How to Build & Sell AI Agents in 2026: Ultimate Beginner’s Guide

Three years ago, I taught myself how to build AI agents without any prior experience in AI. And since then, I've built multiple AI businesses that have generated over $10 million in revenue, grown from 0 to 700,000 subscribers here on YouTube, and I've built AI agents for MBA teams, for Fortune 500 companies, and even publicly traded ones as well. So, it's safe to say that learning how to build AI agents has completely changed my life. So, in this full course, I'm going to teach you everything that I've learned over the past three years about building and making money with AI agents, even if you don't know how to code. My goal here is pretty simple. I want to give you the skills to build the life that you want before these AI agents start taking jobs away from us. Now, as you can probably tell by the length of this video, I'm not going to be holding anything back here. So, the whole thing is split into three different chapters. Firstly, we're going to be building your foundational understanding of AI agents. What they are, how they work under the hood, and the key concepts that you need to know before starting to build them. And no, there's no technical background required for any of this. Secondly, we're going to dive into four endto-end AI agent tutorials where I take you over my shoulder step by step as we build some of the most in- demand AI agent types on the market right now. And finally, I'm going to be giving you my proven blueprint for turning these AI agent building skills into real income while this technology explodes. So, let's get into it. But if you are new to the channel and don't know who I am, my name is Liam Mley and about 3 years ago, I knew absolutely nothing about generative AI. And now I run Morningside AI where we build AI systems for publicly traded companies and even MBA teams. My last AI agent course like this has over 2 million views. And through those videos and my community and my accelerator, I've helped tens of thousands of people to learn AI skills and build a real business around it. Right? And so if you look at how long this video is, you'll realize that there is a lot to cover. But I do not want you to give up halfway through. So before we jump into the technical stuff, let's first get clear on why this actually matters. Why AI agents are genuinely one of the most valuable skills that anyone can learn right now. So stick with me on the spot. But here's the hard truth about AI and jobs. McKenzie released a report in November of 2025 with a pretty shocking finding. 57% of all the work that Americans do today can be automated with the technology that already exists. Not technology coming in 5 years, technology that we have right now. And the World Economic Forum is projecting 92 million jobs displaced just by 2030 alone. Now I know that sounds terrifying and honestly it should be and I don't like using this kind of stuff on my channel if I'm honest. But if you're planning to do nothing about it, then there's an issue. But interestingly, here's what those same reports found where they looked at the other side of the data. They found that workers who have AI skills are earning 56% more than people who don't. And that gap in pay has doubled in just one year. 66% of employers are actively trying to hire people who understand AI. So on one hand we've got this massive automation coming and job loss while on the other hand companies are desperate for people who actually understand this technology and can help them use it. So the difference between getting replaced and getting ahead comes down to one thing actually just learning the stuff. And here's the gap that creates such a huge opportunity right now. Only 12% of workers have taken any AI training at all. And that's not just a little gap that's a canyon. And if you don't believe me when I say that a little bit of self-study on the stuff goes a long way, here is Navar Ravakant, one of the world's most respected investors and technologists on the Allin podcast. Again, I would say the easiest way to see that AI is not taking jobs or creating opportunities is go brush up on your AI, learn a little bit, watch a few videos, use the AI, tinker with it, and then go reapply for that job that rejected you and watch how they pull you in. This video is exactly what Naval is talking about. So whether you're an entrepreneur wanting to learn valuable skills to launch an AI business like I have, or you're a business owner wanting to understand what agents can actually do for your company, or you're just an employee who wants to make sure you're the last person the boss thinks about letting go because you're the only one who actually gets the stuff. Well, I made this video for you. So here's what I want you to do. I need you to close out of all your other tabs, grab a notebook, get your coffee or tea or whatever keeps you locked in, and make a commitment to yourself right now to actually finish this training. Right? So if you've done all that, let's get stuck into it. All right. So, step one in learning to build AI agents is actually understanding what an AI agent is in the first place. Because the term gets thrown around everywhere these days, AI agents, this AI agents that in every tech company is talking about them. But what actually is an AI agent? When I first started learning about this stuff a few years back, I found it pretty confusing, too. So, let me give you the clearest definition that I found that really helped it kind of click for me. An AI agent is a digital worker that can understand instructions and take actions to complete tasks. So in a simple way, just like businesses have employees who handle different responsibilities, AI agent is kind of like having a digital employee. But the interesting part is that you can build them yourself and make them do pretty much whatever you want. It's like being able to construct a human worker from scratch. And unlike human employees, these digital workers cost a fraction of what a person does. They can work 24 hours a day, 7 days a week. They never get tired. They never call in sick. and you can duplicate them instantly whenever you need more capacity. So, I'm sure you can see the appeal for businesses when they look at this versus humans. Now, here's why this matters right now specifically because all major tech leaders are aligned that 2026 are the year that AI engines go mainstream. You've got Satiana, the CEO of Microsoft who said at the start of this year, 2026 will be a pivotal year for AI. We've moved past the initial phase of discovery and are entering a phase of widespread diffusion. Jensen Huang from Nvidia saying the same thing. Sunda Pachai at Google said 2026 is when people will start using agentic experiences more broadly. The point is that this isn't just some far-off future technology. This is happening right now. And understanding how these things work puts you ahead of the vast majority of people. Now to understand why AI agents are such a big deal, we need to look at where we are coming from. Because you've probably encountered all sorts of chat bots before, right? You go to a website, there's a little chat widget that pops up in the bottom corner saying, "Hi, how can I help you today?" And so you type something in, it gives you some kind of response. These kinds of basic chatbots have been around for years. But here's the thing, they are pretty limited and they can really only do one thing, respond with some information. The way I like to think about it is this. A chatbot is like a waiter who can only recite the menu to you. You ask them what's on the menu and they tell you, but they can't actually take your order. They can't bring you your food. They can't process your payment either. They just give you information back. AI agents, on the other hand, are fundamentally different. Let me give you a concrete example to show you what I mean. Say you go to a website and you want to book in an appointment with that business. If you ask a regular chatbot about booking an appointment, it might say something like, "Our business hours are 9 to5 Monday to Friday. Please call us to book." And that's it. Just some information, and you still have to go and do all of the work yourself. But if you ask an AI agent the same question, it could actually check the calendar in real time, find available slots, ask you which time works best, and then book the appointment for you, and send you a confirmation email, and then update the business's scheduling system all automatically in a matter of seconds. So, that's the key difference here. Agents don't just respond with information. They actually take action and get things done. And this ability to take action is what makes agents so powerful and why everyone's so excited about them. They're not just fancy chat bots that sound a bit smarter. They're genuine digital workers that can do real tasks like searching through databases, updating spreadsheets, sending emails, booking appointments, generating documents, and honestly so much more that we're going to be getting into in the build section of this video. Building and deploying an AI agent is actually a lot like hiring a new employee. Because when you bring someone new into the business, you explain their role and responsibilities. You give them access to the systems that they're going to need, and then you trust them to handle tasks on their own. It's the exact same process with AI agents. You tell them what they're supposed to do. You give them access to the tools they need and then they can work on their own independently. The difference is that they work around the clock. They can be copied instantly and they cost a tiny fraction of what a human does. This is exactly why understanding how to build and deploy these digital workers is becoming such a crucial skill. Whether you're an entrepreneur looking to scale your business or an employee wanting to become irreplaceable at your job, knowing how to create these things is genuinely one of the biggest advantages that you can have right now. All right, so now that you understand what AI agents are and how they're different from basic chatbots, let's look under the hood and figure out how they actually work. Because just like humans need certain things to do their job, a brain to think, some memory to remember stuff, and tools to be able to work with, AI agents need specific components to function as well. There are five key parts that make up an AI agent. Let me walk you through on each one. First, every agent needs a brain. In the AI world, these are called large language models or LLM for short. You've probably heard of these already. We got GPT5 from OpenAI, Claude from Enthropic, and Gemini from Google. These are all LLMs. And you can think of the LLM as a super intelligent assistant who can understand your instructions in plain English and figure out how to get things done from there. It's the intelligent that powers everything else. And without the brain, all the other parts of this will be useless. Like having a desk full of office supplies, but no one in it to actually use them. Secondly, the brain needs instructions on how to actually behave. This is what we call writing a prompt. So writing a prompt is essentially how you program the behavior of your agent, but instead of writing complicated code, you're just writing clear instructions in normal language to them. This is what makes building with AI so accessible to people who aren't actually programmers. Because the way you tell an agent what to do isn't through code. It's through these well-crafted instructions that anyone could write. We'll get much deeper into this when we start building. Thirdly, agents need memory. Imagine trying to have a conversation with someone who completely forgets everything you said 30 seconds ago. It would be impossible to get anything done. Memory allows your agent to remember what you just talked about, keep track of tasks that they're working on, and build on previous conversations as well. The good news here is that most agent platforms handle this memory automatically for you in the background. So, you don't really need to worry about setting it up for yourself. Fourth, we have knowledge. Now, the AI models like GPT and Claude are trained on massive amounts of data from the internet, but that training data has a cut off point, and there's a lot of stuff that they just don't know. More importantly, they don't know anything specific about the business that you're trying to help. They don't know about their products. They don't know about their services. they don't know about the policies or your pricing. So, just like you're training your employee with company specific materials, you can give your AI agent additional knowledge through things like PDFs of documents, spreadsheets with product information, customer service transcripts, really any textbased information that you want it to have access to. Without this added knowledge, your agents would be limited to just general information and couldn't handle the specific tasks a business actually needs. And fifth, and this is the most exciting part, we have tools. Tools are what transform an AI agent from something that could just chat into something that can actually get things done in the real world. Think of tools like giving your digital employee access to different software and systems. Just like you might give a new hire access to your email, your calendar, and a CRM, you give an AI agent access to digital tools so they can take real actions. These tools let your agent do things like check real-time data, update databases, send messages and notifications, create documents, make phone calls, anything you can do on a computer, you can potentially give an agent the ability to do that as well. The really powerful part is when agents use multiple tools to solve complex problems, just like how we use multiple different websites and apps when we're doing our own work. Now, here's the practical takeaway from all of this. While an agent has these five components: the brain, the prompt, the memory, the knowledge, and the tools, you don't actually have to worry about all of these five things equally when you're building. The brain is handled by choosing which AI model to use. And honestly, any of the top models work great, and the memory is handled automatically by the platforms that you build on in most cases, which leaves you with what I call the three ingredients. the three things that you actually need to focus on as an AI agent builder. And that is prompting, how you instruct the agent to behave, kind of the glue that sticks it all together, knowledge, the external information that you give it access to, and three, the tools, the actions that you want it to be able to take. So, write that down. Prompting, knowledge, and tools. That framework is going to guide everything we do in the build section of this video. All right. Now, we need to go even deeper on tools because they're honestly the most powerful part of AI agents. Now, what separate a basic chatbot from a genuinely useful digital worker. But to really understand how tools work and to be able to build your own powerful agents later, we need to take a few steps back and cover some of the basics of how software and the internet actually work. Now, I know that might sound intimidating, but this is probably the most technical section of the entire video. But I promise you, once you understand this stuff, it's like having a superpower. It is the foundation of being able to build any kind of software and build apps for yourself and for other people. Everything else in this video is going to make so much more sense. So, stick with me here. So, remember how we said that tools are what allow agents to take action, to actually do things rather than just chat? Well, the way agents actually do work online is pretty similar to how you and I do things online, too. But there's one key difference. Instead of clicking buttons and typing into forms and navigating around websites, agents use something called API. Now, before your eyes glaze over, let me show you what I mean. Because you already use APIs every single day. You just don't realize it. So, every time you do anything on the internet, you're actually making dozens of what we call requests to APIs and getting responses back. It's all happening behind the scenes for you, though. So, let me give you a real example. When you click on this video to watch it, here's what actually happened behind the scenes. Your browser sends a request to YouTube servers, essentially saying, "Hey, I want to watch this video." YouTube servers received the request, found all of the data needed for this video, and then sent that back to your browser to you as a response. Then, your browser took all of that information, unpacked that data, loaded everything up in front of you, and then started playing the video for you. And that whole request response pattern happens with almost everything you do online. If you open up Instagram, you're sending a request for your feed to be loaded and the servers respond with all of those posts and stories. If you're sending a tweet, you're sending a request that contains your tweet data. Then Twitter servers save it and respond confirming that it worked. If you're checking your email, you're requesting your latest messages and Gmail servers are responding with all of your emails and your browser is loading them. It's just like computers talking back and forth, requesting things and responding to those requests. We get nicel looking websites and apps that make it all feel simple, but under the hood, it's all just this request and response conversation that's happening constantly. These requests and response happen through what we call APIs or application programming interfaces. And I know that sounds technical, but think about APIs as like waiters at a restaurant. You tell the waiter what you want. The waiter takes your order to the kitchen. The kitchen prepares it and the waiter brings your food back to you. An API works in the same way. They take your request to the server. The server does whatever work is needed and the API brings back that response to you. Now, there are two main types of requests you can make through APIs. And this is really worth noting down. You have get request which is when you're asking to get information from somewhere like checking the weather or looking up a stock price or loading this video. You're requesting to receive data. Post request on the other hand is when you are sending information for the server to do something with like posting a tweet or sending an email or uploading a photo. You are posting data for someone else to store and process for you. So you got get and you have post requesting data and sending data. Write those down because you're going to be seeing them a lot. All right. So, here's where this becomes super relevant to AI agents. AI agents use the exact same APIs as their buttons to do things. When we talk about giving AI agents tools, what we're really doing is giving it the ability to make these API calls themselves when they think it's necessary. Each tool an agent has access to is essentially an API that it can call to either get information or send information somewhere else. Now, these kinds of tools come in two different flavors. Firstly, you've got pre-made integrations. These are tools that someone else has already built and packaged up nicely for you into an API. These are things like Google calendar integrations or Gmail or Slack. The hard work of building and figuring out the API is already done. You just kind of plug into it and your agent can use it. You can think of these as like buying a ready-made meal. Everything has already been prepped for you. You just kind of heat it up and eat it. On the other hand, you've got custom tools that you build yourself. This is when you need your agent to do something specific that it doesn't have a pre-made integration for. In this case, you need to build the tool yourself, as we're going to go into later, and set up the API connection to your agent manually. You can think of this as like cooking from scratch. It's more work, but you can make exactly what you want. Both approaches work great, but when we get to the build section, I'll show you how to use pre-made integrations and how to create custom tools from scratch, and it's much easier than it sounds, especially with the no code platforms that we're going to be using. The important thing to understand right now is that any action an agent takes, whether it's sending an email, checking a calendar, updating a spreadsheet, whatever, it's doing it by calling an API by making one of these requests and getting a response back. And once you understand that, you start to see the internet completely differently where every action online is just request and responses. Which means that if you can identify the right API, then you can build a tool that allows your agent to automate almost anything. Okay, so now you understand that APIs are how the internet works. The next question is how does an AI agent actually know how to use a tool? Because if you think about it, you can't just point an agent at an API and expect it to figure everything out. It needs to know what the tool does, how to use it properly, and what information to send to it in order to get it to work. This is actually a lot simpler than it sounds. And it's one of the things that makes AR agents so powerful. So, for a tool to work with an agent, it only really needs to know three things. Firstly, what the tool does, which is a clear description of the tool's purpose, like this tool sends an email or this tool checks calendar availability. Secondly, what input the tool needs. What information does it actually require to work? An email tool might require the recipient's address, a subject line, and the message body. A calendar tool might require a date range to check. And thirdly, what does the tool actually return? So, what does the tool give back to the agent after it runs? The email tool might return a confirmation that the message was actually sent. The calendar tool might return a list of available time slots, and that's it really. You've got the description of the tool, the inputs it expects, and the outputs. Now, here's the cool part. The way you provide information to an agent isn't through complicated code. You're essentially just explaining how to use the tool in plain language, just like you'd explain it to a new employee. You write something like, "This tool sends an email. It needs three inputs. The recipient's email address as text, the subject line as text, and the message body as text as well. And it returns a confirmation that the email was sent successfully. The agent can read that description and it genuinely understands it. It knows what this tool is for, what it needs to make it work, and what to expect back from it." There's a standard format for writing these descriptions known as a schema. And I know schema sounds technical and scary, but it really is just a structured way of writing out those three things. What the tool does, what it needs, and what it returns. And the great news is that most of the noode platforms we'll be using actually generate these schemas automatically for us. So you won't have to write them from scratch, but it helps to know that they exist and what they're doing. Because if an agent is using a tool incorrectly, it's usually because the description isn't clear enough. Now, here's where the magic actually happens. Modern AI models like GBD5 and Claude aren't able to just read these tool descriptions. they can understand them well enough to figure out both how to use the tool and when to use it, when it should actually be triggered in a conversation. So, let me show you what I mean with a simple example. Say you've given your AI agent a tool with a description that says, "This tool checks today's weather for any city." The tool leaves one input, the name of the city, as text, and it returns the current weather conditions. Now, someone chats to your agent, says, "Hey, what's the weather like in Tokyo right now?" Here's exactly what happens inside the agent's brain. First, it looks at the message and understands that this person wants to know about weather. Then it looks through all the tools it has and it might have 10 of them and only one of them is the weather one and it sees one tool description mentioning weather. It thinks, okay, this is probably the one I need. Then it looks further and it checks what inputs that tool requires. It sees that it needs a city name. Then the agent looks back at the message, extracts Tokyo as the relevant city and makes the API call to the weather API with Tokyo as the input. The API goes off, gets the weather data, and then sends back a response, probably a bunch of technical data about temperature and humidity, conditions, all of that. And when the agent receives the response, the clever part is that it doesn't just dump all of that raw data on the user. It takes the information and writes a natural conversational reply like it's currently 72° and sunny in Tokyo with a light breeze. So what we see here is that the agent understood the intent. It found the right tool, figured out the inputs, made the call, and then translated that kind of technical database response into something actually useful for the user based on what they asked for all automatically. And this is what makes AI agents so different from the software that we've had before. Traditional software needs you to click the exact buttons and fill in the exact forms. Whereas agents can understand what you're trying to accomplish and figure out how to use their tools to get it done. When you really get this, you will never see technology the same way again. The combination of language understanding and tool use is genuinely a new kind of capability. And the better you get at setting up tools with clear descriptions, the more reliably your agents will be able to use them. In the builds later, you'll see exactly how to set up tools in practice. And you'll see the small changes to descriptions that can make a huge difference in how well the agent uses them. But for now, just understand the core concept. Clear descriptions are how agents know what their tools do and when to use them. All right. So now you understand how an agent uses a single tool. It reads the descriptions, figures out when to use it, grabs the right inputs from the conversation, makes the API call, and turns that response into a helpful answer. But obviously, having an AI agent that can only check the weather will do one thing isn't particularly impressive. The real power of AI agents comes when you give them multiple tools and the ability to use them together to accomplish their complex goals. Do you remember our definition from earlier? An AI agent is a digital worker that can to complete tasks. When you give an agent a task, it will try its best to complete it. But if it doesn't have the right tools available, it can't do much. It's like asking an employee to send a report to a client but not giving them access to their email. Doesn't matter how smart they are, they can't do the job without the tools. The more tools you give to an agent, the more flexibility it has to solve problems. And this is where things start to get really interesting. So, let me give you a real example from my own business to show you what I mean. Say I give an agent this task, which is find AI startups that have raised money recently, put them in a spreadsheet, add a summary of each business and email me the link when you're done. Now, that's not a simple one-step task. There's actually a lot going on there. And when you give an AI agent a complex task like this, and you're provided it with multiple tools, it will actually break down the problem and plan out how to solve it. Kind of like how you or I would approach it. The agent might think, okay, I first need to find AI startups who have raised money. So, I'll use the web search tool and I'll use that to run some searches about the AI startups and their recent funding rounds. Then I need to put them into a spreadsheet. I have the Google Sheets tool that can create new spreadsheets and add new rows. I'm going to use that for each company I find. I'll need to add them as a new row in the spreadsheet with their information. Then I need to add summaries of each business. I can write those summaries myself based off what I found in my research. Finally, I need to email the link. I have a Gmail tool, so I'll use that to send the spreadsheet link. And then it just does all of that step by step using different tools in sequence working towards the goal that you gave. This is when you really start to see why they call these things digital workers. They can plan out multiple steps and execute them in order, use different tools as needed. Basically approaching problems the way a human would. Now the really cutting edge stuff happening right now is with what are called reasoning models. These are newer AI models that are specifically designed to be better at this kind of multi-step planning and problem solving. What makes them special is that they don't just plan once and execute. They can actually reflect on their results as they go and adjust their approach if something isn't working. So let's say an agent runs that first web search for AI startups raising money and it doesn't get very good results. A basic agent might just push forward with bad data, but a reasoning model might think, hm, those results aren't that good. Let me try a different search query, or maybe I should search for recent funding announcements specifically. But it's that ability to plan, execute, reflect, and then replan that makes these agents capable of complex tasks without constant handholding. Now, I should be honest with you here, this technology isn't perfect yet. These multi-step tasks can be incredibly unreliable, and agents definitely still make mistakes on complex workflows. For anything important, you still typically want human oversight so that you can catch the errors before they actually cause problems. But things are moving incredibly fast and the agents that we can build today are dramatically more capable than what we had even a year ago. And if we look a bit further ahead, we're already seeing the next evolution, which is multiple agents working together. Instead of one agent trying to do everything, you can have multiple specialized agents that each focus on one thing. Kind of like having different employees with different job roles. So, you might have a research agent that's really good at finding information, a writing agent that's optimized for creating summaries and content, an email agent that handles all the communication stuff, and the cool part is that one agent could actually use the other agents as a tool. So, your main agent receives a task and delegates specific parts to the specialist agents like, "Hey, research agent, go find me these companies. Hey, writing agent, take this information and create summaries." Each agent focuses on what it does best, and together they accomplish more than any single agent could. This is exactly what companies like Microsoft, Salesforce, and Google are betting heavily on right now. Entire systems of AI agents working together to handle complex business processes. Now, we're not going to build multi-agent systems in this video. That's a bit more advanced stuff that you get to explore once you've got these fundamentals down. But I want you to know that it exists and where all of this is heading. For now, let's keep building your foundation. Next up, we need to talk about the different ways people actually interact and use AI agents. All right, so we've covered what AI agents are, how they work, and how they use tools to get things done. Now, we need to look at the different ways that AI agents are actually used in the real world because not all agents work in the same way. There are basically two main categories that you need to understand. First, you've got conversational agents, and these are agents that humans interact with directly. So, someone is actually there chatting with the agent, giving it instructions, asking questions, and getting responses back. You've probably seen these in a bunch of places already, like chat widgets on websites where you type messages back and forth, WhatsApp bots that you can text, Instagram DMs that respond automatically, or even voice agents that you can actually call and have a conversation on the phone with. In all of these cases, there's what we call a human in the loop that's actually sending messages to the agent, and the agent is responding. It's a back and forth conversation, just like you'd have with another person, except the other side is an AI. Open AI's custom GBTs are a great example of this. You can create an agent and then chat to it directly through the chat GBT interface. Secondly, you've got automated agents. And this is where things get a little bit more interesting because with automated agents, there's actually no human sitting there sending a messages directly. Instead, the agent is part of a larger system or a workflow and it gets triggered automatically when certain things happen. If you think about it this way, conversational agents wait for a human to talk to them. Automated agents wait for an event or a condition to trigger them and then they spring into action on their own accord. For example, you could have an agent that triggers whenever a new lead fills out a form on your website. The moment that form submission comes through, the agent wakes up, looks at the lead's information, does some research on them, decides if they're qualified or not, and then updates the CRM with its findings. All of this without any human pressing a button or sending a message directly to it. Or you could have an agent that runs every morning at 9:00 a.m. It checks your calendar for the day, looks up information about the people you're meeting with, and then sends you a briefing email before your first call. Again, completely automatic. You didn't have to ask it to do anything. It just runs on a schedule or a different trigger. This is a really important distinction because it opens up a whole world of different use cases. Automated agents can handle processes that happen constantly in a business. These are things that would be tedious or impossible for humans to monitor and respond to 24/7. In the building section of this video, you're going to get hands-on experience with building both types. We'll be building four different types of agent. Three conversational and one automated. So, you'll see exactly how each works in practice. Understanding this distinction is important because it shapes how you think about what's possible. A lot of people only think about chatbots and agents that you talk to, but some of the most valuable applications are agents that work completely in the background handling tasks that you'd never want to do manually. All right, so we are almost done with the foundations. Let's just look at some real world examples of how businesses are actually using these agents right now and then we'll do a quick knowledge check to make sure you've got everything here before we jump into the building. Okay, so before we wrap up the foundations, let's quickly look at some real examples of how AI agents are being used by businesses right now. Seeing these concrete use cases helps all of this theory stuff to click into place. And these are all the kinds of things that we're going to be building in the build section. First use case is co-pilots. And these are AI agents that help someone in a specific role to do their job more effectively. For example, a customer support sales rep could have a co-pilot that would be able to instantly search the knowledge base of a company for answers mid call. So it helps them to do their job. Same thing with a sales rep. They can have a co-pilot that gives them access to everything they need to know about the lead and maybe can do research for them and so on. In fact, in our final build, we'll be creating a sales co-pilot that was able to research leads and generate briefings so that the sales reps can walk into every call feeling fully prepared. Second major use case is lead generation and appointment setting agents. This is probably the most common business use case right now because it directly impacts revenue. When someone visits a website at 11 p.m. on a Saturday with questions, instead of having to wait until Monday and probably forgetting about it, an AI agent can immediately get back to them and answer the questions, capture their information, and even book appointments by checking calendar availability in real time. These days, speed to lead or the speed at which you can reply to leads is everything in sales. And the business who responds first wins the deal most of the time. These agents are starting to show up everywhere. Website chat widgets, WhatsApp, Instagram DMs, even over the phone. And the point is that 24 hours a day, they are always ready to engage potential customers. In build two, we're creating a website chat agent with some very powerful tools that handles lead generation for a solar company. Third use case is research and qualification agents. These are agents that gather more information about leads so a business can prioritize the best opportunities. Sometimes a lead comes in with just basic details, a name, an email, a phone number, not too much to work with. A research agent can automatically dig deeper, finding LinkedIn profiles, company information, pulling together context that helps the sales team know who they're going to be talking with. Or you can go even more direct and have an agent reach out by SMS or over the phone and ask them some qualifying questions. This is getting the information straight from the source. In build three, we're creating a voice agent that automatically calls new leads to qualify them, gathering that extra information to decide if they're a good fit or not before a human ever needs to get involved. And fourth is voice agents more broadly. This is one of the fastest growing areas in the AI space right now and the ROI with businesses has been proven. One of the startups retail AI is processing over 40 million AI phone calls per month. Businesses are using these voice agents to answer inbound calls, qualify leads, book appointments, and even make outbound follow-up calls as well. The economics of this stuff is pretty compelling with CLA reporting that the AI agent handles work that required previously 700 customer service reps with fast response times and higher customer satisfaction scores. as well. We've got voice AI woven all through the builds that we're going to be doing. So, you're going to get plenty of hands-on experience with it. So, these aren't hypothetical future applications. Businesses are deploying agents like this right now and seeing incredible results, and you're about to learn how to build them. So, let's do a quick knowledge check to make sure that everything in this foundations chapter has landed and then we can jump into building. All right, we've covered a lot of ground in this foundations chapter, but before we jump into building these agents, I want to make sure that everything has landed for you. Because here's the thing. If you don't have these concepts down solid, the build section is going to feel very confusing. You'll be following along but just clicking buttons without really understanding why. And that's not going to help you when you're trying to build your own AI agents later. So, let's do a quick knowledge check. I'm going to ask you a few questions, and you're going to genuinely try to answer them before we move on. Question one, what's the difference between a chatbot and an AI agent? Question two, what are the three ingredients you control when building an AI agent? Question three, what are the two main types of API requests? Question number four, what three things does an AI agent need to know about a tool to use it? Question five, what's the difference between a conversational agent and an automated agent? If you couldn't answer all of those confidently, I would genuinely recommend going back and re-watching the sections that you are fuzzy on. So, pause the video, go back, take some notes. Do not rush this. The foundations are what makes everything else click in this course. And trust me, once you get into the builds, it'll all make much more sense if you've got the stuff locked in. But if you're feeling solid on all of that, let's get into the fun part. Time to actually build some agents. All right, guys. So, jumping into build number one. We're starting off with the Telegram receipt analysis assistant. This is a pretty good general purpose assistant that can be created quite easily on Nin, as I'm going to show you. And it shows you how to do a few things like extract information out of images, which is a really valuable skill to have. It shows you how to set up a chat loop that works on Telegram, so you can do any kind of Telegram deployments for your apps. And most importantly, what I'm going to teach you here, this is actually the second time I've shot this tutorial because I went through it the kind of traditional way of showing you how to build these nodes one by one and stack them together and connect the variables. And I got to the end and I was like, that took a really long time to do a relatively basic system. And so I've decided to take you more on the route of what the future of AI automation and building automations like this looks like. And that is using AI tools to research and plan out your automations. That's step one. I've created a GPT for you that you're going to be able to use for that. And then feeding it into a spec writing tool, which is basically taking in all the research and planning you've done, uh, putting it into a few fields, and then it's going to print out a perfect NAT spec to actually pass to N8's AI, and it will actually do a lot of the hard work for you. So, we're shifting in the AI automation space right now from needing to know every single setup and and how to connect each and every node to I know generally what I want and I can use AI to help me explore and research and figure out the best way to do that. And then I'm going to be heavily using the NAT AI feature within it to build this out for me and when things go wrong, loop around and around and around until it gets to the point that I'm looking for. Now, this is going to be a much more future proof and honestly faster way of learning this stuff. So, I'm excited to show you some of the tools that I've put together to help you guys learn this faster. Okay, the first handy AI tool that you're going to be using in building out these automations is this GPT I've created called your AI automation CTO. This is as if you've got myself or someone else who's really familiar with NA10 um who can research the web and help you to explore and sort of ask you the right questions so that you can get to the right kind of concept for your NA10 automation. So to save time, I've put this through its paces already. But this basically has all of the NAT documentation as a knowledge base and it's been prompted to guide you through steps to extract the right information from you. So you can click this button here, help me plan an NAN automation idea and that will start the process that you're about to see that I've already gone through. The link to use this GPT alongside the other tool we're going to talk about and all of the resources for this tutorial are going to be on my school community in the first link in the description. Head to the classroom, head to the AI foundation section and you will see the full course for this video broken down there in chunks with the right resources at every step. So, make sure you go and grab that link now and you can follow along. But here's how you use this GPT to plan out an automation. So, in this case said, help me plan an idea and asked me a few questions. I've said this is an NAT automation for a Telegram based assistant. Nice. That's a good front door. uh sort of runs through and just sort of verbal diarrhea of all the stuff that it knows about it, repeating what it's found in the documentation and then breaks down a practical blueprint for it and then asks me directly what questions it needs to move forward. So I've gone down here and given it the feedback. It's triggered by a new Telegram message. It should be connected to a Google sheet. Should be able to take in pictures as an input of receipts and then be able to log that info in a spreadsheet for company expenses. If over $500, then email my CFO via Gmail. I want to be able to chat to the data in the spreadsheet as well. Then it processed that, looked at his documentation, and then planned out what's going to be in the Google sheet. This has actually gone a little bit more complex and I probably would have done this myself if I was walking you through, but this is going to be interesting to see just how much more complex using AI as our assistant, we can make this in even a shorter amount of time than we would have before. So, it starts planning out the NAT architecture, plans out the conversational structure, and then it gets down to the bottom here when it asks me two tiny details, the company default currency and the CFO email address. I've given those here. And then as you see on the side here, we have a different tool that I've created that takes in a bunch of fields about the automation purpose, the trigger details, the desired outcome, the specific providers, and the usage pattern, model preferences if you have any, and additional quotes. And what this does is runs it through a specific prompt that I've created in the background here that's going to take all that information and create the perfect prompt to pass into NAT. So I've got a kind of like lot of prompt chaining here going on, but this is set up to be as usable as possible for you as a beginner. So, so this GPT has been prompted to give you the right fields for this. So, as you can see here, I was just able to copy and paste in the automation purpose to here, trigger details, desired outcome, specific providers, and so on all over into this run the tool. And here we have our inate spec, which is spat out by this tool, which again, you'll be able to get it in the resources on school. And then I can just come in here and copy this command C. And you can see down here there's actually a button that allows you to switch. It'll probably appear like this for you. Um, it's actually better to click on formatted and grab the markdown formatted version. And then what we want to do is head over to init. You can come here to get started. Put in your company email and go through the sign up process until eventually you see the homepage and it looks like this obviously without all of the workflows in it. Then you want to come up here and click on create a workflow. And this takes us into the editor which will do a bit of an orientation in a second, but I just want to get this started as quickly as possible. So you can click on build with AI. You'll see that we get monthly credits here. And now this does have a limit on the number of credits you can use. I have this because I have a a business plan going. But if you come over to the pricing page, you can see that you can start a free trial with no credit card required and you can get up to 50 AI workflow builder credits that will allow you to get started as quickly as possible following the workflow that I'm walking you through here. Then all you need to do is paste in that spec from the tool that we had before. So scrolling down and grabbing the spec, hitting back over and pasting it in here and hitting submit. Now that's going to work away in the background using NAT's AI to analyze what you've asked it for. It's going to figure out the nodes that it needs and then it's going to throw them all on the canvas. Yours may vary, but I will provide the exact prompt that I gave it in the resources on school so you can try to follow it as closely as possible. But the thing about this is that you need to be a little bit more flexible with how you think about it. There's no one right answer necessarily. There's definitely simplified versions. This one that I'm looking at here that it's generated for me. It's definitely on the more complex side of how you could approach something like this, but often the AI will actually opt for a lot more strict and kind of nonI features to make it a lot more deterministic. Now, this is important because AI models themselves, language models are non-deterministic. that means that you can give it the same input and there's no guarantee that it will generate the exact same output which is going to be a bit of an issue when you're trying to build reliable business systems and so the N8I leaning towards uh things that are much more deterministic where possible means that it tries to shrink the room for error down which is actually a good thing and good practice in building automation. So while this would be possible to do a lot simpler I'm going to kind of go with the flow here when you're using the N810 AI builder it'll leave you with a few things here like kind of to-dos which are the last few things you need to link up correctly in order to get it to work. What I like to do is just give this a a sort of skim through and try to understand each of the steps it's doing and maybe ask a few questions to help understand it better and then I can move forward with actually going into testing. So we start here with a telegram trigger. That's going to be what we set up where it sends a message. We're doing some workflow configurations here like setting the Google sheet ID and the drive folder. That's okay. It's actually a good practice to have these set first because then you can just change one node and everything that references those downstream will be updated in one go. Then we have a check of message type. So, it's going to look to see if the Telegram message was a photo or a message, which makes sense because we've kind of got two different modes for this, which is chatting to the data, or we're going to be sending a receipt photo for extraction and logging in the spreadsheet. So, here we have it going to the spreadsheet and trying to pull all the data from the spreadsheet, which is what we want. Then, we have our AI agent, which is prompted to uh answer questions, which makes sense. It's already got the Google sheet data here, and then it's going to send the response back to Telegram. So, that'll make sense. Then, we have the extract receipt data. Now, this is a bit more complex, but it's telling the agent that it needs to analyze a receipt. extract the following fields, return a confidence score, and some more in details about the formatting of those responses as well. So, that all seems to be pretty good. We're using the Gemini 2.5 Flash model, which is what I actually put specifically in this section here around the model preference because we needed a multimodal model out of the box, so that we could just use Google Gemini 2.5 Flash, which is really cheap and affordable. Um, that requires a little bit of researching or just chatting with the uh the assistant here to figure out which model would be best for this. But, you can see here it's actually not using the correct model. So, I want to come down and select the right one. Now, for you guys, you won't actually have your Google account set up correctly, which is something we're going to do in a second. You'll see in the NA10 AI down here, it'll be saying, "Hey, you need to set up your Google, you need to set up your Telegram, you need to set up your sheets," which are all things we're going to do shortly. Then, in order to be able to take the information in that receipt image, and then pass it further down, we need a receipt data passer, which is just going to make sure that it's sending the data based off that receipt. It's turning that image into what's called structured data or JSON here. And this means that the language model is going to fit all of that information into something that we can easily interpret and then use downstream as variables. So this is a very helpful skill to know in automation. And that's using an LLM to turn some sort of unstructured data into structured data and then passing that downstream into what's called a passer. And the passer is going to take be able to read this JSON right here and then give us variables that we get to play with downstream. So it's very important and we'll be using this quite a lot. And then we have a little format message. So it's just taking the variables. So you can see here already we've taken these vendors and expense date and currency and all those things that we just extracted with Google Gemini 2.5 flash and then passed it through that passer and out came these variables that we can now use. And then in this case we're just turning this into a little message that can be sent back via Telegram and it's asking them to please confirm or edit reply with confirm to accept or vendor with the changes if we want to edit. So that's a nice little feature that I didn't actually have in the original version of this build. Then it's going to send that message off. Nothing too fancy there. Now, this is where things get a little bit more hairy if I'm honest. I saw this and I was like, hm, this is waiting for a user confirmation. It's waiting for a web hook call and I didn't see this web hook call actually get used anywhere else. And so, in my little skim over before, I went to the NA10 AI and I said, basically, explain this to me. I don't understand. Can you explain how the wait for user confirmation works? Goes, great question. It works by this, this, this. And then it spotted an issue in its own workflow. So, the tricky part is that when a user replies to Telegram, that reply comes as a new Telegram message. it doesn't automatically go to the weight nodes web hook. You need to bridge this gap. Then it gave me two options and the first one seems great. So I'm going to say let's implement option A. And the thing is as you're going through this yourself, I obviously have a bit more information on how this all works. You get to go through here at every step and explain, hey, what is an output passer and what does that mean? You need to really really get used to relying on this NA10 AI because it is going to be your best friend in learning this platform as quickly as possible. So here we can go. It's planning out the steps to implement option A. Okay. And so now we actually get to an important decision that you need to make as the automation builder here, the one steering the ship. And that is here. It is actually asking us to use another external platform called Reddus, which I've just looked up and appears to be some kind of lightweight database service uh for managing real-time data. And if I look at the pricing, they do have a free tier, but if I'm being completely honest, guys, this is supposed to be the introduction tutorial for this video. And doing all of these external integrations is actually a little bit too much for just easing you into it. So, I'm going to show you how to actually go back and rethink this and go back to the GPT and ask it to plan it in slightly different way cuz this is a really good example of what these different kind of when you're heavily using AI for planning and building. There's so many like I've I've actually done this multiple times and every single time it's given me kind of a different way of doing it like I said before. And so, it's an important skill for you to have is to be able to go back to this planning phase. So I'm going to ask the GPT and say hey is there any way we can simplify this like a single agent with a few tools and taking out the complex functionality like the confirmation message. So if we send this so here it's giving me two different options a simple sort of faster version and one with a bit more caution around logging these receipts. So I'm going to say let's go with option A give me the fields to put into the form referencing the form that I've got over here. Then I can just grab these one by one. So I've got those all filled in now. I can just run the tool again. And actually I see a few things in this plan that I'd like to remove as well. So, I'd say so my last touchup here is let's remove the Google Drive saving feature. Let's make it as aentic as possible with the sheets and Gmail tools all connected to the agent for simplicity. That's going to make the automation look a lot smaller and simpler. And let's also use Gemini 2.5 Flash for the model as it's multimodal. That means it can take both image and even video and text as inputs uh without an issue. So, with all these added in now, I can click run tool, wait a few seconds, and we'll have our new brief to pass into. So, I'm going to copy this And we're going to go back and create a new workflow. Open up the N18 AI and then paste that in again. Nice. And now, as I said before, it's actually easier to have a lot of these things handled within this. So when you change it here, it automatically updates all of these. For example, the Google Sheets. I only want to set the Google Sheet value once. And then it can be passed down through everything nice and easy. So it's good practice to have. So, I'm going to ask it, can you set up the workflow configuration steps so that I can add most of these in one go? So, and I'm sure you can see how much simpler we got this down. This is kind of what I was trying to show you that there are very advanced and complicated ways of doing things. And then if you can prompt NAN or prompt the sort of spec rider that we have to opt for simpler and often more agentic ones, it's going to look a lot less complex cuz as you can see here, we're getting the agent to do a lot of the heavy lifting and it has to decide when to use the sheets, when to use the Gmail tool, when to send an email, when to send a message back. And so this AI agent node here is able to intelligently use the right tools when needed rather than having these big long chains of uh like JSON passing and if this happens then do that which yes it is more deterministic and likely more reliable for most use cases like this. You can rely more on using intelligence and tokens rather than trying to strip it all down to the most deterministic setup possible. Here we can see we've narrowed this down to just setting up this node. Now I'm going to hide the AI assistant for now. And now we have things like the CFO email. When I want to escalate it, where do I want that email to go? I can just plug it in here. What's the expense threshold for sending an email to my CFO? That's $500. Default currency USD. Google sheet ID, which we'll need to set up in a second. Sheet name, which is here. Sheet headers, so on. Now, to get this working, all we need to do is configure each of these nodes so that we can give it a test. So, I'm going to start with Telegram here. You'll need to come to create a new credential here. And you're going to go to Telegram. So, you want to download and open up Telegram. It's free to use. You can get it on your phone or your laptop. Then, to create a new bot, you're going to want to search up here for botfather. Click on the chat here. And then you can come in and write new bot. That's going to take us through a setup process to get this new bot set up. So what's the name? We can call it BIS receipts bot. Let's also call it BIS receipts bot. And there we go. Just like that, we have our token. I'm going to copy that. Head back to NA here. Paste in the access token. And then click save. So that's super easy compared to some of these. If you do need help at any point when you're getting your integration set up, N8 AI here is actually really helpful. You can click here and it's going to use AI to look up the documentation, get you the latest information on how to do this. And if you get stuck, you can chat here. So again, learning to use AI at every step and rely on it to help you get through Roblox is really essential. As you can see, it's got the exact steps we just went through. So we'll click save there. It's going to test it for us, make sure that it's working correctly, and we have this green box, so we're ready to go. So that's our Telegram bot set up just like that. It's triggered on message and it's going to automatically download the files and images which is great. Makes it a lot easier for us. And then we want to work through the rest of these configuration steps like firstly the Google sheet ID. So we can make this a lot easier for us by clicking into here grabbing these sheet headers and then going to Google Sheets. Now one thing to note about using Google Sheets and anything kind of Google related that in order to integrate it with NAT as quickly as and easily as possible, you need to go to what's called a Google Workspace. This is basically a business account on Google that allows you to access docs and sheets and stuff through your through a business account rather than a personal one. And it's really important that you do this and set it up. It does have a 14-day free trial. Um you can try it free for 14 days. You can come on get on the starter one here and start a trial. I highly recommend you do that or it's going to take you a lot more effort to get these integrated in a second. But in this case, I'm on my Morning Side AI account. So I've got that all set up. I'm going to go to a blank spreadsheet here and paste in these fields that we had before. Actually, I'll put them down here. And then I can just start to grab these fields out, start popping them in. Then I have all of these columns set up correctly in the sheet. I'm going to just bold them uh to make it easier to look at. I'm going to call this company expenses tracker and I'm going to name this sheet one expenses. Then I'm going to grab the URL of this and double click this segment of it. As you can see after the /d, we want to copy that ID. Head back to N8. And in case you got lost there, I was grabbing the sheet headers out of this section here. And so then all I need to do is paste in our Google sheet ID that we just took from the web page there. We've got the sheet name set up as expenses which we had correctly here. And then we have our workflow configured correctly like that. Now we've got a few more configurations to set up. We have firstly the Google sheets. You can go to create new credential here. And here you can see as it says sign in with Google that's connected to that Google Workspace. So if you're trying to connect with a personal account, it's going to walk you through a much more complex setup. So please please get that Google Workspace set up. It's going to take you a few minutes. Do that. It's got a 14-day free trial. Come back. sign in here with that same Google account you created on your workspace. And then you will have correctly set up your Google Sheets account here. And that means that N8 is going to be able to access that ID of a sheet that we gave it before. And here you can see it's inserting that ID that we set up just before into this document and also the sheet name. So you can start to see how when we set those variables earlier, it can be passed downstream a lot easier. And a key bit here to note that the NA10 AI is actually uh correctly set up the descriptions of the tools. As I explained earlier in the video, tools have kind of descriptions and they have information on the different variables that need to go in them. And then this tool description which we are setting manually here, we are giving it all the information about what this tool does, what it takes in, how it's connected to the rest of the workflow. So that our AI agent here with its system message also understands what each and every one of these tools does. The Google sheet adding to the spreadsheet, the reading the whole spreadsheet for when we're asking questions over the data, and the Gmail tool for sending it, and the Telegram reply and so on. So that's one of the reasons why the N810 AI can be so powerful when you prompt it correctly because it will do all that context setting for you uh rapidly. So the same thing needs to be done for this Google here. So you can come to create a new credential, go through the same process, sign in with that new workspace email and then you're going to have your Gmail connected and it's going to be sending from that email that you set up. And then finally down here we have the Telegram set up and we can connect that to the Telegram account that you just created. And now the final piece of the puzzle is to connect up our Google account correctly so that we can actually use the model that we want to want to change this model here because it struggles to use the exact one that you ask for. So we're going to use Gemini 2.5 Flash. Then to actually be able to use these APIs, you need to create a new credential here. And then you need to go to Google Cloud. You'll be able to log in here with your Google Workspace account. Then you go to the console. Then you want to come up here and click on a new project if you haven't got a project already. Then once you've set up the project, you want to head to billing account management. You'll likely need to create a new billing account. You want to go to payment method here. Then you will need to add some sort of debit or credit card in here cuz that's how you're going to be charged for it. You can set all the limits you want, but for what we're going to be doing, it's not going to use much at all. You might be charged a dollar or two max. So get your card added in here. This is going to mean that you're able to create an API key. And then we can head back to our cloud console. You can see that we're in the project that you just created. And then we're going to want to head down to APIs and services. enable APIs and services up the top here. We're going to search for the Gemini API. Click on this. Yours will say enable here. So, you want to click enable. Then go into manage. If you head to credentials on the left here, then create credential. Click API key. Now, we have our API key. You can copy this. Head back over to NAN, paste it into this API key section, and then click save. And it'll do a little test on it. Make sure it's working. You may need to go back and forth to make sure your billing is set up and it's able to charge you correctly. Once you've got that, you can click save and you will have connected your Google Workspace and cloud account to Naden to be able to use that in all future workflows. And one thing to go over here is this conversation memory. This is just so we can have a bit more of a conversational assistant and it's going to keep the last 10 messages in the chat in the context so the agent understands what's happened before. So that's just helpful for a conversational agent. Sometimes if it's non- conversational then you can just leave that out. Now, with all this set up, you can just pop into the system message here. Make sure that it's all prompted correctly because this is kind of the glue that holds all of this functionality together. You are an intelligent expense tracking assistant. Help users log expenses from receipt photos, your capabilities, extract expense data, log expenses, answer questions, send CFO alerts, reply to users, and breaks down the workflow and full details there. Now, all that's left to do is the moment of truth. We're going to save this. We are going to click execute workflow. Now, this means it is waiting for the trigger. So we can send a message on telegram and it's going to ping this and it will receive it. This is just a way of testing it. It's not actually live. It's not going to reply to every message yet. So we can go into our chat here and say hey man just to test it and yep it has received our message but we're getting an error here. So this starts the process of using the AI agent and assistant and then to fix things. So this is very important to see this live as we go. We can click open node here. No session ID found. Expected to find the session ID as an input called session ID. So we can just click on the NA10 AI here. It's going to analyze the error, give us a recommended fix, and then actually do it for us. So again, I'm trying to show you the AI based workflow for this and that. Let's try to plan it out. Let's try to get the NAI to build it. Let's get it to explain it to us if we don't understand certain parts. So to get this built out, I'm going to copy all of this. I'm going to go to the build section. Then I'm going to say paste it in here. Fix this session ID error. So the session is just an ID that's linked to our account. So whenever we are messaging with the bot, it's like, okay, well, what conversation is this? Because this can handle thousands and thousands of users through this one automation. So we need to be able to track which user we're talking to so that we add the messages to the right chain of messages that we're building for each user. So here we can see it's fixed the conversation memory session ID. Um changing it from input to custom key mode. Memory will now probably use a telegram ID from message ID. So we can execute and refine. Go back to Telegram here and say, "Hey man, there we go. It's working correctly. I couldn't understand your request. please rescend a receipt photo or text like so and so or ask a question like how much should I spend last month? So, it's analyzed that and realized that it's not something that it's supposed to handle and it's asked us for the correct input. And as you can see to break down how this is displaying what's happened to us, uh we have a green tick here, a green tick here, a green tick here, and then it's gone and used the Gemini flash twice. It's checked conversational memory multiple times, which is a bit strange. And then it's used this tool once to send that response via Telegram. Now, all that's left to do is to test the rest of the functionality. So, I'm going to close this off. We can click execute workflow again. And this time I'm going to send it a receipt. So I'm going to drag this in here. Send it. And we see it should analyze it with this. Extract the information. And then so we have an issue with the Google Sheets account. So again, we just go through the process asking the N10 AI to help us with this. And as expected, it's just going to tell us to double check our credentials. So I'm going to go through this process again. Create a new credential. I'm going to rename this to latest. Save. Make sure both of these are on the I'm going to save. Make sure you're saving the workflow constantly because it needs to save in order to load those changes to the actual deployment. We should see over here. Boom. There we go. So, we have added in the information to the spreadsheet successfully. I just drag these out so we can see the receipt file ID, the vendor, expense date, currency, total tax, everything that we asked for here, even the items that were on that receipt. It's got a high confidence as well, which is great. So, we have successfully extracted information from that first receipt and put it into a spreadsheet. So, that's a big win. Interestingly, it's chosen to read the sheet as well. I don't know why it's reading the sheet before it puts it in. If I was going to keep tweaking this around, I'd make sure that it wasn't doing that, but for now, it seems to be working fine. Maybe it's just getting a read of the column so that it can better fill it out. At this point, I'm not too bothered. It seems to be working. Now, another thing we want to check is the ability for it to escalate it to my CFO if it's over $500. So, I've got this set up on a Google Workspace email as I recommended here. Then, I'm going to execute the workflow again. Oh, and as you can see, we're getting a response back, which is great. Giving the user a bit of feedback on what's happening. And here I have an Apple Store receipt for a uh healthy €3,500. I'm going to send this here. And there you go. It looks like it didn't even need to read the sheet this time because it had already read it in the last session. It remembers the last message we've sent and the ones before it. So maybe it already knew what the sheet looked like. And it seems we're getting more issues with authentication here. So I'm just going to set this up again. Save that. Save the workflow. Exit it again and send our receipt. Yep. And we've seen an email sent off and logged in the spreadsheet as well. Boom. There we go. over 500. True. And this row here was from the failed one. So, we can probably delete that. But here we go. Our CFO email sent at it's officially sent an email and logged this in here. And it's detected that it's over $500. So, now if I go to email, then I've got an email from myself here saying high value expense alert at Apple Store Graphen Street. High value expense has been logged. So, this is going to be a helpful way to send off to your finance department or CFO whenever there's a big expenditure so that they can follow up on that and make sure that that that's an approved purchase. So, now that we've got all this working, the last thing to do is ensure that we're able to chat to this and it's able to pull that information from the spreadsheet and we can ask questions over that data. So, what I'm going to do is just grab this here. I'm going to grab all the information in the spreadsheet. Head back to our handy dandy assistant and ask it, can you create a bunch of dummy data in a table need to copy to sheets? I think I could turn off thinking here. So, I can copy this data route. I can head back to the spreadsheet and I just click in here, paste it in. Now, I've got a bit more to work with, a couple more categories and so on. And then we can head back to Telegram. I'll execute this workflow again. I'll say what is the total spending on this account. Now, it should query the data. It's pulled that all back to the agent and boom. Total expending on your account is 5,000 USD or and 3,000 total. So everything is working as expected. That's great. Now we can click on this toggle here to make it live. That means that instead of having to execute it every time, it's going to run with every message. And then we have our buddy here to chat away with and we can say how many trans. So as you can see, we now have a handy dandy expense assistant put onto Telegram that's able to add things to a spreadsheet from a image. very powerful skill to have. We've heavily used the N8 AI and the AI tools that I've created for you guys to plan this out, to iterate, to go back and forth, to use the N8 AI to also debug and work through issues. You have just seen a realistic walkthrough of what the stuff looks like when someone like myself or other AI automation engineers are building these systems. So, so that is build one under the belt. I will be giving you guys this template exactly so that you can import it yourself if you don't want to do everything yourself. I'll also be providing those final prompts that we gave to the AI so that you guys can try to follow along as closely as possible with the experience that I've gone through here. This has been a great skills foundation for what we're going to be doing in the next three builds, which are kind of linked together as a chain of different automations and systems that you can build for a given business that work together as part of a larger system. All right, guys. So, before we build our second agent workflow, I want to talk you through hosting since it's particularly important that you understand where your workflow is actually going to be running once you've got them set up and deployed for your client. So, in order for things to run 24/7, your workflows have to always be on, which means they need to be hosted in some kind of server somewhere. So, when we signed up to NA and when we started building, we were automatically putting NA10 on the N8 cloud. So, with N8 cloud, you're basically borrowing some space on their server. It's like renting an apartment, but it's for running your workflow. They host these workflows for you automatically with no setup required for the hosting, but you pay based on usage. So, this works fine when you're learning and just starting off and building your first few workflows just like this. But as you scale up and particularly start to deal with clients doing a lot of volume, you're going to hit walls where your workflows might crash with a large amount of data, your bills can spike unexpectedly, you can't install certain tools that you need and ultimately you're stuck within NA's limits and their pricing structure as well. So for running an actual AI agency or AI automation agency powered by NA10 and selling those workflows to your clients, pretty much every AI agency I know is self-hosting their NAT and using that to run their client workflows. Or in other words, they own their own home for their workflows versus renting an apartment in NA10's cloud. So when you sell host, you can run as many workflows as you want basically. And instead of paying surprise bills to N10, you pay a predictable and usually much smaller monthly fee just for the server access. You also get the freedom to install any community tools that you need. And most importantly, you control everything about your infrastructure. But if you had to set all of the self-hosting stuff up from scratch, that would be a lot of complicated work for beginners particularly. But fortunately, there are a lot of services out there that handle all of the technical complexity for you of this self-hosting in order to get the most scale out of your automation. So, when it comes to self-hosting, especially for beginners, Hostinger is my recommendation since it does basically all of the heavy lifting for you automatically with literally like a one-click NAT installation. And fortunately for me, Hosting has offered to sponsor this video. So, shout out to Hostinger for being the sponsor of this video. So, let me show you how to get yourself hosted in 10 on Hostinger up and running if you plan on scaling your agents professionally as an AI automation agency. So below this video, there's going to be a link that will send you to hosting as self-hosted N8 page. They've got a page specifically for it. You can select whatever tier you want, but KVM2 is a good price for the value you get. So we can choose this plan and then we can select either a 1 month or 12 month or 24 month for better savings. Your server location of where you're hosting your systems uh will automatically be chosen based on your location. And the closer the server is physically to you, the better performance you're going to get. Now down here, you'll see NM was already selected for me, so I can leave that as is. And before heading to checkout, you can get an extra 10% off with code Liam Mley, all caps, guys. So, you can put that in there, get an extra 10% off your hosting setup. Then, you just register your account. You can fill in your billing details and enter your payment info. Now, we just need to add a root password. Make sure you save this somewhere safe. And next, we can get malware scanning for free, which is great. And I'm going to select that before finishing things up. Now, Hostinger is doing all of the complex setup for us automatically, which is awesome. And once it's finished up, we'll be routed to the dashboard where we'll go from there. Now, from here, I just click on manage app. I'll be taken to my own self-hosted NA10 instance. So, here's where you enter your info and get your instance up and running. Once inside, we have the option to receive some free features for advanced debugging, search, and organization. And now that I'm in, I can go ahead and create my N10 workflows from here. So, either using a provided template or creating from scratch and building node by node. Now, I have unlimited automation power running 247 with predictable cost. So, that's a key thing for turning this into a business. Occasionally, NATM will release versions with new and useful features. So, you'll want to update your NATM from inside your Hostinger account, which there's a tutorial for right here. And if you want to customize your domain, you can follow the guide here for a custom URL that your workflows actually live at. And if you need to reset your password, you can find out how…

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