Learn Multi AI Agent Systems with crewAI: Lesson 1

First of all, congratulations on signing up for this course. This is going to be great. I'm so excited to dive into this with you. We are going to learn everything that is to know about agents and in this first lesson we are going to understand what make them tick, how they work, and even look at some of the examples that we're going to build together throughout the entire course. So let's dive in real quick. So in this course we're going to talk about so many different things, so so many interesting concepts. I want to just highlight a few so that you know what you're going to see. We're going to be talking about role playing. We're going to talking about agents that are able to focus, to use tools to cooperate with each other. We're going to really dive into how guardrails are crucial to make agents work and how memory can make them so much better. We're going to talk about all the different ways that agents can collaborate, not only working sequentially and hierarchically, but also asynchronously. There are so many different options, so make sure that you stick around because we're going to build a lot of different crews throughout the different lessons.

A crew is a team of AI agents working together, each with their own defined roles. Don't worry if you don't know what this means yet. That's what this course is about.

We're going to start with an overview where we're going to build a simple research and write crew, but we're going to go all the way to build more complex ones. Building a customer support crew, a customer outreach crew, an event planning agent system, and a financial analysis agent system. And then to wrap up, we're going to build our most complex crew yet. A crew that is going to be able to tailor made your resume to any job posting out there, increasing your odds of getting an interview. So make sure to stick around for that because honestly, things are only gonna get more interest from here out. So what will we build in this course? Let's look at this full stack engineering job posting. So you can see there's a bunch of interesting stuff in here. If you look close at the job description, you're gonna notice that they're basically looking for a full stack developer. They want someone that nails both front and back end and there is a bunch of like actual criteria in there. They want you to be able to write APIs and to have experience with databases and all that. So this is what Noah's profile looks like. I'm not going to read over it. But let's highlight some of the stuff you can see that Noah is trying to highlight how he's a good leader. So he mentioned how he has excelled in leading teams both remote and in office. He mentioned some data science and machine learning. And he also mentions about the players scalable AI solutions. That is nothing to do with the actual position that Noah wants to apply. But if you dig in there, you're going to notice that Noah also has some of those skills. You can see that mentions there that it knows Ruby, Python, JavaScript, that it has been a software engineer for 18 years. So there is definitely enough things in there that kind of could explore and that make Noah qualify for this position. So how do we make sure that we help Noah to highlight all that in his resume so that he increases his odds of getting an actual interview for the position that he does wants to apply? Well, you can use agents to do it. In our case, by the end of this course you're going to be able to put together a multi agent system that does that and that we do that by leveraging four different agents. It's going to have a tech job, researcher, a personal profile for engineer, a resume strategist for engineers and an engineering interviewer preparer. Together those four agents will have a few tools that they're going to be able to use from searching the Internet all the way to do rag over your resume. Everything to kind of like help in this case or friend Noah, but yourself as well to make sure that you're highlighting the right skills for the job. So this is what the new profile looks like. Let's compare both for a second. So in the second one you can see that it was highlighting a lot of his leading experiences. But now in the second one it's kind of like doubling down on the other skills that he has that better match the job that he's applying to. You can see that we mentioned JavaScript, Python, Ruby, we also mentioned UI UX. You mentioned how he knows HTML and CSS. It's everything in there. So it's still the same profile and the same skill set, but frame in a way that now better allows them or in this case allows Noah to get the interview that he desires. So stick around if you want to build this because by the end of it you're going to be able to build not only these, but way more complex groups of multi agents that are going to be able to do a bunch of automation for you. All right, so what is agentic automation? If you think about what automation used to be like before, it was something completely different. In the past you would say, hey, I want to go from point A to point B and you can write code to automate that. And then what happens is that as edge cases appears, you start to kind of make that a little more complex. You start to add a lot of conditions and ifs analysis there where like, well, if X do C, if Z do D. And you can see how these things can become quite complex, especially as you start writing more and more edge cases. So in the old days, whenever you're trying to do automations, what you end up with is this very complex code base with a lot of different conditions and edge cases and you just can never cover it all. The beauty of agentech Automations is that you don't need to drown the map, you can show the options. So it's fundamentally a new way to write software.

All right, so in regular applications you have strong inputs, so you know exactly the data that is get into your application. You understand if that's a string, if that's an integer, if that's a float, you have a very clear understanding of that. And then you also have a very clear understanding of on what is the mathematical operation that you might be doing with that or any other heuristics. So you know, if you're multiplying those numbers together, if you're interpolating their string, whatever is happening in there, you have fully control and understanding of what it is. And because of that you also understand what is the output. You know, if the output's going to be another float or another integer or another string, you can replicate it. And, and that's the beauty of regular engineering. But with these new AI applications you have something else differently. You have a fuzzy input, meaning that you don't know what the user is input into your application. So you know that is a string because we're talking about an LLM. But you don't know if it's a string that represents tabular data, if it's a markdown, if it's a regular text or, or if it's a math operation. And then the transformations are also fuzzy because they're LLMs. You don't know if the LLM will decide to transform this into a list or if it's write a full blown paragraph. And because of that you don't know exactly what the output will be because it can take different forms and shapes depending on the inputs and on the transformations. But that's the beauty of it. There's a place in the world right now where, where these fuzzy applications make way more sense than regular existing software applications. So that's the main reason why people like ChatGPT so much. If you think about ChatGPT, that's an AI application with fuzzy inputs. You don't know what your user is putting in the chat. It's also fuzzy transformations, you don't know what the LLM will do with that data is and it's also fuzzy outputs. You don't know what will be the final output, what will be the final response for that user input. And that's why people love it, because it's something they can relate to. If you think about the word as we have today, the word is a fuzzy place. So this is the beauty about AI applications is that it's a valuable tool that has its merits and places where you want fuzzy applications and where you want strong typed applications and, and each one has its merits and its place depending on the software that you're trying to build.

So let's look at some actual example here. Let's talk about data collection and analysis. And if you have been working in engineering for a while, you're probably familiar with this. Your company probably has a website that captures leads data through a form and those leads eventually go into your sales team that are going to try to make them into customers. So this is very usual for most of the businesses out there. And the way that these leads become customers customers is by prioritizing them. And there's a bunch of ways that companies use to do that. So you have your form and you capture information about this company through that form and then go through an automated process of classifying these leads where you might look at some data points like hey, is this company a big company, it has more than 10 employees. Is this company located in the US or is this company located somewhere else? And depending on the answers for those questions, you might give a different score for these companies or treat them differently on your sales process. So this is regular data analysis and data collection. But it turns out that now that you can use multi agent systems, you can go beyond that and you can go even further. So let's try something different. Let's think how a crew of AI agents could actually help us here and do something that's better than what we have been doing through the past few years. Let's add a crew of AI agents on, on the lead generation process. So what you can do now, instead of having all those ifs, analysis, analyzing like specific data points for the companies, you could have this AI agent do research, go out there and do data collection. And this research can be searching, Google can be searching online, can be searched on an internal database or internal data set that you might have, whatever you want, any place where you can find more information about these companies. Then you can also have this agent draw comparisons between these companies and companies that you already have in your data set, companies that you're already talking to, companies that turned out to be great customers. And then you can also have this AI agents do some scoring so they can actually score this company and making sure that this company has a score that you can actually prioritize and decide to who you're going to be sending it to. And then the final point is coming up with talking points. So not only doing the research, the comparison and scoring, but, but also getting ahead and making sure that you know what are the topics that you should bring up when you go out and reach out to this customer and what that conversation should look like. And now you can take that and send that to your sales team and you have a way better data set to work with than what you used to have before. So this is a great example on how regular automation can actually become way better by using agentic automation. All right, so we talked a lot about agents so far. We talked about a lot of different types of automations, but in the end of the day, what are agents? So let's talk about that for a second and step back.