AI Agents Are Not Just Chatbots. Here’s How They’re Different (2025)

Nobody likes chatbots, and I get why. They tend to be clunky, slow, and worst of all, unhelpful. That probably explains why more people would rather wait on hold to talk to a human agent than get instant help from a computer program that can only operate within extremely narrow parameters.

As AI agents increasingly dominate tech conversations, people are understandably wondering: How do agents differ from chatbots? Don’t we already have those? What’s all the fuss about?

Complicating matters further is the general confusion over what agents actually are. One expert noted that the concepts of “agentic” workflows had meaning—until “marketers and a few big companies got a hold of them.” Now, they’re at risk of becoming a meaningless buzzword, part of a conference-speak soup in the same vague category as “synergy” and “metaverse.”

It’s a fair point. But throwing the agents out with the bathwater would be a mistake, given their genuine potential to transform how business is done. So let’s dig into why AI agents are different—and much more powerful—than the chatbots of yore.

Defining The Terms

Chatbots are pretty simple: They’re computer programs that simulate human conversations, but function off of a predefined set of scripted responses. If your request sits within their narrow parameters—say, canceling a hotel room or checking a flight status—then odds are good they can successfully handle your request. They may speak in natural language, but they have no ability to understand context or engage in high-level reasoning. If you have a question, or need more information beyond their programmed knowledge, you’re out of luck.

AI agents, on the other hand, may superficially look and sound like chatbots—but that’s where the similarities end. Unlike chatbots, agents aren’t hemmed in by a predetermined set of rules. Rather, they’re built on large language models (LLMs) and trained on huge datasets, allowing them to offer complicated, nuanced responses to questions and engage with users on a significantly higher level. For businesses, a major perk of agents is that they can be trained on company-specific data, making them experts in their fields.

Agents Are Always Improving

Traditional chatbots are static—their responses are only as good as their most recent manual update, and any improvement requires developer intervention.

AI agents, on the other hand, have the capacity to continuously improve. What makes agents in particular different from even AI-powered chatbots is their ability to conduct multi-step interactions across different platforms. For example, the restaurant conglomerate Yum! Brands recently announced it would be implementing agents across its fast food chains, including Taco Bell and KFC, where they will not only be responsible for tasks like taking orders, but also strategizing how to speed up long drive-thru lines and generating action plans for underperforming locations, among other things. The agents can then adapt their approach based on real-time feedback and changing conditions, setting themselves up for a cycle of continuous improvement. We’re experiencing this firsthand at my company, Jotform—we built our agents for customers, but because our own teams are using them so much, we’re refining their capabilities for ourselves.

Agents Are Easier To Implement

Given that agents are so much more advanced than chatbots, you would think implementing them would be that much more challenging. In fact, customer service chatbots need a ton of training in order to be able to capably handle natural language requests. And even at that, their interactions are still limited to what they’ve specifically been taught.

Agents, on the other hand, are actually easier to implement and launch. Out-of-the-box agents like those used for sales, lead generation, and customer service are pre-built and easy to set up; there are also low-code options for non-programmers, like Langflow, n8n, or Salesforce’s Agentforce, which let anyone quickly build an agent tailored for their specific needs.

Generalists Vs. Specialists

Because chatbots operate within defined parameters, they tend to be extremely specialized. That’s actually fine for businesses that get a high volume of the same frequently asked questions, like a retailer fielding queries about product availability or a delivery service answering questions about package tracking and estimated arrival times.

However, this specialization becomes a limitation when customers ask questions that fall even slightly outside the chatbot’s programming. The conversation quickly breaks down, leading to frustration and ultimately a transfer to human support.

Agents, on the other hand, can handle a range of inquiries, making them great for customer-facing issues and business-side automations alike. In some cases, however, it may be better to build separate agents for specific tasks—because they can work together, an entire team of agents is ultimately the most effective way to achieve a goal end-to-end. Chatbots, specialized as they are, lack that flexibility.

Chatbots may appear similar to AI agents, but they’re actually extremely different. Agents are heralding a new era in how businesses can not only improve their customer-facing support, but optimize and improve their operations. Much more than a buzzword, agents have the power to genuinely transform how business is done.