AI agents from first principles

0:00 Yet, you are still trying to figure out, should I use Lang Chain or Llama Index and trying to debug and figure out all of these AI agent systems that you're building and tinkering with. All of the tutorials that you'll find either are messy or contradicting. And every week there is a new fire ship video dropping something new where you're like, "Oh do we now also need to know this?" So all in all, it's a complete mess.

0:22 And my goal with this video is to help calm your AI anxiety and give you clarity on what's currently really going on within the AI space and why you can pretty much ignore 99% of everything that you see online and just focus on the core foundational building blocks that you can use to build reliable and effective agents. So in this video, I'm going to walk you through the seven foundational building blocks that you need to understand when you want to build AI agents regardless of what tool you are using.

0:46 Now I'm going to give these code examples in the Python programming language, but honestly it doesn't matter what tool you use whether that's NAN, TypeScript, Java or any other programming language. If you boil it down to these seven foundational building blocks, you can implement it in anything because they're so simple. So, I will execute these simple code blocks and show you the output and walk you through everything step by step with diagrams as well. So that even if you've never written a single line of Python, you can still follow this video and I can guarantee you that after watching this video, you'll have a completely different perspective on what it takes to build effective AI agents and you'll be able to look at almost any problem, break it down, and know the patterns and the building blocks you need in order to solve it and automate it.

1:31 And then you might be thinking, okay, like Dave, but why should I listen to you? Aren't you just part of the 99% of noise you just mentioned? Well, I'll let that up to you. But I have somewhat of a unique perspective on the AI market right now. And that's because I have over 10 years of experience in AI. So, I have background in machine learning and I run my own AI development company where over the past years we've done over 20 full system deployments.

1:54 Next to that, I have plenty of communities with thousands of members combined. So, that gives me a really good pulse on everything that's going on, what's working and what's not. And next to that, I also regularly interview industry leaders like Jason Leu or Dan Martell, for example. So, I briefly wanted to bring that up just so you know that I'm not just some random guy building NAT workflows from his bedroom. And there's nothing wrong with that.

2:16 It's just a different category. Clients work with us and hire us to build production ready systems that they can rely on. And that's that knowledge from that I want to bring to you in this video. And the big big problem that I see right now within the AI space and why you feel so confused as a developer all comes from this simple image over here. There is simply a lot of money flowing into the market. And every time throughout history when there's an opportunity like that, what happens?

2:39 People jump on it because people want to try and capitalize on it. So even if you're remotely interested in AI, this is what most of your social media feeds will look like. And they all make it seem super easy. There are all these tools that you can use to build full agent armies. Yet you are still wondering like where do I start and how do I make this all work in really a production ready environment. And on top of that you have of course all of the frameworks and libraries that follow a similar trend right so developer tools GitHub repositories all kinds of tools that make it seem super easy to build these AI agents.

3:26 And then of course we have the news, everything that's going on and the plenty of other tools that are built on top of that that you can also use. And this all results in you feeling really overwhelmed and having no idea what's going on and what to focus on. And now there's a clear distinction between the top developers and teams that are actually shipping AI systems that make it to production versus the developers that are still trying to debug the latest agent frameworks.

3:49 and that is that most developers follow all the hype you see on social media, the frameworks, the media attention and the plethora of AI tools that are out there while the smart developers realize that everything that you see over here is simply an abstraction over the current industry leaders the LLM model providers and once you realize that as a developer building AI systems and you start to work directly with these model providers APIs you realize that you can actually ignore 99% of the stuff that you see online and also O realized that fundamentally nothing has pretty much changed since function calling was introduced.

4:13 Yes, models get better, but the way we work with these LLMs, it's still the same. And every time I bring this up, people are like, "What? How is this possible?" But our code bases from 2 years ago, they still run. They still work. We only have to change the model endpoints through the APIs because we've engineered them in such a way to not be reliant on frameworks that are essentially built on Quicksand. So all this context that I'm providing right now is super important just like with LLMs because otherwise the rest of this video and the seven core building blocks won't make a lot of sense.

4:59 So the first most important thing to understand is that if you look at the top teams building AI systems, they use custom building blocks, not frameworks. And that is because the most effective AI agents aren't actually that agentic at all. They're mostly deterministic software with strategic LLM calls placed exactly where they add value. So, the problem with most agent frameworks and tutorials out there is that most of them push for giving your LLM just a bunch of tools and let it figure out how to solve the problem.

5:21 But in reality, you don't want your LLM making every decision. You want it handling the one thing that's really good at reasoning with context while your code or application handles everything else. And the solution is actually quite straightforward. It's just software engineering. So instead of making an LLM API call with 15 tools, you want to tactfully break down what you're actually building into fundamental components, solve each problem with proper software engineering best practices and only include an LLM step when it's impossible to solve it with deterministic code.

5:44 Making an LLM API call right now is the most expensive and dangerous operation in software engineering and it's super powerful but you want to avoid it at all cost and only use them when it's absolutely necessary and this is especially true for background automation systems. This is a super important concept to understand. There is a huge difference between building personal assistants like chat GPT or cursor where users are in the loop versus building fully automated systems that process information or handle workflows without humor in defension.

6:32 And let's face it, most of you aren't building the next chat or cursor. You're building backend automations to make your work or your company more efficient. So when you are building personal assistant like applications, using tools and multiple LLM calls can be more effective. But when you're building background automation system, you really want to reduce them. And for example, for our production environments for our clients, we almost never rely on tool calls. So you want to build your applications in such a way where you need as little LLM API calls as possible.

6:55 Only when you can't solve the problem anymore with deterministic code, that's when you make a call. And when you get to that point, it's all about context engineering. Because in order to get a good answer back from an LLM, you need the right context at the right time sent to the right model. So you need to pre-process all the available information, prompts, and user inputs so the LLM can easily and reliably solve the problem. This is the most fundamental skill in working with LLMs.

7:19 And then the final thing that you need to understand is that most AI agents are simply workflows or DAGs if you want to be precise or just graphs if you include loops. And most steps in these workflows should be regular code, not LLM calls. So what I'm trying to do in this video is really help you understand AI agents from a foundational level from first principles. And now that we've set the stage, we get into the foundational building blocks that you need.

7:42 And there are really only seven or so that you use in order to take a problem, break it down into smaller problems, and then try and solve