NEW ChatGPT Agents Explained — Stop Googling, Start Delegating!

Open AI just changed the game with the Asian feature. Now Chad GBT is truly all powerful, baddest, meanest, coolest AI Asian there is. All without you, all without any setup or steep learning curve. It's deep research operator and reasoning all in one package. Connects to apps, browses the web for you, creates automations and a lot more. If you can do it, it can do it too. And to use it to its full potential, you really have to understand what AI agents are, how they work, how they are different from automations, and how you can create your own agents with zero coding. What

even is an AI agent? In one sentence, an AI agent is like a chatbot with the ability to take actions on its own using other software or tools. Don't let the fancy term scare you. At heart, most AI agents are basically just custom bots powered by GBT or similar models. If you've used something like Chat GBT, you're already halfway there. The big difference is an AI agent isn't limited to chatting. Can do stuff for you by hooking into other apps or services. So, how is an AI agent different from the automations you might have seen before? Think about a normal automation like those made with tools such as Zapier. Traditional automations are like a rigid checklist or recipe. Do X, then do Y, then do Z exactly as scripted. If something unexpected pops up, they freeze or fail because the script didn't anticipate it. Basic automation just waits for one specific trigger and then runs through its preset moves without deviation. AI agents are way livelier. They start with a goal. They pick their own moves to reach that goal and they can even change tactics on the fly if a tool fails or if the goal needs a different approach. They have a sort of decision-making ability that old school automations lack. Chat GBT by itself can

chat and write sure but ask when is my next meeting and it tricks. Your calendar isn't part of its knowledge. Now imagine we add a tiny automation step. If the user asks about meetings then check Google calendar. Suddenly, ChatGpt through that automation can answer, "Brainstorming with Andrew tomorrow at 3 p. m. Sweet. " But then you ask, "Great. Now check my email. " And the script crashes. Checking email wasn't in the plan. You could keep bolting on more fixes, a weather API step, voice alert, reminder for tomorrow, etc., but you'd have to hardcode every single possible branch. Inside our AI 101 course, we included a short guide on building custom GBT experts to help you skip the trial and error and get practical results fast with no code. That's still just a plain automation, not a real AI agent because it can handle anything it wasn't explicitly programmed to handle. That's what the new agent fixes. It gives Chad GBT the power to do all that without much integration. if it can be done from a browser, clicks by itself where it needs to, sets everything up and gives you the result and all you have to do is log in to a website and that's it. With automation, you could offload up to 70% of your customer support calls to a virtual agent. That sounds human and still keep customer satisfaction above 90%. Sounds like sci-fi, right? But that's what Leapin AI is doing. The sponsor of today's video, Leapin AI's voice agents talk naturally and handle even complex calls and setup. No developers need it. It's a no code platform, so you can get it running without writing a single line of code. Integration is straightforward and these agents support multiple languages right out of the box. For me, the realworld results are the most impressive part. One of Germany's biggest wine retailers had Leapin AI handle 100% of one brand support calls and their customers stayed happy. Another retailer's AI agents converted 30% of labs leads in one week, something their human team took 5 weeks to do. Plus, these AI agents keep learning. After each call, they analyze it, tweak their responses, and get better on their own. It's like having a support rep who improves every day without extra training. So if scaling up support is on your road map, Leaving AI is worth a look. It's easy to set up, delivers human level service, and keeps getting better. You can check them out via the link below if you're curious. Definitely worth a look if scaling support is on your road map. What's the

secret sauce that regular automations are missing? One word, brain power. An AI agent brings in an AI brain, usually big language model, to make decisions dynamically. Instead of only following a rigid script, an AI agent can essentially think to itself, hm, I need to answer this user's question. Do I need to use a tool? Which one? Did that result solve the problem or should I try something else? In short, the agent can reason about the task and adapt as it goes. That's a gamecher compared to the old if this then that bots. All right. Right, now that we know conceptually what an AI agent is, let's pop open the hood and see how one actually works. Underneath, all AI agents use the same basic building blocks, just arranged or tuned differently for each use case. There are five core elements that make up an AI agent's structure. Trigger, reason, and engine, memory, tools, and output. Let's break those down. Trigger. So, this is the wakeup call for the agent. The event that starts the whole thing could be a schedule time like a cron job at 8:00 a. m. every day, a new message, for example, a DM on Telegram or Slack, a new row added to a spreadsheet, a web hook from another app, you name it. The trigger is basically anything that shows, hey, start the workflow now to kick off the agent. Often a trigger comes with some input data. For instance, the content of that new email or the details of that new spreadsheet row. So the agent has something to work with immediately. Reasoning engine, the agent's brain. Once triggered, control passes to the reasoning engine. This is usually a large language model LM like GBT4, GBT3. 5, Google's Gemini, Anthropics Claude, or whatever AI model you've plugged in. Think of this as the agent's brain. The model looks at the input plus any initial prompt or instructions you've given it and then it plans and decides what to do first. We'll break the overall goal into subtasks. Choose an action, execute it, then evaluate the result and plan the next action. Many no code platforms literally have an AI agent block or node that encapsulates this reasoning loop. Agents need memory so they don't lose context or repeat themselves. There are usually two kinds of memory. Short-term memory halts the running conversation or recent events within the current session. It's like the agents working in memory, so it remembers what just happened a moment ago or what the user asked earlier in the chat. This way, it doesn't forget information as it moves from one tool to the next during its reasoning cycle. Long-term memory is stored outside the immediate session, often in a database or vector store. This is for anything the agent should remember between runs or recall from past knowledge. For example, a user's preferences, past results or facts it learned yesterday. It might be a simple database, a Google sheet, or a fancy vector database for semantic recall. In plain terms, short-term memory is the agent's attention span and long-term memory is its knowledge base or notes that persist between sessions. Now, tools, these are the actions or skills the agent can use to interact with the world outside its own AI brain. Think of tools as the agent's hands or eyes, APIs, apps, or functions it can call. Examples of tools include making an HTTP request to fetch data from website, quering a database or Google spreadsheet, doing math calculations, sending an email, checking your calendar, etc. The agent doesn't have these abilities by default. You explicitly give it a toolbox. The popular design pattern is often called react, reason plus act. The agent, the LM reasons about what it needs, then chooses a tool to act, gets the result, then reasons again, and so on. Essentially, tools let the AI step out of just thinking in text and actually do things in the real world or digital world at least. So, finally, the agent needs to produce an outcome or response. The output node is how the agent delivers the goods at the end of its process. This could be as simple as spitting out a chat reply to a user, or it could be more actionoriented like adding a new row in a Google sheet, updating a record in a CRM, sending a message on Slack, or even generating a file. The agent keeps looping, thinking, using tools, updating memory until it decides it has achieved the goal and can produce a final result. That final result is then output through whatever channel makes sense. All AI agents you build will use some version of these five pieces. The cool part is they are modular. Of course, how smart any module X hinges on prompt quality. So, our AI 101 course has a hands-on iterative prompt refinement lesson that lets members see immediate results. Today, your agent might use a time trigger and GBT4 with a Google calendar tool to be a meetinguler. Tomorrow, you could swap the trigger to new email arrives. use a different model, plug in a spreadsheet tool instead, and it's a data entry assistant. Nothing fundamentally breaks when you swap out one component for

another. The framework stays the same. That means once you get comfortable building one AI agent, you can reuse that same pattern to make lots of different agents without starting from scratch each time. I've hinted at this already, but let's tackle it head-on. What exactly makes an AI agent autonomous as opposed to just a workflow or simple script? This is a crucial point for understanding why AI agents are considered a step beyond ordinary automation. But don't get too excited. The agent from OpenAI isn't fully autonomous. It does a lot, but still needs you to log in or click where it can't. So, you better still look at the screen where it does its thing for the first time. And once you figure out the prompt and the pipeline, sit back and enjoy the show. An agent becomes autonomous the moment the AI, the LM starts calling the shots without a human pre-programming every step. In a classic workflow, you the human had to anticipate and code every if this and that branch ahead of time. So the system can't improvise beyond what you hard wrote. But in an autonomous agent, you hand the steering wheel to the AI model. The agent decides things on the fly, which tool to use, whether the result is good enough or if it should try something else, whether it should loop back and attempt a different approach. To break it down further, this self-driven behavior rests on three key abilities that autonomous agents have and regular automations don't. The AI can take a fuzzy goal, for example, create a weekly content calendar for my blog and figure out a plan to achieve it by itself. Can break the goal into subtasks on the fly, find trend and topics, then outline article ideas for each, then schedule them on a calendar. You didn't script those exact steps. The AI planned them in real time. The agent can choose which tools to use and in what order. at runtime decides it needs to do a web search first, then call an AI writing tool for summaries, then use a CMS API to schedule posts, all because that sequence looks like the best way to reach the goal. In a non-aututonomous setup, you'd have to predefine the exact sequence. An autonomous agent can make those choices on its own react style as the situation demands. The agent stays goal driven and constantly asks itself, "Am I done yet? Have I achieved the goal? If the answer is no, say an API

tried down or the data fetched looks wrong or empty, the agent can pivot to different strategy instead of just crashing and stopping. Maybe the web search failed. We'll try a different search source or different query. Maybe one tool didn't give a good answer. Can try an alternative tool or just the input. This ability to handle unexpected hiccups and keep trying new approaches until the job is done or a set retry limit is hit is what separates a level three autonomous agent from a level one or two automation. Now autonomous doesn't mean uncontrolled or endlessly learning by itself. You, the creator, still set the boundaries and guard rails. You define what tools the agent is allowed to use, what it's not allowed to do, how many times it can loop, and so on. Think of it this way. The agent is driving, but you build the car and design the road it's allowed to drive on. The AI model isn't magically improving or learning new facts each time on its own. It's not becoming Skynet. Learning is at the core of everything in AI. These models keep getting better because continuous training on new data. To build great agents, you also have to keep learning. An AI agent can only be as effective as the prompts and instructions you give it. Take prompt writing for example. I've been honing that skill for over 3 years. I've tried every tool, every feature, and kept an eye on the job market. Let me tell you, it ain't pretty out there if you fall behind. AI is automating jobs left and right, and more job postings expect you to know how to use AI tools effectively. You have to stay ahead of the curve, but learning everything by yourself can be tiresome. Trust me, I know if I tried to cram everything you need to know about AI into one video would end up 3 years long and put everyone to sleep. That's why we spun all our best practices into Generative AI 101 crash course. The course guides you from your very first prompt all the way through advanced tricks that turbocharge your output quality. And it's not a dump of info all at once. We drop fresh lessons every week so you can learn at your own pace and constantly stay up to date. We distilled my team's hard one insights into snagsiz modules packed with visuals and crystalclear walkthroughs. And because you are here hang out and AI master, you can snag a one-year subscription at 63% off. Yep. Seriously, hit the link below, hop in, and start turning that AI curiosity into hands-on mastery. It's the fastest way to get good at this stuff without the three years log. All these AI agents we're talking about are essentially super flexible workflows that can adjust themselves within a task. I like to say they're like really capable interns. They work hard in a task and even correct themselves until the work is done right. But you still want to supervise them so they don't run a mock. Remember those early experiments that made headlines like AutoGBT and Baby AGI? Those were autonomous agents let loose in the wild with almost no restrictions. They were equal parts genius and chaos. They could roam the internet, write code, and compile research on their own. But they also tended to spiral into nonsense or get stuck in loops because they had too much freedom and not enough oversight. Modern builders, the smart ones anyway, have learned from that. Today, we give agents autonomy but in a sandbox. For example, inside tools like Zapier or NAN or within a custom controlled environment, we whitelist the APIs they can use and we dictate where their outputs go. Essentially, we let them drive but only within safe boundaries. So when you hear marketing hive about fully autonomous AI agents, translate that to the AI can reason, act, and iterate toward the goal you said just in its own route. Instead of waiting for a human to tell it each step, that shift is powerful. It means

you can automate tasks that used to require constant human judgment and babysitting. The agent will handle the nitty-gritty work like drafting content, reconciling data, scheduling events while you step back and become more of an editor or overseer. You move upstream to focus in strategy and final tweaks instead of spending all your time on button clicking, copy, paste, and grind. In other words, the agent handles the 80% of busy work so you can focus on the high impact 20%. So, how do you actually build one of these agents for yourself? Do you need to be a programmer or AI scientist? Well, good news. You don't need a computer science degree or even any coding experience to create useful AI agents in 2025. The rise of no code and low code tools means building an AI agent is often a drag and drop experience. You can think through the logic and design of what you want, then let the platform handle the technical heavy lifting. The easiest way out would be just learning to use the chat GBT agent. Seriously, there's no simpler entry here. It's easy to learn. It does a lot, but it's still limited and stumbling still something it does fairly often. So, if you need something real, I'd suggest building a proper agent yourself. There are several no code platforms out there. They're adding AI agent capabilities. NAN, Zapier, Mag. com, R Zapier AI agent workflow, new article in Feedley triggers Chad GBT to summarize it. Then Zapier saves that summary to a file and uploads it to our custom GBT knowledge base. The moment an

article comes out, our agent adds a summarized version of it into our knowledge base automatically and we set the whole thing up with simple no code blocks in Zapier. No manual copy pasting, no code. If you want to replicate it, our brand new Prompt Lab Pro bundle includes prompts that show you exactly how to map and test these automations flows step by step. This is a real example of an AI agent build with everyday tools. It listens for new info, uses AI to understand that info, and then takes action by saving it where we need it. If we can do this, so can you. In short, if you can describe the steps of a task in the plain language, there's likely a no code platform that can turn that description into an autonomous agent for you without you ever touching a single line of code. It's never been easier to get started. So, here's your cue to experiment. The best way to learn is to get your hands dirty in a no code way, of course. Start small and simple. When you see that first hands-free result pop into your inbox or chat, it's a magical moment. It's the gateway drug to thinking, "What else can I upload to an agent? " You'll start spotting all sorts of mundane chores in your life or business that a little AI assistant could handle. AI agents are like digital teammates that don't sleep, don't need coffee breaks, and get a little smarter with each upgrade. Now that you know how they tick and where to start, the next move is simple. Bell your first one, watch the magic happen, and then let your imagination snowball. Before you bounce, don't forget to lock in your 63% off AI Master membership. The link's waiting for you below. If you found this helpful, drop a comment about what kind of agent you wish existed. I read them all and it might inspire the next project. And of course, smash the subscribe button, like button for more AI guides. Go enjoy those extra hours that will start appearing in your week. You've earned it, and your new AI agents will make sure of it.