AI agents explained in 15 minutes

Imagine you are in a meeting presenting project updates. While you are talking, an AI agent is quietly working in the background. It's cleaning yesterday's data, updating a dashboard, and even drafting your follow-up summary so it can send an e-mail. That's the simplest definition of an AI agent. It's like passive income, but for your productivity. You are getting work done while you're not even working. And this video is your masterclass on AI agents. By the end, you will understand what they are, how they work, and how to explain them to anyone, your co-workers, your manager, and your friends, and how to build whether you are tech or non-tech. Because with memory tools and frameworks like LangGraph agents act like digital co-workers and tools like OpenAI systems and Diffie make them easy to build. Here is what we'll cover in this video. We're going to start with simple and get deep real fast. We'll talk about the fundamentals, what makes agents different from chatbots, how they actually work, how to build one yourself, real-world use cases, and what's next, including AI browsers, multi-agents, and more. So let's start with the fundamentals. AI agents are built on top of large language models like GPT-4, CLAW-3, or Gemini 1.5, but they have three defining abilities. Autonomy, memory, and tools. Don't get it? Let me explain. Autonomy means agents can act independently. Think of when a baby first learns to walk. They wobble, fall, but eventually they take those first steps on their own. That's where AI is right now. A year ago, models needed step-by-step instructions. Now you can give them a goal and they figure out how to get there. That's what autonomy means. AI learning to walk on its own. Memory gives an agent continuity. Traditional chat models forget everything once the conversation ends. Agents, on the other hand, can store information long-term using vector databases or embeddings. They remember your projects, your preferences, and even your mistakes. That's what turns a chatbot into a teammate that learns over time and become an AI agent. And lastly, tools. Tools are how agents act in the real world. They can call APIs, run code, send messages, browse the web, or update database. Let's say if ChatGPT is a brain in a jar, agents are the same brain with hands that can actually do the work. Together, autonomy, memory, and tools turn AI from our reactive assistant into active collaborator. Let's actually go a little bit deeper and learn how AI agents work behind the scenes. Let's say you're asking an AI agent to pull last quarter's sale data, find trends, and draft a summary for the leadership deck. In seconds, it gathers the data, analyzes patterns, and creates charts. That's what AI agents are supposed to do. Pretty Pretty amazing, right? Now here's what's actually happening behind the scenes in five stage workflow. When you ask your AI agent to pull last quarter's data, this is what it does. First thing it does, it builds perception. The agent starts by gathering input from APIs, databases, documents, or even sensors. Next, it goes into reasoning engine. It uses a large language model like GPT-4 or CLAW to analyze contacts and decide what to do next. Here is the interesting part. most modern AI agents actually follow what's called the react framework, short for reason and react. That means they don't just think about what to do, they actually do it, observe the results, and then refine their next steps. That constant loop of reasoning and action is what gives agents this almost human-like sense of adaptability. After perception and reasoning comes planning. This part breaks big goals into smaller manageable tasks like fetching data, analyzing patterns, and writing summaries. For example, when I ask my AI agent to write this e-mail, for me, it will actually start thinking about each step individually. For example, the first thing it will say, I will open e-mail. So it takes the entire goal and breaks it into smaller tasks and works on those tasks that is in the planning phase. And that's what the planning module is. After planning comes a memory model. This stores past interactions, decisions and outcomes so the agent can learn and improve over time. Then we have action. This is where things actually start to happen. It executes the task, calling API, updating dashboard, sending report, or even triggering automation and sending that e-mail that I ask it to do. Together these components make the agents autonomous meaning it can operate continuously without waiting for you to tell what to do next. I actually did this video a few months ago where I used ChatGPT operator. It often got stuck and asked me to take control. Compared to when I use it now, it's becoming a much more smooth and it's asking me less and less to take control because it's figuring it out and it's getting better. So in short, traditional AI responds once. Agents on the other hand think, act, observe, and keep improving with time. Now let's get to the fun part. Let's discuss how to actually build an AI agent. There are two main ways to build an AI agent and we're going to discuss both of them. First, you can use existing tools like ChatGPT to create a custom agent or agentic workflow. I have built a few agents in ChatGPT but one of my favorite is called SK Admin. It basically has access to my calendar, my inbox, my drive and it looks at my inbox, calendar and drive every day and it tries to figure out what I need to focus on today. So every 7am I get an e-mail and notification from the ChatGPT app where it has everything summarized on what I need to focus on today, if I have any meetings, if I need to respond to any emails. It literally saves me so much time. And this is great if you're a non-tech audience. But if you're very technical and you like to code, the second option is for you. The second option is that you can build an AI agent by hand if you're a coder using Python and open source frameworks. Before we actually start coding, let's reframe how to think about an AI agent. Building an agent is like assembling a tech team. Every great team has few key roles. There is a software engineer, a product manager, a data scientist, and operations engineer. And then we have the manager that manages the entire team and keeps everybody aligned. That's exactly how an AI agent is structured. Every module plays one of these roles. So using this analogy, let me explain the steps that we would be taking to build an agent. The foundation models like GPT-4, Claude 3, or Gemini are your software engineers. It's the one doing the actual work, writing logic, and solving problems and generating output. Okay, other roles in a tech team also play a role, but just stay with me for this analogy. Next, we have the planning module, which is your product manager, it doesn't do the hands on work, it figures out what needs to be done. Okay, product managers do actually do work, but please stay with me for this analogy. The planning phase breaks big goals into smaller actionable steps and creates the roadmap for execution. The memory module is your data scientist. It tracks everything that the team has done, past action results and drives insights. When the agent faces a new task, it uses the memory to make smarter decisions, just like a data scientist learning from past experiments. Then we have the action models, which are your operations engineer. They take the plan and actually make things happen, calling APIs, updating dashboards, sending reports or running automations. And finally, the orchestration layer is your manager. It coordinates the entire team, making sure everyone communicates and works together smoothly. That's what frameworks like LangGraph and Autogen do. They manage collaboration between multiple agents, handle task delegation, and keep everything running. Now, how do you actually put this into practice as a coder to build your own team? I mean your own AI agent. You will use tools like OpenAI's agent SDK or crew AI for lighter setup, perfect for role-based or custom workflow. If you're working in an enterprise environment, Google's ADK integrates nicely with Gemini and Vertex AI. And if you just want to experiment or prototype fast, Diffie and NNN are great for low or no code builds. So it's just like a real tech team, every agent needs five things. A brain, a planner, a memory, a doer, and a manager. Once they are connected, you have got a system that plans, executes, learns on its own. Okay, enough talking, let's actually build an agent using both of the options that I shared. Option one will use tools like ChatGPT or similar platform to create an agentic workflow. This is the easiest way to get started, especially if you don't want to code, you can build a custom GPT or connect external tools like inside GPT. For example, I built one for myself that checks my emails and calendar every morning, summarizes my meetings and prioritizes and sends me one daily briefing. Perfect. This is exactly what I need. This is an agentic workflow, a combination of autonomy, memory, and tools working together seamlessly. Option number two, where you build your own agent by hand in Python. If you want full control, here's a simple structure that you can follow. This is a simple coding snippet in Python where I'm calling open AI API and building a research assistant. First, I give it a role, then I tell what the goal of this agent is. I want to find top AI trends for 2026 and I want it to summarize. And then I'm defining tool that I wanted to use web search and e-mail sender. And this is a simple function that executes all the steps together. Now you can go as fancy as you want and make it as customizable based on your customization preference. You can make it very custom to your own needs. This simple snippet will basically help you get started. And if you want to learn more definitely check out this AI engineering course that covers all the fundamentals but also teaches you with hands-on projects. It has four guided projects and by the end of it you basically have your own hands-on LLM app that you have built using tools like Hugging Face, LangChain, and more. Regardless if you're going with option one or option two for building your AI agent just remember that principles stay the same. Plan, act, reflect, and improve just like any high-performing tech team. Okay enough with analogies. Now let's actually talk about real-world use cases of AI agents. AI agents are already reshaping how work gets done. I just gave you an example of how I am personally using an AI agent to help me stay on top of my inbox and help me figure out my priorities and deadline. But let's keep building on it and actually talk about specific roles where AI agents can be helpful. In data science, agents can automate cleaning, reporting, and analysis. I do want to preface these use cases that a lot of the AI agents out there are not there yet. But the goal eventually is to basically satisfy some of these use cases that I'm listing here. So in data science, these use cases could be automating data cleaning, building reports, and writing analysis. In software engineering, they could review pull requests, debug, and document code. In marketing, multi-agent systems could plan campaigns, write copy, and analyze performance data. In operations, agents update CRMs, resolve tickets, and manage repetitive admin workflow. Some agents can even critique their own work and make improvement before you see the final output. That's the difference between automating and adaptability. Now I do want to say that the agents are not there yet. They are not fully autonomous, so you basically have to babysit. But compared to my experience six months ago versus now, they are already much more autonomous. All right, that being said, there are some limitations. AI agents are powerful, but far from perfect. One major limitation is something called context drift. It happens when an agent slowly starts to lose track of original goal during a long-term workflow. Let me give you an example. You ask your AI agent to write an e-mail to your manager explaining the project will be delayed and include key reasons for the delay. Cool, simple task. It starts fine, clear and polite, but as it edits, it rewrites and reframes the message. By the end, your we are delayed e-mail becomes we are making great progress. That's the context drift for getting the main goal over long workflows. Another issue is overconfidence. Agents can act as if their outputs are perfect even when they're not verified. For example, your agent might send the same e-mail automatically or update a project tracker without checking whether the information is accurate. It's confident but confidently wrong actually. And then there is privacy and safety. If your agent has access to emails, which mine does calendar or company data, you have to set boundaries. A small mistake in prompt design or permissions can lead to oversharing sensitive information or editing the wrong file. So while AI agents can automate humans still define the limits. They are great at execution, but you provide the context and ethics and judgment. Think of them as powerful teammates who are still interning. They're not autonomous decision makers yet. Okay, you learn a lot in this video. But let me just leave you with what is the future. The future of AI agents is teamwork, we're moving toward a multi agent system where several agents collaborates like a team, one researches one codes and the other one test. And if you have been keeping up with all the latest launches, there are AI browsers such as open AI Atlas and perplexities comet, I'm actually going to cover the AI browsers in one of my next videos. And honestly, my brain is blown away because I was skeptical but it really surprised me. Then we have multi-agent system. Let me know if you want to do a video to talk more about multi-agent systems. There was a study done by Gartner which predicts that 70% of enterprise will deploy AI agents within two years. This isn't about efficiency anymore it's about scaling creativity decision making. In my opinion the real challenge is the scalability because an individual like you and me can build like these small agents where we can like automate our emails or we can like have it read our e-mail and tell give us our to-do list. But when it comes to like large-scale organization where so many humans are involved, so many systems are involved, so much data is involved that is sensitive, that's where the real challenge be. So AI agents might be very autonomous in the next few years, but scaling part may take us a while. So in the last 15 minutes, you have learned what AI agents are, how they work, and how to build one yourself. Next time someone asks you what an AI agent is, you will have a solid explanation and plenty of analogies to make it click because I shared a few. If you enjoyed this kind of educational deep dives, let me know in comments. Tell me which part helped you the most and what topics you want me to cover next. And remember, we're not just using AI anymore, we're learning to manage it. With that, I hope you're having a beautiful day. I will see you in the next video. Have a good one. Bye. Transcribed by https://otter.ai