's only got the data it has. An agent You may have a chat interface, but the agent's going to go out and do stuff, make some decisions, and then come back to you. Did that clarify things?

No, I think that makes perfect sense. So we have a question here from Ishika, and we've had this in a bunch of our workshops now. A ton of people, they're scared about the AI revolution, things that are going to happen. As an individual, what advice would you give them? To preface, Ishika is a student.

Sorry, one second here. I have a team member who's pinged me 4 times in the last minute.

No worries.

So now with that said, what was it you were asking again? My apologies.

Yeah, so a lot of people, they're scared of AI and its effects, especially on the workforce. What advice would you have for somebody you know, who wants to maybe learn more about AI or somebody who's a little reluctant. And again, this has been asked by Ishika, and Ishika right now is a student.

So the truth about AI is you cannot put the genie back in the bottle, right? There are moral reasons not to use AI. Right? It is basically institutionalized copyright theft. This is true. Uh, when AI creates art, it has unlawfully used that art as training data to create something in the inspiration of. That's also true. Uh, but it will continue to be used. You have two options ahead of you then. One is to say, well, kind of like the business model for music transformed when Napster showed up, and then instead of buying CDs, now we listen on Spotify, right? And does Spotify pay artists the same way how music CDs do? Hell no. But the industry moved to a new place. And you can't go back to how things used to be. The same thing is happening with AI intellectual property, whether it's text, visual, any of this. If you decide not to participate, you'll be kind of like a music artist who says, well, I only release CDs and maybe, heaven help you, vinyl LPs. Okay, you may make that choice, but you probably won't be very relevant in the modern era. So, as a student, the options ahead of you are either learn this, be good at it, and be highly desirable as an employee, or don't, and then look at a bad job market where you don't have the skills and no one wants to hire you. I think when you set the binary options up that way, my recommendation for which way to go is pretty to be clear. I do think the people who adapt and stay relevant with agentic AI and with helping make AI performative, they will be in demand for 5 years, 25 years, I don't know how long, for a while, even once we have fully autonomous general AI. That could just go out and be like a human, self-dispatch all of its own problems. Even when that happens, we won't use it everywhere because there'll just be areas where it's not worth putting an AI into it. AIs are expensive to run, they're expensive to train, they're hard to keep running, they're hard to keep focused. Humans will still have a place. I like to point out that, uh, up until about 2010, the top-selling semiconductor chip was still the, I think it was the 8088 chip, which only had 64 kilobytes of RAM, which was at the time in 2010, like a 35-year-old semiconductor. Why in the world was that still top-selling? Because every single feature was known. Every single bug was known. It was incredibly cheap and power efficient. And for most use cases, you didn't need something more powerful. Humans will probably look like this in the era of superintelligence. We are generalists, we are trainable, and we are not as hard to keep on task as an AI may be. Learn how to be useful in a world where maybe there are smarter-than-us AIs out there, and you will be able to continue working, finding meaning in life, whatever it is you want to do out of your next 50 years. If you don't, I think you may very well be left behind.

Awesome. Thank you for that answer. And we had a bunch of people who very graciously answered Shikha's question in the chat as well. So folks, thank you for that. There is a question I missed out on, Trevor. I want to ask that super quick and then we could probably close things off here because we do want to save some time for networking. Adam asked, does having a human in the loop improve Claude code over time? Is this some sort— is there some sort of reinforcement learning?

Here? Yes and no. Like many things. One, the general performance of Claude code itself varies day to day. I would say of Anthropic, of OpenAI, of Gemini, any of these, the general performance of the tool varies from day to day. Because on particularly high demand, times, hours, whatever, there's less GPU compute power available to answer your question. So you get a shallower response, you get less thought, and you get inferior results. If you go look at Reddit on /r/claud, people have been spitting mad for 2 or 3 weeks because they're paying Anthropic $100 or $200 a month and the quality of the answers has been going worse and worse because there's more and more users. You can, you can only buy GPUs so fast. So when you 5x your user base in 2 months, you cannot 5x your compute at the same time. And I can tell there's 2 major models for Claude. There's Opus and there's Sonnet. And I accidentally started with Opus open this morning. And I got exactly 2 queries in before it went, you used up all your Opus credits for the next 4 hours. Done. Right? I almost always default to Sonnet, which is the more cost-effective one, because I don't run into rate limits then. So, when you are human in the loop, does Claude get better? Well, Claude itself is better or worse on given days. That said, as you provide better information on tasks, as you do a better job of making a concise prompt that produces the results you want, and as you get better interacting with AI, which, let me remind you, is an alien intelligence that always kind of does things the way it is thinking about and probably not the way that you want it to. I will give an example. For the last 3 weeks, I've been using that slash find tasks command I showed you this morning and getting it to run and execute and plan my tasks for the day. This morning Claude went, don't know what that is, but here are some ideas for what find tasks might be. And I went, okay, we'll go look in this folder and then you'll see what the find task command is. And it went, oh cool, now I know what you want. Right? Clearly Claude was having a stupid morning. But also, I've already documented everything it needs to do. And so once I pointed it to the right place, it was still able to do the job. And being aware of how the AI works and how sometimes, just like a human in some ways, you've got a bad brain day and you make a dumb answer to a question. Okay, well, let's refocus and come back. And As you have better context to give it, you will get better results. Is that the feedback loop making the AI better, or is the feedback loop making you a better consumer of AI? I think it's some of both.

Awesome. Awesome. That was, I think, slightly more poetic than we were expecting of an answer, but nonetheless, it makes perfect sense. Trevor, thank you so much for doing this. With us. It's been 4 incredible workshops, folks. Again, if you missed out on anything, Amelia will share the link for the workshop series, and you can actually check out all the recordings there. And again, folks, if you could maybe show Trevor some love— and there you are, you are showing Trevor love. Trevor, before we close things out here, anything that you want to leave with, maybe plug, Whatever the stage is yours.

All right. I think the last thought to take away here, right? This is the Founders Institute. You're all here because you wanted to build something cool, or you're investigating building something cool. And it's never been easier, and it's never been harder. The easy bit is actually building it. The easy bit is actually making something someone can touch and and evaluate. That's never been easier. I'm old enough to remember 20 years ago when to build something like any of the 3 products that I've launched this year would have required a million dollars just in infrastructure before I ever even tried to make the software happen. And the software would have been impossible. And now it's, I mean, my server bills are $14, $15 a month. Right? I pay $100 a month to CloudMax. That's probably it. Right? The entrance is easier than ever. But being visible and finding the tribe, to use Seth Godin's words, who really love what you build, has never been harder. There's so much noise, so much chaff, so much attention demand on all of our heads. I saw a post on Threads today. That said, this cannot be the same brain that in middle school used to read 800-page novels in 3 days. And I'm like, I kind of feel that, right? I certainly don't have focus like I used to. I'm sure nobody on this call has focus like you used to. Cutting through that noise and being visible is harder than ever. But because making something tangible is easier, you can focus on that bit. You can focus on, as a non-tech founder, even as a tech founder, how do I get into the market? How do I get in front of people who I want to buy my product? And quickly either figure out, yes, I can, or no, I can't. And if you can't, then as a founder, your gut check needs to be, do I keep pushing this idea Or do I have another one? Because it's never been easier to build. So once you find the thing that cuts through the noise that people want to buy, go do that. And maybe don't waste months or quarters or years of your life on something that you can't figure out how to get people to buy, because it's always going to be possible to build the next thing. Everybody who's attended this or any of these workshops everybody who's ever attended an FI session. You could build something awesome. It's never been easier to build something awesome. I want to see you guys go and build something awesome, because the only way we get cool things in this world is because a founder made it happen. So go build something cool for us. Good luck.

Awesome, folks. You'll have the replay in your inbox in the next 48 hours. Like Trevor said, go build something awesome. We'll see you in the next one.

Take care, y'all.