Intelligent Websites: AI Agents vs. Chatbots on Your Website

This article explains the differences between AI agents and chatbots and their varying impact on website functionality and risk.

While agents and chatbots are often mentioned together, they’re different items, based on different tech. This article dives into the differences beyond this:

• AI agents work (on someone’s behalf) to do things

• Chatbots talk to people, or to other AI (also on someone’s behalf)

First, let’s take a look at how these differences impact websites.

Agents and chatbots have different risk profiles

Chatbots respond. They talk, but they don’t usually act. If one goes off-script, it may confuse a user, but it won’t alter data or trigger a transaction. It’s still important to have guardrails in place — disclaimers, usage boundaries, and awareness that some people are actively trying to exploit chatbot outputs to gain access, discounts, or refunds. This is why we study AI prompting, and why we protect websites with legally sound practices.

Agents can act. They may submit forms, modify records, send messages, initiate workflows. A mistake by an agent can have real-world consequences. For a site owner, this raises the stakes. Internal oversight, testing, and fail-safes become essential.

Different design and integration paths

Chatbots are often modular and surface-level: you plug them in, feed them content, and they run. We work with chatbots most frequently.

Agents require deeper integration — into APIs, databases, or workflows. That affects implementation time, cost, and ongoing maintenance. We're not recommending custom AI agent integration for most site owners at this time. In many cases, the same business logic can be handled more safely — and far more affordably — using Wix site apps, which are often AI-powered themselves.

You can, of course, have an agent that is also a chatbot. That’s common. But they often run on different architectures, with different technical priorities. There’s a good reason for that.

What they share is important: both can now be connected to a website — not just visually, but structurally. That means they can access data, handle input and output, and interact through the site’s interface. They can retrieve information, take action, and respond dynamically to what a user is doing — or trying to do.

People have been predicting for a while that AI agents would shape the future of computing (here’s one example), but for anyone who’s been thinking about other things, it’s useful to know that this isn’t speculative anymore. We’re already living through it.

Chatbots aren’t new, but what they do and how they do it has changed. The shift from scripted dialogue trees to systems like ChatGPT is not a linear improvement. A modern chatbot is to its early ancestors what ChatGPT is to your laptop.

And even though chatbots and agents often get lumped together in the media, it help to be precise about what we're talking about. These aren't interchangeable terms, even if their interfaces seem identical.

The Technical Distinction: Chatbots vs. AI Agents

As mentioned above, a chatbot is primarily built for conversation. It operates at the communication layer — engaging with either humans or other systems. Modern chatbots are typically powered by large language models (LLMs) like GPT-4, Claude, or Gemini, trained on vast datasets to generate human-like responses. A good chatbot can interpret questions, relay information, summarize content, and personalize responses based on context. While sophisticated chatbots can simulate understanding remarkably well, they're fundamentally designed for dialogue. Their core function is to communicate effectively.

An AI agent, by contrast, is built to perform tasks. It typically leverages an LLM for reasoning and understanding, but extends these capabilities with additional components like tool use, API connections, planning systems, and execution logic. Agents can initiate processes, access databases, make API calls, modify data, or handle complex automation workflows. The key distinction is that agents are designed to accomplish specific goals autonomously, often working through multi-step processes without constant human guidance.

In practice, the line between chatbots and agents has become increasingly blurred for end users. Many "plug-and-play" solutions abstract away the underlying complexity:

• A user might interact with what seems like a simple chatbot interface, while behind the scenes, a sophisticated agent framework is orchestrating multiple tools and services

• Platforms like OpenAI's GPTs or specialized assistants built with Claude or Gemini hide the agent architecture entirely, presenting what appears to be just another conversational AI

  
  

Both systems can connect to websites and applications through various integration methods:

• Embedded chat interfaces - The most visible implementation, where users directly interact with a system through a chat window

• Modal components or sidebars - Context-aware assistants that appear based on user actions or page content

• Headless systems - Backend implementations that process data and perform actions without a direct user interface

• API endpoints - Structured connection points that allow systems to exchange data and trigger functions

• Trigger-based systems - Platforms like Zapier, Make, or Wix Studio that enable no-code/low-code automation based on specific events or conditions

  
  

In most modern implementations, users may not always know (or need to know) whether they're interacting with a pure conversational (chatbot) interface or a more complex agent system. But for developers and business stakeholders, understanding the architectural differences helps inform better implementation decisions.

Additional important considerations:

The most powerful solutions often combine both capabilities, for example, using conversational interfaces as the "front door" to determine user needs, then dispatching specialized agents to handle specific tasks behind the scenes. This hybrid approach is the direction of modern web development is headed, where a single interface might connect to multiple specialized systems rather than relying on a single all-purpose AI.

As technology continues to evolve rapidly, with the boundaries between chatbots and agents will perhaps keep blurring. We can expect to see LLMs based chatbots becoming even more capable, while agent frameworks will likely become integrated with large systems like Google Workspace and Wix Studio, making them accessible to everyone.