How I’d Teach a 10 Year Old to Build Agentic Workflows (Claude Code)

Agendic workflows are changing everything for AI automations. These workflows fix themselves and can be made 10 times faster than normal step-by-step automations without writing a single line of code. But recently, I saw that some people in my community are still confused about what they are and don't know where to start. So that's why in this video I'm going to explain what agentic workflows are in the simplest way possible so that even a 10-year-old can understand it. And then I'm going to build one right here in front of you guys so that you can see how simple it really is. And by the end, you'll know exactly how to apply this to any workflow you want to build. So let's get into it. So before we actually build anything, let's make sure we're on the same page about what agentic workflows actually are. With traditional workflow automation, the stuff that I've been teaching on my channel for a while now, you're basically showing the system exactly how to do something step by step. That means that you're dragging nodes onto a canvas. You're connecting them together. You're making sure that the right data flows into the right spots. You're setting up your own API calls. You're the one who's mapping out pretty much every single step. But with Agentic Workflows, you just tell the system what you want, and its job is to figure out how to get there on its own. So here's the simplest way that I can explain it. Think about it like you're ordering food. Traditional automation is like cooking dinner for yourself based on a recipe. So you follow step one, you make sure you have the ingredients, you follow step two, step three, you grab everything you need, you measure it out, you set the timer, you set the oven, you have to flip the burger, whatever it is. If you skip a step or you mess something up, then the whole thing kind of falls apart. Agentic workflows, on the other hand, are like walking into a restaurant and just telling the waiter, "I want a delicious steak dinner." You're not telling them how to season the steak, what temperature the oven should be, or even maybe like what sides to pair it with. You're just giving them the outcome that you want, and they figure out the rest. But of course, even a great chef might need a little more information from you. So they probably will come back and say, "Okay, cool. That sounds good. But how do you want your steak cooked and once you tell them medium rare, they'll go off and they'll handle everything else until you pretty much get that end result." So that's exactly how Agentic workflows work. You give the system a goal. It figures out the steps, but also it asks you clarifying questions to make sure that it gets exactly what you need because a lot of us are really good at knowing the end goal that we want, but not exactly how to get there. So let's make this practical. Let's say you need contact info for 50 dentists in Chicago so that you can send each one of those dentists a personalized outreach message. With an aentic workflow, you don't have to map out every single step. You just tell the agent what you need and it goes and figures out how to scrape the data, where to scrape the data from, how to find the contacts, how to generate personalized emails for each one. Of course, it may need to ask you, okay, what services do you want to offer them so that I can make the personalized emails better? So, if I showed you these two approaches side by side, here's kind of what they'd look like. A traditional automation might look like this. You've got a trigger, you've got a tool call, you've got maybe another tool call, you've got an AI step, maybe some conditional logic, and then a final output to Slack or ClickUp or wherever with an agentic workflow, you give it the input, and maybe you answer a question, and then you get your output. That's it. Now that you understand that concept, let me show you what that actually looks like in practice so that you can see how simple it really is. All right, so even if you don't know how to code or if you've never touched an IDE before, you're going to be just fine. IDE stands for integrated development environment, and you don't even need to know that it's said for that. So, what we're going to do is we're going to get into cloud code and I'm going to walk through everything that you need to know because it can be a little intimidating, but I'm going to show you exactly what you need to look at and what you don't need to look at. And by the end, it's going to be so much easier than you probably thought. So, the first step is you need to go to Google, search for Visual Studio Code, and then just download this. It's completely free to download. And this is where we're going to be using Claude Code. Once you open that up, this is what it's going to look like. And the first thing that I want you to do is go over to the lefth hand side and click on extensions. And once you get in here, just search for Claude Code. And then when you click on that, it's going to allow you to install the Claude code extension for VS Code. And that's how we actually use it. So if you don't have a Claude plan, you are going to have to go get on a paid plan for Claude. You can start at 17 bucks a month. And this actually allows you to get Claude code as you can see includes Claude code with Opus 4.5. So you do have to be on a plan. And then once you open up the extension in here, it will prompt you to log in with that email that you have that plan for. And then it will basically sync it over everything over here and you'll be able to use it. So the next step then is to open up a project. So on the lefth hand side, instead of clicking on extensions, you're going to click on explorer. And this says you're not in a project yet. You don't have a folder open. You need to open one up. So I've got a folder right in here called Aentic Workflows Demo. And that's the one that I'm going to open. If you don't already have one made, just go ahead and create one first. And then you can open that up. And so you'll see if I click into this one, there's nothing in here. It's a completely blank project. So I'm going to select that folder. And now we have this right here. So this is our file explorer. This is where we can see Agentic Workflows demo. And then on the right hand side, what I'm going to do is click on this button up here, which looks like the Claude logo, and it says Claude Code open. So I open that up. I'm going to close out of this main window. And now what we have is Claude Code, which kind of looks like a chat GBT or a regular Claude interface where we can talk with our coding assistant. So this is what your screen should look like. Once you get here, let's talk about what comes next. Okay, the environment that we're currently working in, cloud code within VS Code. On the lefth hand side, we've got our files. So in ours right now, we have one called Aentic Workflows demo, but there's no other files in there. This is where cloud code will actually build workflows for us and build files and things like that. And we'll see them populate on the lefth hand side. Now on the right hand side, this is where we have our chat interface with the agent itself. This is where we do our planning. This is where it asks us questions. And this is where it actually executes his actions. And once again, we'll be able to see all of that live. So now I wanted to tell you guys about the framework that we're actually using today to build ouric workflows. It's called WAT, which stands for workflows, agent, and tools. So the agent itself is cloud code. That's what we talk with. That's what the AI brain uses to build workflows and tools. The workflows are going to come in a format called markdown, which just looks like this. It's natural language. It has headers and it has bullet points and bold font just to make it easier to read, but it's literally just a natural language document. And then the tools come in Python. So, this is the logo. It'll be a py file, which I'll show you guys. And this is the ugly stuff. This is where we actually have code that I don't really want to look at. You guys don't want to look at. But luckily, we don't have to. So, what's the difference between these workflows and tools? Well, workflows are processes and tools are actions to take. So, let's go back to our analogy of like, you know, food and maybe making a cake. So, when you want to make a cake, you've got a recipe and then you've got a bunch of ingredients and you have to figure out what to do with them. So, basically, the agent is a chef and the chef needs to make a cake. The chef is going to either read a pre-existing workflow, which is a recipe to how to make the cake, or the chef is going to build its own recipe. And within the recipe, it'll say, you know, like crack two eggs into a bowl, add a cup of flour, whatever. Those are the tools. So, eggs are tools. Flour is a tool, sugar is a tool. And so that's how the chef, the agent uses a combination of recipes, workflows, ingredients, tools in order to make something which is either a cake or an agentic workflow automation. So now that you guys understand that framework, we need to make sure that claude code understands that framework. So what I'm going to do is I'm going to drag in a file. And this file will be available for download in my free school community. The link for that is down in the description. And this is our claude.md file. So every time that you set up a new project in cloud code, you have to give it a cloud.md file. They won't always be the same, but when you're building agentic workflows and you're using a WAT framework, you can just use this and copy and paste it every single time. This is basically telling Cloud Code how to work. This is its job instructions and description. So, if you were to go get a job at a grocery store on your first day, they wouldn't just let you loose. They would say, "Hey, we're going to get you onboarded. Here's what you do. Here's what you wear. You know, here is specific tasks you do." So, here we're telling the agent, you're working inside the WAT framework, which stands for workflows, agents, and tools. We have three layers. The first one is workflows, which are the instructions. The second one is agents, which is you, the decision maker. And the third one is tools, which are the executions. So we talk about why this matters. The AI tries to handle every step directly. Accuracy drops fast. So if each step is 90% accurate, then you're down to 59% success after just five steps. So basically, we're just explaining why we're doing it like this. We then talk about how to operate. So you look for existing tools first. You learn and adapt when things fail. You keep your workflows current. Blah blah blah. We've got a self-improvement loop. And then we've also got a file structure. So, like I said, Claude Code when we're working with it is going to create files. It's going to create tools. It's going to create maybe temporary docs to look at notepads. And when it does this, it adds them on the lefthand side. So, if we don't tell Claude how to organize its files, it's going to get messy quick to the point where I don't understand where things are, and neither does Claude code. So, we're just giving it a nice structure for workflows, tools, temporary files, things like that. And so, obviously, you guys can read this whole thing. I don't want to spend time reading this line by line. Really the moral of the story here is this helps Cloud Code understand the framework that we want to use, how to build workflows, so that when I'm talking to it, we're on the same page. All right, so what we want to do now is have Cloud Code read that and then set up our actual file structure. But before that, I wanted to show you guys one thing at the bottom when you're talking to Cloud Code, which is the mode. So you can be on ask before edits, you can be on edit automatically, you can be on plan mode, and you can be on bypass permissions. So all of that just gives Claud code a different level of autonomy. If you don't want it to do anything and you just want to make a plan, you start with plan mode. And this is really important. And I'll show you guys how this works. If you wanted to ask for edits, you can have it do that. If you want it to just edit automatically, you can have it do that. Or if you want to have it just bypass permissions and just completely go, then you choose that. Now, if you don't see this option in yours, you have to go to your settings and then you'll type in claude code and then you will enable allow dangerously skip permissions. And I only really like to do this if I'm sitting next to Claude watching it work. And if I realize it's going off track, I can just kind of poke it and steer it back in the right direction. So, now that we've covered that, I'm going to be on bypass permissions mode and I'm going to say, "Hey Claude, I just gave you a claude.md file. I want you to go ahead and set up this project so that we're ready to go. We're ready to build aic workflows together. So as I shoot that off, you can see that this is very similar to chatbt except for in here we can see everything that it's thinking and doing. So let's just start from the top. It says, "Okay, cool. Let me read the cloud. Mmd file to understand the project setup requirements." And then we can see it actually did this action. It read it read this file that lives here. And now it said, "Okay, cool. I understand the WAT framework. Let me check what already exists in the project. And then I'll set up the required structure." As you can see, it then goes to list the current project contents. And there's nothing. The product is empty except for the claw.md file. Let me set up the right structure. As you can see, it creates a to-do list. It thinks, it searches, it updates the to-dos, and it basically goes through this step-by-step process that you can have full visibility into and see what it's doing, see how it's thinking until we're done. So, now it looks like the to-dos are pretty much all the way done. And it's going to come back and say, "Nice, we're all set up. What do you want me to do next?" And you can actually see in real time on the lefth hand side, we now have different files. We've got our temporary folder, we've got our tools folder, we've got our workflows. Obviously, there's nothing in these yet besides just some read me and some basic stuff, but that's why we gave it this folder structure so that it keeps it organized and it doesn't just throw a bunch of things in a random order. Cool. So, it says ready to build a workflow. Let me know what you want to accomplish. Awesome. So, we're all set up. Let's actually start talking about the workflow that we want to build. So, what I've got is this list of remote jobs. So, I searched for social media. There's 622 remote jobs. And let's say I want to apply to all of these. Well, that would be really tough to log all of these manually. And there's multiple pages. There's 21 pages of these jobs. So, what we can do is we can have Claude code look at this stuff, get all the jobs we need, and then put it into an Excel sheet for us. And for this, we're going to be using a tool called Firecrawl. Firecrawl lets us do tons of different actions, and it lets us basically take a website like McDonald's right here. I can drop in the URL, and I can ask for a scrape, and it's going to go ahead and grab all of the information from that website. So, in this case, I just requested markdown, and it just pulls back all of the text from the website. As you can see, it is a lot more powerful than that. It can turn websites into LLM ready data. Whether that's scraping the data, getting screenshots, mapping the data, crawling the data, searching, extracting. There's a lot of different things that we can do with firecall. Now, the thing is we want to just say, hey, cloud code, use firecrawl. Just go after it. Use whatever the different tools that firewall offers in order to accomplish the job that I've got for you. And so, we do this using a framework called MCP, which stands for model context protocol. Now, I know that this may just sound like some tech jargon or some gibberish. So, let's try to contextualize this a little bit. Think about Gmail for example. In Gmail, you've got an action to send an email. You've got an action to draft an email. You've got an action to get a bunch of emails. There's so many different tools within that tool. So, MCP basically says, "Okay, cool." The agent is going to figure out how to use all the tools, when to use all of them, what parameters to fill over, all that kind of stuff, so that you, the human, don't have to think about that. So, if we go back to our example of making a cake, let's say we realize, okay, so for this cake, we need eggs, flour, and frosting. Okay. Well, how do we do that? Well, let's just give our agent access to the supermarket MCP and say, "Okay, whenever you need a new ingredient, just go to the supermarket MCP, grab what you need. I don't really care. Just figure it out and then come back with the right ingredients." Rather than saying, "Okay, cool. So, like eggs, let's go to the egg store. Flour, let's go to the flower store. Frosting, let me go to the candy store." We're just going to get everything in one spot. And that's the power of MCP. So, in Firewalls docs, you can see that they have an MCP server, which is amazing. And this lets us get stuff like web scraping, crawling, searching, all this kind of stuff that we want in any of the tools that we want to use. So right here I can click on running on cloud code and this shows us how to add the firewall MCP server using the cloud code. So what I'm going to do is go ahead and copy this message right here. I'm going to come into cloud code and I'm going to clear out this conversation history and I'm going to say hey claude I want you to help me install the firecrawl mcp server. You need to install it using this command in the cloud code. And then I paste in that command. Now what you'll notice here is that it's prompting us for our API key. And so an API key is basically like a password. And I don't actually want to give it to cloud code. I don't want my API key to be stored in the conversation history of cloud code. I want to just put it into the file or into the project locally myself just to do it a bit more secure. So I'm going to say go ahead and get this initialized but I'm not going to give you my API key directly. I'm going to put it into the EMV file. So help me get that set up as well. So I shoot off that message. It's going to think about how to actually help me set all this up. So it's checking in on the existing file. It's checking in on our project configuration. And then it's going to help us actually do this. Okay. So it said that I've added the API key placeholder to your file. Now you just need to add it there. So the env files on this lefth hand side. I'm going to open that up and you can see now it says cool firewall MCP server. Put in your API key right here. So I'm going to delete this. I'm going to go into firecrawl and I'm going to go to my dashboard. So if you haven't already sign up for firecrawl you can get started for free and you can get 500 credits right away which is more than enough to play around with. And then you can see right here API key. So I'm going to copy this. I'm going to paste it right here into the actual MV file. And then I'm going to go to file and save this to make sure that it actually gets saved on our project. And then you can see it says then run the MCP add command. And it gives me all this reasons and I actually don't understand what this means. So I'm just going to ask it to see if it can do it itself. I don't exactly understand how to do that. I have added my firewall API key to the ENV file. Would you be able to actually just run this command to make sure we can install the Firecol MCP server? Okay. So went ahead and installed the MCP server. Now something I did want to bring up is that when it actually runs that command with this bash operation, it does put in the API key right here which technically will be stored in conversation history. So, in this case, we're fine because this is a free key. It doesn't have much access, and I'm probably just going to rotate it right after this video. So, what you would want to do in an environment where you have a key that has a lot of risk is you would want to have Cloud Code just walk you through how you can run it in your own terminal in order to make sure that Claude never actually touches the key. But still, best practice, always store them in AENV. That way, if you ever are pushing something to a public repository anywhere or someone gets access to your files, then that's all going to be encrypted. So, now we are pretty much set up for actually starting to build this agentic workflow. So, I'm going to do a slashclear, start a new conversation. And what we're going to do is, like I said earlier, we're just going to explain in super clear natural language what we want. So, I'm going to go ahead and switch this to plan mode, which I would always recommend doing before you actually start building an agentic workflow. I'm going to go back into this tab and I'm going to grab the URL from this page where we have 622 job opportunities here for social media. Coming back into Cloud Code, I'm just going to start talking to it. So, I'm going to paste in this URL and I'm going to say, "Hey Claude, I just gave you a URL for a website that has a bunch of job opportunities. There are about 622 job opportunities here, but they're spread across different pages. So, there's like 21 total pages. And what I want you to do is go ahead and scrape all of those for me, and I just want you to put those into an Excel sheet so that I can actually look through them, you know, do things with them. Make sure you're getting all of the relevant fields that I may want. So, that's kind of my overall plan. Let me know if you can help me make that project requirement more robust, and you can feel free to ask me any questions that you may have for me to make sure that we can build out this workflow in a really high quality way. So, just shot that off in plan mode. So, it's going to do a lot of thinking. It's going to reason about like the way that we should actually do this. Hopefully, it understands that it can use Firecall's MCP server. As you can see, it's searching that right here. And then what it's going to do is it's going to come back to us with tons of questions, I'm sure. And then it's also going to come back to us with a plan. So, once I'm able to approve that plan, it will start actually building the workflows and the tools for us. All right. So, here we are with our first round of questions. Do you want me to also scrape each individual job detail for more complete info like company name, full description, benefits, or just the listing? So for the for right now, we're just going to go with the actual listing. For the output location, can we So output location, it says where should I save the Excel file. I'm just going to go ahead and do a local in the temporary folder, which we've created right over here. So I'll choose that. And then for filtering, do you want any filters applied or should I grab all 622 job posts? Now, what I'm going to say here is other and I'm going to go ahead and grab all the jobs, but I'm doing this as a demo for how to build an agentic workflow. So just go ahead and grab only 200 for now just to prove that this concept works. So, I've submitted those answers and now you can see it's going to keep on going with its plan. So, we just got back this plan. I'm going to go ahead and give this a quick review, but also for the sake of the demo, I want to see how well it did on the first shot. So, realistically, what I would do is I would go ahead and read this whole thing, and if there were any adjustments to be made, I would go ahead and make those. But what you can see is it gave us a pretty comprehensive plan of what it's going to do. It's going to create a tool called scrape daily remote. It's going to create a workflow called scrape job listings, and then it's going to actually execute that scrape and get us all this information. So, I'm going to go ahead and say yes and auto accept. So, it just spun up that to-do list. It's going to start going and I'll check in with you guys when that's done. All right, that just finished up. We can see that we got 209 done. We have different metrics here. We have different locations and also created a tool and a workflow. So, if I open up this folder, we can see we now have a scrape daily remote jobs tool. And in the workflows, we now have a scrape job listings workflow. So, basically meaning next time we ask it to scrape jobs, it's going to be able to do it better and it has more direction because it's already done it. And if it has any mistakes, it will update the workflows and the tools so that the next time it's even better. But let's go ahead and take a look at the output. So, it said that it stored it in the temporary file. So, right here, temp. And we've got social media jobs, Excel. All right. So, this is the Excel sheet that it created for me. I'm going to go ahead and zoom out a little bit so we can see. But, it's got like different filters on here already, which is pretty cool that it did all this itself. We've got job title, we've got job type, position, location, experience, category, salary, description, summary, tags, we've got the actual URL, and that's pretty much it. And like it said, it was able to get us, I think it said 209. So, yep, this is 209 total job postings just like that. So, we're going to go ahead and try a different use case now that uses the firewall MCP. But what I wanted to show you guys is down here we have context. So, it says 45% of your context is remaining until it will auto compact. And so, you guys have might have heard of something called context rot. That basically just means the more context that you have in a conversation history with an AI model, the worse it kind of gets. So, typically whenever my context gets over 60, I'll probably just compact it or reset and then keep going. So, what I'm going to do is I'm just going to go ahead and click this right now, and it's going to compact our workflow and all of our conversation history. So, we can keep going, but it still remembers the important things that we've done. All right, so that's been compacted. So, it basically just summarized everything that we've done. So, let's try something else now. Let me look at this same website, but now there's no search filter. So, there's 214,000 jobs. I'm just going to take the URL and I'm going to go into Cloud Code and say, "Hey, Claude, I've got this URL that I need help with. I want you to basically be able to scrape this. I want jobs that are sales opportunities or sales jobs and I want to look just in Europe. Scrape all of this. I want to get like 500 jobs back and put it in a nicely formatted Excel sheet for me. Now, this time what I'm going to do is we're just going to go ahead and do bypass permissions. And like I said, normally you want to go on plan mode. You want to ask questions, but I just want to show you guys what this might look like with a pretty vague prompt and just letting clog code go after it. And hopefully it's able to do a better job now because it understands how it can scrape job listings and it has this tool and it may even have to create a new tool. So, let's just kind of let it run. I'm going to analyze what it's doing and then I'll report back once we see what it actually ended up doing and start off by saying, "Cool. I have a scraping tool from before that I can use." So, here's my plan. I'm going to scrape the sales jobs. I'm going to filter for Europe and then I'm going to export it all to an Excel sheet. It found 409 total sales jobs and now it has to filter for Europe. And when it did that, it basically found there's a limitation. There's only 52 sales jobs in Europe, but there are 409 total. So, let me check if including worldwide would help get closer to the goal. So, what's going on here? It has our natural language request which was that we wanted 500 sales jobs in Europe. And it realized, okay, this actually isn't going to work. Let me brainstorm and see what else I can do. And now you can see what it did is it asked us a question because it wants to help us reach our end goal better. So it says, do you want me to expand the search to get more jobs? We could either keep the 55 sales jobs in Europe, we could broaden it to all job types, or we could also do US-based sales jobs. So I'm just going to go add US jobs as well. And now it should keep going. And hopefully now you can also see why I wanted to give it a temporary folder because in this operation where it's running into a few issues, it's creating some other temporary files like all sales jobs, sales jobs raw, it's also created three different Python tools that are temporary tools just because it knows that it needs some help filtering things out. And now it was able to find 372 sales jobs and it was saved to sales jobs Europe and US. So it says that we've got 372 sales jobs. I'm going to open up that Excel sheet and we can see if we scroll all the way down, we should have gotten 372. Perfect. And this is also similar because we can filter up here with all of these pre-made filters that it put in. We've got job title. We've got all this information. And it said that it added a region column right here. And this is where we could get rid of US and mixed and worldwide. And we now should see that we've only got about different actual rows. Yeah, 49. And just as one final test, let's see what happens if it gets a crazy type of request that we haven't really prepared it for. So right now, this is good at scraping jobs from a given URL. What if I just said, "Hey Claude, I'm looking to reach out to tons of dentists. Can you find me dentists in the United States and give me their contact information so that I can basically just build up a lead list of dentists that I can contact? I want this to be in an Excel sheet. I'm going to shoot that off. Once again, this is in bypass permissions mode. So, we're going to see what it does. We're going to see if it uses firecall. We're going to see if it thinks about, hey, actually, I can't use firecraw. I need to get access to some sort of like, you know, lead generation API or lead list API. We're going to see what it does here. It said, I can help you build a dentist lead list. Let me first check what tools and workflows exist. Then it says, let me check the available APIs. you have fireall available and I can build a similar tool to your existing job scraper but for dentist leads instead. All right, so look at this. It used firecol to search for dentist directory and then it started scraping those sources. Once again, the ADA site uses JavaScript to load results dynamically. So the static scrape doesn't work. Let me try a different method. So it found out yellow pages works well. There are 3,000 dentists just in New York City. And now I'm going to create a scraping tool so I can actually do all of this. Okay, so it looks like this is finishing up here. And what you guys can see is that it created a new workflow which is scrape dentist leads. and it created a new tool which is scrape dentist leads. All right, we ran into another issue. Only two dentists were found. The parsing might need adjustment. Let me check what was captured and then refined the reg x pattern. So look how awesome this is. It found the issue right here. And now what it's doing is it's fixing the tool. So it's updating the tool so that it doesn't actually run into that issue again. Okay. I mean look at this. It said done. I've scraped 120 unique dentist leads from four major cities. Here are the cities we got. It includes all of this data which is awesome. And then it says for future scrapes I've also created a reusable tool that you can run any time. So, I'm going to open up that Excel sheet right here. And we can see that we do indeed have all of these different dentists here. It even formatted the Excel sheet a little bit, but we have phone number, we have address, we have city, state, zip code, website, specialties, and we get the actual listing URL as well. So, this is incredible if you think about the fact that I didn't know what tools to use at all. I could have put this in plan mode and I could have said, "Hey, this is the workflow I want. Ask me questions, do research, figure out the best approach, and then I could have, you know, went back and forth with a little bit." And this scrape might have even been better. But on the very limited amount of information I gave it, it still gave us a really good output that would have taken me so much longer to get manually or building it in an end. Because the truth is a lot of us know what we want. We know the end result, but we don't exactly know the exact tech stack and all of the different things that we need to get that end result. So why not let an AI agent with a really smart brain like Opus 4.5 figure that out for us, look at five different approaches, and then pick the best one. And the cool part is you're not just limited to one agent. You could open up five different agents in here. As you can see, we could just keep stacking agents on agents. And then what I could do is I could just tell all of them to try a different method. So I could have four different workflows running and then I could test all four at the same time and whichever one gives me the best result, I would just delete all the other agents and then stick with that main workflow. So before we wrap up here, I actually wanted to just contextualize one more time what's going on. So let's take a look at that first workflow we did. This one was called scrape job listings. This one says the objective is to scrape job listings from daily remote.com based on a search term. The required inputs are search term, max pages, output path. The tools to use are just this one, the one that we created called scrape daily remote jobs. And then it goes through the exact steps and the exact outputs, edge cases, error handling, all of the stuff that I didn't tell it to do. It's because of the framework. And it's because it understands how to fail safely. So that every single time whenever we say, "Hey, I want you to scrape leads from daily remote." It just invokes this workflow which inside of it invokes the tool. So it's the exact same thing that just happened for scraping dentist leads as you can see. So that's how simple it is to build an agentic workflow. But before you go to actually build one by yourself, you do need to understand the mistakes that most people are making right now. Because understanding how to think about these systems is what's actually going to make it valuable to you. So the first mistake is not being clear enough about the actual goal. You can't just say, "I need a lead scraper for LinkedIn." That's way too vague. Agent will have no idea what kind of leads you want, what industry, what role. It'll just start pulling random profiles. Obviously, it can ask you questions, but you do need to be specific about the problem that you're actually trying to solve. And so, what you're going to want to do, as you saw a little bit in the demo, is put the agent in plan mode and say something like, "Hey, here's a rough idea of what I want. help me turn this into an actual solid PRD or project requirement doc. The agent then will have to brainstorm and it will reason and it will think and it will maybe even do research for you and it will ask you all the right questions so it knows exactly what to build. Just like the way if you wanted to give an actual human software developer specs for an app or for a workflow or whatever it is. You would have to give them enough information so that they could actually build that. You're totally allowed to treat the agent like the expert. You're just the manager to make sure that you keep it on the right path. So mistake number two is not defining what done looks like. Agents need to know when to stop. If you don't give them the clear finish line, then they may over complicate things or break things or keep researching or keep looping, keep iterating, and they might just keep wasting time when the answer was actually simple. I've definitely seen agents overcomplicate a lot of things. So, instead of saying, "Search for LinkedIn profiles of CEOs at tech companies," which is pretty open-ended, say something like, "I need exactly 75 LinkedIn profiles of CEOs at tech companies. Put them in a spreadsheet with their name, company email, their link to their profile, and once you have 75, you're done. It's a clear input and a very clear output, and that's how you're going to get consistent results." So now let's talk about why agentic workflows are just better. First, no more debugging loops. With traditional workflow automation, you'd build something, you'd run it, and then there would be some edge cases that you didn't think of, and that would break the system. So then you'd spend the next hour reading through the logs, looking at the error messages, looking at the execution data, and trying to figure out what went wrong and why. With a Gentic Workflows, the agent basically handles all of this for you without even asking. You saw it earlier in the build as the agent was working, it would run into some sort of roadblock or it would hit an error, and it would just say, "Okay, this is what happened. Let me think about what I could do differently." And then I fix it. And then I update my workflows and my tools so that it doesn't happen again. It's basically self-healing. And that's a massive time saver because this means that I can have an agent on my right monitor building stuff and then on my left monitor I can just be doing different work or maybe even watching a YouTube video or catching up on my favorite show and I've got Claude code right here building things for me and all I have to do is sit here and make sure I can poke it in the right direction if I need to every once in a while because at the end of the day it is AI and it is nondeterministic. So it might veer off the path a little bit. Second is natural language control. With tools like end you had to pretty much learn every node. You had to know what each one did, when to use each one, and what all of the different parameters or settings meant. If you wanted to connect to an API, you had to read the API documentation. You had to find the right endpoint. You had to structure your JSON correctly. You had to set up the authentication. And that could be a lot, especially when you are new to the space. With the Gentic Workflows, you just explain what you want, and the system will look at all the tools available, whether it has an MCP server or not, or whether it just has to look through and research the API documentation on its own. And this is absolutely beautiful. Third, it gets smarter over time. So, I know we've talked about this a lot, but it's just so cool. If you wanted to update an automation in the past, you had to go in and you had to change the nodes and you had to configure it manually. With Agentic Workflows, every time the agent runs into an issue, it learns and it updates. Now, there is one important caveat that I wanted to talk about, which is the difference between automations that you trigger yourself versus automations that run on a schedule. So, if I'm sitting at my desk using Cloud Code and I say, "Hey, you know, we just had a call. Go ahead and write up a proposal for client B." That's a human triggered event in this case, and the agent's right there with me. So, I can watch it. I can talk to it. And that's how it's able to self-heal in real time. But if you want something to run on a schedule like every morning at 6 a.m. or maybe an event trigger like whenever someone submits a form on your website or something like that, that's actually going to be you deploying that code, not the actual agent. So the agent would deploy its workflows and tools, but not itself, not the cloud code model that lives in VS Code. And the agent is what actually makes the workflows and tools self-healing. So you're not deploying that. But anyways, I'm not going to dive deep into that right now. That's a whole other video. You can also check out this video which I will link right above up here where I go into pretty much that whole process of building an automation in cloud code and then actually deploying it just so you guys can see what that actually looks like. So look, I know that this might feel a bit overwhelming at first. The space is moving really fast, but the reality is that this is just the beginning. We're definitely headed towards like fully autonomous workflows, agents managing other agents and systems that improve themselves while you sleep. And the ones who understand how to make them faster will be ahead in this automation market. So I don't want you to worry if you're still learning how to make automations or you're still learning to end. Your job isn't over. And I think that's a really good place to start. Now, we're just moving from builders to architects. The key thing that matters is how you adapt to new challenges. And if you want to make it easier, you can check out my free community with over 200,000 AI builders like you. And I put everything that we talked about today into a completely free resource guide you can access in that community. Link for that is in the description. And if you're looking to dive deeper into this kind of stuff and you want to get unlimited tech support and weekly group calls with me, then check out my plus community, which is also linked in the description. But anyways, that's going to do it for today. If you guys enjoyed the video, please give it a like. It definitely helps me out a ton. And as always, I appreciate you guys making it to the end of the video. I'll see you on the next one.