3 Reasons AI Agents Are Better Than Chatbots In 2026

Instead of having to retrain static bots on new scripts, I was able to just do that. And the amount of time it takes to do that little voice thing I did or the amount of time it takes to just have it refresh things like my website. Like on the personal knowledge base, I literally have it refreshing on different uh landing pages. Of course, that's going to be better than having to retrain the data uh manually and uploading it over and over again. This is awesome. Way less overhead, way less time taken. Hi, my name is Dmitri Bonichi and I'm a content creator, agency owner, and AI enthusiast. You're listening to the AI agents podcast brought to you by Jot Form and featuring our very own CEO and founder, Idkin Tank. This is the show where artificial intelligence meets innovation, productivity, and the tools shaping the

future of work. Enjoy the show. Hey there, my name is Demetri and welcome back to another episode of the AI Agents Podcast. In this episode, we're going to dive into a couple of different things that I think will really tie in why you need to focus on using AI agents and not worry about chat bots anymore. This is three reasons why AI agents are better than chatbots. So, first let's actually just define terms, okay? Cuz AI agents, chat bots, they might mean different things to different people and you always got to define terms in context like this. So first and foremost at their core chatbots remain software that engages in scripted or retrievalbased dialogues to answer predefined questions. Now AI agents are different. AI agents are autonomous systems that are capable of perceiving their environment, planning multi-step actions, calling tools and APIs, and executing tasks

end to end without constant human oversight. Now, why does this shift matter? Because I mean, it's mid2025 at this point, right? Enterprises demand solutions that do more than just respond to queries. And this is going to be a couple of the different ways that AI agents essentially outperform chat bots via just their execution of the work that they're doing. So first let's talk about autonomous task execution. AI agents decompose highlevel goals into subtasks, sequence them intelligently and invoke externally available services like APIs and different endpoints that are really important to note that can be connected very seamlessly with AI versus chat bots. You don't need to complete manual intervention in these steps either. It's very very useful and they have the ability to handle errors in a dynamically planned out way. So why can't chat bots do this? Chatbots can't do this because they're only

able to do scripted or retrieval only logic. So most chatbots are basically built on predefined scripts or simple rag or retrieval augmented generation that has a pipeline that fetches canned answers from a knowledge base and there's no native tool invocation. Chatbots are only basically built in orchestrators. They can't natively call on the API or anything like that. Now, with our tool, Jot Form AI Agents, the nice thing is we have shown you before what it's like to truly have an AI agent system that does things with other tools. Like for example, unlike chatbots, it has the ability to connect with so many different tools. If I go to train, you'll see tools are immense here. It's not just getting different points of information. And it also has the ability to set appointments with my Google calendar. It also has the ability to send files to

Google Drive, take photos, create tasks on Jot Form boards, send messages via Slack, trigger different workflows, and send to other tools like this. One of my favorite tools is Relevance AI. And the reason is because it can do so many different things with different tools. Like the entire pitch of relevance AI is how quickly you can create these AI agents because you can literally utilize their prompt builder where I just type out some requests and it creates an output like this very quickly. As you can see here, it gets personal profiles, recent posts, company profiles, and extracts that data through calls to LinkedIn and then scraping the websites themselves. Like for example, if I were to go to this run section and just quickly copy and do the same task again, the difference between this and the chatbot is chatbot would just be looking up

some random answers that it has and try to figure out whether what I sent it is corresponding to something that is the truth in its back end. Whereas this is calling on the internet, right? This is calling on the internet and then parsing out the information that it's getting and then giving you a proper response. The way that it finally ends for all intents and purposes here is that it does those different steps that I showed and then gives a huge output. Here's the input data. Here's the profile summary, job changes, recent posts, previous experience, cold email insights, best talking points. And this is all done based off of a set of instructions that are predefined, but the agent's able to figure it out on its own, right? It takes these different components and sometimes I don't give it all the components and it still

figures out what parts of the instructions and tools to call and then gives me an output of context of all of that stuff. This is a great leaden enrichment tool and is something that chatbots could never dream of doing at this point. By the way, it didn't even take a minute to do that which is crazy how quickly these tools work. Other AI agentic components you've probably seen are scheduling tasks in CHPT, Langq, Auto Agents, Microsoft Autogen, and a bunch of different tools out there. Some of the implications of this are huge efficiency gains. Organizations adopting AI agents are going to see like 30% probably reduction in the costs associated with different tasks because that manual processing time is going to go away. And it's scalable. You can have so many AI agents running at one time and their window of operation is going to

continue to expand as the hours and hours and hours and days pass this year and in the future. Now, number two, let's talk about another key point that makes agents better than chatbots. They have enhanced context awareness and continuous learning. Agents store user profiles, past interactions, and business rules in vector databases or specialized memory modules, allowing them to reference prior conversations, and user preferences, even across sessions. And modern AI agents employ RL loops, so they propose actions, receive feedback, whether that be success or failure metrics, and adjust internal policies. Over time, they become more accurate with manual rule updates. Now, why can't chatbots do this? Well, most chatbots reset context when a session ends or rely on session tokens that expire. They cannot persist memory beyond a single interaction without manual storage or retrieval code. Chatbots are usually fine-tuned on static data. They cannot incorporate

real-time feedback to refine behavior. And updating a chatbot's responses typically requires a manual retraining cycle, not continuous learning on the fly. Now, this is the case for a lot of agents. Okay? So, for example, even in this tool I just showed you, if I wanted to leave a comment here, I could write something saying like, I think it would be good to expand on, you know, I could say good or bad, by the way, just with the response or I can leave a comment down here. It, by the way, as you'll notice, automatically saves messages into a knowledge table titled agent feedback. It gave a solid response length and quality. I could say I think it would be good to expand on the uh pain points and other things for the email insights to extract from the person and that'll factor it in moving forward.

This is the case in a lot of agents. Um, you'll usually see this. If I go to my website, for example, um, I can ask questions here. And what I really think is important is, uh, with a lot of agents, there's like capabilities to give feedback to the responses. And in the back end here, what's so cool about the training session is I can literally just talk to my agent here. So, I could say things to it. So, it's like, oh, say I want to call it something specific about consulting or resources. Oh, it is great to connect with you. Could you make sure that you add knowledge to your system that prepares for if people ask questions about the infinite content engine? Uh, I would say that it's a free tool that is a complete system that turns the world news into your content

pipeline while you sleep. So, if people are tired of staring at a blank screen wondering what to post next, this prevents them from having to deal with that and the content creator burnout. It is a complete system that includes a relevance AI agent, Make.com automations, a notion content hub, setup tutorials, content templates, and solves content creators biggest problem, which is grinding to find new info. You can find that at the URL. It should be uh in the risproductive.com/newsletter page and there's a link there to get it. Yeah, that's it. I think that should be good. Just add that to the training and that's it. So now when I go back to the chat, you'll see it added that knowledge here. And I could have just chatted with it like this. But tell me in what world a chatbot would have been able to do that

a couple years ago. Uh guess what? It wouldn't have. And some of the implications of this are pretty huge. Higher personalization and user satisfaction is the biggest thing here. Like chat bots were always meh. Like there's no other way to put it. When you didn't get the right response, you got to be you had to be like, "Oh man, I got to go retrain this whole thing." or the users side of it was just the experience of okay I got an answer and then they got the wrong answer then they got frustrated and the feedback loop was basically such that maybe they put a thumbs down and then the person in the back end would have to figure out why it was a problem. It it's not exactly the best system to be honest cuz then retraining took a while. This is much better. This

is important because there's higher personalization and user satisfaction, but also there's lower maintenance overhead. Like instead of having to retrain static bots on new scripts, I was able to just do that. And the amount of time it takes to do that little voice thing I did or the amount of time it takes to just have it refresh things like my website. Like on the personal knowledge base, I literally less overhead, way less time taken. Now, last but not least, there's multimodal and deep integration. So, multimodal input processing is essentially AI agents can analyze text, uh, decode speech as you saw just there. I was able to talk to it, interpret images. There was a integration you saw earlier in Jot Form that asked, can you give them the ability to interpret an agent? So, that was a tool that you can add to the toolkit

and even process short video clips. Now, that's going to get even more improved this year and moving forward. So, just be on the lookout for video being a big deal in AI agents. They fuse modalities to extract intent and decide on an action. For instance, analyzing a customer's photo of a broken heater to autofile a warranty claim. And then from the native system level integration standpoint, agents connect directly to ERPs, CRM, HRIS, IoT platforms, Helenders, as you saw earlier. They stream real-time data and push updates. So, think about it from that chatbot perspective that we just saw, right? It's pretty important. We we had a whole episode on this on the Spotify and e-commerce side. Chatbots are able to do things like say, "Hey, we've been getting a lot of orders. U make sure to check out the database for the amount of items we

have on file and in stock." They're going to be able to make orders for you to get more stock in. Chat bots, on the other hand, no, they're not going to be able to do that. They're going to respond to the questions of people and then be stuck in their little ecosystem. And then when they're essentially going to be uh running out of different items as a company, the AI agent would have been able to help, but the chatbot was not going to be able to help. They're also limited to like text or voice. Sometimes they don't have image capabilities really. They're just uh kind of a little bit behind in that regard. And to connect a chatbot to like an ERP or a CRM, developers need to write custom like middleware, so like web hooks or serverless functions. Chatbots do not have native modules

for deep system calls. They're coming out with so many different things like the model context protocol. It's essentially an open standard open source framework introduced by Enthropic to standardize the way artificial intelligence models like large language models integrate and share data with external tools. This is going to get so much better overall with agents. You're going to be able to chat to agents and it's going to be able to interact with all of the APIs you can imagine. So just imagine a world in where every use case of help in regards to contacting another system. Agents are going to be able to help you with and chat bots just are going to be stuck in the past and won't do this. If I chat with this agent and I'm making a lot of different comments about a certain thing, it's going to learn. It's also

going to be able to reach out to me and say, "Hey, you may want to make another product like this." It's also going to be able to analyze if we're low on stock and order more purchases. These are the different things that AI agents can do that chat bots just can't. And from a broader implication standpoint, they just have way more use cases and way more speed and way more power. and the native connectors that exist are going to save you time and make you more money and save you more money because the interaction with other tools is really what makes AI better than any chatbot could ever be. Because when you have that extra context awareness and then that deeper integration with the rest of the world, you start to unlock things that we couldn't even imagine. A couple months ago, I had like

a trivia bot I made using Chad GPT. It didn't have access to the internet. So, I had to like make up this whole system where it would prevent itself from referencing previous trivia that it made to my team. Now, I can just make an AI agent relevance and have it look up the internet for recent news. That's a very basic change. But once you understand that difference of it being connected to the world and connected in such a way where it benefits us exponentially, there are more and more use cases available as it connects to more tools and as it has a better feedback loop and better training capabilities and more multimodal capabilities as well. So if you're interested in trying out an AI agent that will continuously improve, I would recommend checking out AI agents by Jot Form. We really appreciate if you did.

We also appreciate it if you hit that like button, subscribe, and leave a review on Apple Podcast. We'd really appreciate it. Thank you so much for listening to this episode, and we'll see you in the next one. Bye.