There’s been a lot of talk about “AI agents” lately. But what does this really mean?
The official definition of AI agent, according to Google, is a software system that uses AI to pursue goals and complete tasks on behalf of users. That’s still pretty general.
To get a better understanding of what an agent is, let’s take a look at a familiar example: ChatGPT.
A very familiar agent
In the olden days, and by that I mean 2022, if you typed a prompt into ChatGPT, it sent that prompt pretty much directly to the GPT AI model and then served you back the response. At the time, ChatGPT was really just a prompt/response interface for GPT.
Then DALL-E was released as an image generation model, and you could type in a prompt to get DALL-E to make an image for you. ChatGPT, at that time, was still little more than a traffic cop; you’d type in a text or image request, and it would route the request to either GPT or DALL-E depending on whether the prompt started with “image of…”
Keep in mind that at the time, each AI model was confined to what it learned during its training cycle, which was already long over by the time you typed in your prompt. If it didn’t have the answer in its training, the model might make something up based on what it did know, a practice we call “hallucinating”. And the hallucinations could be pretty wild.
But now, in 2026, ChatGPT works differently. When you type in a prompt, ChatGPT takes a look at what your intent is, and might do any of a number of things depending on what it thinks you want. For example, if you type in “images of Mount Rushmore,” ChatGPT might just go find some existing photos and serve those up as the response, rather than creating something new. This saves tokens, and gives recognition to creators who are already in the business of taking great photos of landmarks.
These photos weren’t part of a model’s training. They are simply what the agent went out and found, on the fly, after reading your prompt.
Another example is a prompt like, “What is the architecture like in the Akihabara neighborhood of Tokyo?” The AI agent might use Google Maps to find photos from that neighborhood, then feed those photos to an AI model and tell it to analyze the architecture in the photos. The model already knows how to analyze photos, based on its training. The difference is that an AI agent found the photos and served them to the model itself, rather than you having to go find photos and the model yourself.
In this regard, ChatGPT now functions more like an agent than an AI model.
We also see similar behavior in Google Search. When you enter AI Mode, Google doesn’t just perform as a search engine anymore; it acts as an agent to go find you the answer, then summarizes it for you.
Agent-ness is a spectrum
Is ChatGPT actually an “agent” now? That’s a great question. The thing is, the agentic quality of a piece of software is a spectrum, ranging from agents that do everything for you to mildly assistive agent-like interfaces that require you to do 99% of the work.
For example, a lot of websites now have AI chat agents that will answer your questions without needing to involve a human. The least agentic ones require you to do all the heavy lifting by phrasing things a certain way, or by choosing from a menu. The most agentic ones can take a human-worded question like, “I’m looking for a toaster. I never make bagels but I do toast thick bread, sometimes with cheese on it,” and figure out that what you really need is a toaster oven.
Hopefully these examples answer the question “What is an AI agent?” to your satisfaction, so you can recognize an agent when you see one. And always keep in mind that agents are software, not people, and are prone to making mistakes, sometimes even more than humans are.
No AI was used in writing this post.