Generative AI Vs Agentic AI Vs AI Agents | Difference Between Agentic AI, Generative AI & AI Agents

Key Takeaways

[Music] Hi everyone. Today I'm here to clear up one of the biggest points of confusion in tech right now. That is what's the difference between generative AI, agentic AI, and AI agents. We hear these terms everywhere, but what do they actually mean? People often mix them because they sound similar but in reality each one works differently, has a different role and comes with unique strength and weaknesses. So today I want to walk you through these three terms in simplest language possible. We will look at what they are, how they work, their internal model structures and what makes them different from each other. To make this fun, imagine three different assistant. One is a creative writer who can instantly craft poems, code or even pictures. The another one is a robot worker that follows rules and does not task again and again perfectly. And the third one is a butler with a brain. It can plan, decide, use tools and even bring in other helpers to flex job. That's the big picture. Now let's break them down one by one. Now before we dive deeper, let me ask you this question. Which of the following best describes the role of generative AI? It generates new content based on patterns learned from data. It performs specific task like booking flights or sending emails. It makes decisions and coordinates multiple task with minimal human input. Take a moment to think about it. Don't worry, we'll clear up all these concept in just a bit. Let me know your answer in the comment section below. Also, if you're interested in launching a high career growth in artificial intelligence and machine learning, this program might be the best thing you'll ever come across today. The professional certificate in AI and machine learning offered by P University online in collaboration with SimplyLearn and IBM. This isn't just another course. It's a complete career transforming experience. Ranked one online AI and ML certification by Career Karma. This program is designed to help you master the most in- demand skills in AI, automation, chart GBT, generative AI, LLMS, deep learning, agentic framework, and so much more. So whether you're just starting out or looking to upskill, you'll get hands-on with 15 plus real world projects, explore tools like hugging face, tensorflow, midjourney, and even build LM based application. You'll attend live online classes delivered by industry experts and top faculty from Purdue and IBM covering everything from prompt engineering to building intelligent agents. Plus, you'll earn a recognized certificate from PU University and unlock access to their prestigious alumini network. So, what are you waiting for? Hurry up and enroll now and you can find the course link below. So, let's start by understanding what is generative AI. Now, generative AI is a type of artificial intelligence that is designed to create something new. It doesn't just repeat information. It learns patterns from massive amounts of data and then generates fresh content, text, images, code, music and even video. You can think of chart GBT mid journey or daily. You give the prompt and then they create an output. If I say write me a bedtime story about space traveling cat, it will instantly write one. That's generative AI in action. So let's talk about how it works. That is the model structure. Now the heart of generative AI is something called large language model LLM. This is like the brain of the system. So it's trained on billion of words from books, Wikipedia and online articles. So it understand how languages work. The model uses something called transformer architecture which basically breaks down text into smaller units called tokens and learns the relationship between them and using probability it can predict the next word sentence and even image pixel based on patterns it has seen before. So in simple words is a superpowered autocomplete but instead of just predicting the next word it can generate entire essays code snippets or even pictures. So let me show you a demo of how chat GPT works. So now I have chat GPD here and I'll be just giving a prompt that write a 100word story about a space traveling cat. Okay, now I'll just hit on enter. So now you can see it's giving feedback on a new version of charge GPD. We have two responses here. So the first response is bit shorter as compared to the second one. So from here you can see how instantly it creates a unique story. This is called generative AI in action acting as a content creator. So from here you can select any of the response which you like. So you can just select on response to. Yeah, that's it. Talking about the key characteristics, it's great at creativity. You can see from here. It's great at writing, designing, summarizing, coding. But it do have some limitations that is it does not understand real facts and it can also hallucinate. Now let's talk about the AI agents. Generative AI is just like a brain that writes. But what if we gave that brain some hands and tools? Now that's where AI agents come in. So an AI agent is a program that not can only generate answers but also take action. It uses generative AI as its brain but it's connected to external tools, APIs or memory. So instead of just answering your question, it can perform a specific task for you. So let's talk about the model structure and how it works. So at the center you'll again find the LLM which is the brain. Just like in generative AI, this is the part that understands and generates language. But here's the twist. The brain isn't working alone. Around it, the AI agent is connected to tools and APIs that can call whenever needed. Let's say for example, it might connect to a flight booking system to check tickets, a calculator to solve math problem, or a database to pull out stored information. Think of these like extra gadgets the brain can use to get the job done. And on the top of that, the AI agent has a bit of shortterm memory. This means while it's in the middle of the task, it can remember when it just did and what it needs to do next. Let's say for example, if you ask it to book a flight, it remembers the destination, the date, and your budget during that particular conversation. So now that you know what AI agents act as smart assistant and a short-term notepad, it's more capable than just generative AI because it doesn't stop at creating answers. It can actually take action using that tools. I'll show an example of using GitHub copilot in VS code. So I have used this agent mode from here you can see and I asked it to generate a sample data set for product analysis in Jupyter notebook and it gave me an answer and it also generated the code for me. You can see from here this live code and it also gave me the output and then I also wanted scatter plot. Then I asked it about the scatter plot. It showed me the graph the scatter plot relationship between the stocks units sold and the product. So you can see that I don't need to even code to do anything. This AI agent itself does everything all the task needed to do. You just need to give one prompt and that's it. Now this is very different from generative AI. Chat GPT alone can't do this. It can only give you the code needed. But then the AI agent can actually give you the entire code in your coding summary and everything needed. Talking about the key characteristics, the autonomy level, it's limited but real. It can decide that which code needs to be done if you want to book a flight, which flight is cheapest. It can find it for you. It's narrow and it's focused task. It's not great at complex multi-step reasoning, but it works best when the task is clear and simple. Now, we were talking about agentic AI, which is the autonomous orchestrator. So far we have seen generative AI which acted as a writer and AI agents the task doers but what if you need something that can plan reason and coordinate multiple steps. Now that's where agentic AI comes in. Agentic AI is just like a superpowered version of agents. It doesn't just do one task. It can manage entire workflow, make decisions and even call other agents to help. is designed to handle complex multi-step goals with minimal human supervision. Let's talk about how it works. So you can think of agentic AI not as a single tool but as a whole system working together. At the center we've got the LLM brain. This brain doesn't just do everything itself but connects with different agents and each agent has its own tool. For example, one agent might use a flight booking API and the another agent might check the weather and another could handle a visa requirement check. Now, who tells them what to do and in what order? That's where the planner module comes in. You can imagine it like the project manager. It decides first check the visa, then look for the flights, and finally, it confirms the weather. But that's not all. Agentic AR also has a long-term memory so it remembers when it left off and keeps track of the progress and if something changes like the flight gets cancelled the system uses feedback loops to adjust the plan and find another option instead of starting from scratch. So in short, Agentic AI works like a smart team with a leader memory and the ability to adapt on the go making it much more powerful than just a single chatbot. Now you might still be confused about the difference between AI agents and agentic AI. So let's use this simple smart kitchen example. Now you can see on the left side we've got AI agent and on the right side we have agentic AI. Now imagine an oven that looks at the dish you have placed inside. I'm talking about in the case of AI agent. It understands what it is and then it automatically sets the right temperature and cooking mode. That's an AI agent. It's smart but it only handles a single task. Now in this case adjusting oven settings. Now on the right side we have agentic AI. You can think of it as a whole kitchen system working together. Here the oven doesn't just configure itself. It talks about other smart devices. It checks your grocery and pantry in the fridge. Considers the time of day and energy usage and coordinates with your coffee machine or other appliances through a smart kitchen hub. It can even suggest recipes based on what you have at home and it automatically configure your devices to match. So the difference is very simple. An AI agent is just like a specialist great at one specific job. And agentic AI is like a team manager. It coordinates multiple agents, tools, and systems to achieve a bigger, smarter outcome. That's why agentic AI feels more autonomous and powerful because it's not just solving one problem. It's orchestrating everything together. All right. So, let's break it down in simple terms and we'll understand a side-by-side model structure comparison. We've got three levels of AI here. Generative AI, AI agents, and agentic AI. So, first let's talk about generative AI. You can think of this as a creative brain and its core it's powered only by a large language model or LLM. It can generate content like writing a story, creating an image or drafting an e. But that's just about it. No memory, no external tools, just pure content creation. Autonomy here is bit low because it only responds to your prompt and nothing more. Next, we're moving on to AI agents. These are a step up. They still have LLM as the brain, but now they're connected to tools and APIs. That means they can take action, not just generate text. Let's say for example, they can book a flight, fetch live data, and even run calculation. They usually work with shortterm memory remembering details only while performing the task. Their autonomy level is medium that is they can follow through on task but they still need your instruction for each job. Finally we've got agentic AI. Now this is where things get really exciting because agentic AI combines the LM brain with multiple agents, a planner and memory. It doesn't just do one task. It can plan and execute a whole workflow. Let's say for example, instead of just booking a flight, it can plan your entire holiday, checking visa requirements, booking flights, hotels, and evenuling activities. It works with long-term memory, so it can learn from context and adapt over time. Its autonomy is a bit high, almost like a project manager coordinating everything for you. So to summarize, generative AI creates content. AI agents acts on tools for specific task and agentic AI plan, coordinates and execute multi-step processes with memory. Now when we're talking about the real world use cases, now generative AI is already being used by big companies. For example, we have Belulk which uses it to automatically write product description while Morgan Stanley relies on it to generate research summaries for their analyst. It's just like having a smart assistant that saves time by creating text quickly. Now, when it comes to AI agents, you can see them in action with Clara's customer support bot, which help answer customer questions. And with Zapio, where agents move data between different apps automatically. Now, these are great at handling repetitive task reliably. Then there's agentic AI, which takes things to the next level. We have Shopify Sidekick which helps store owners manage their shop by planning and taking actions while but financial users uses it for automating money transfers and financial decisions. This type of AI doesn't just follow instructions. It can plan, reason and coordinate task on its own. Of course, each comes with its own cautions. But with generative AI, you should always fast check because it can make mistakes. AI agents work well, but they have a limited scope and it needs regular rule updates. An agentic AI is powerful, but it must have strong guard rails or else it might go off track when making decisions. So now that you know the difference, generative AI is like a creative writer capable of producing new text, images, or ideas. AI agents act as reliable task doers following instructions and completing specific jobs. And then we have agentic AI, the autonomous orchestrators that not only complete task but also reason, plan and coordinate multiple steps on their own. Each level builds on the previous one, moving from simply generating content to using tools and finally to advanced reasoning and decision-m. Now, if this breakdown helped you, don't forget to give it a like, share it with your friends, and drop a comment telling me which type of AI you are most excited to try. So, that's a wrap- up on this video. See you in the next one. Hi there. If you like this video, subscribe to Simply YouTube channel and click here to watch similar videos. To n up and get notified, you can check the description box below.