When ChatGPT launched in November 2022, we got a simple chat window, following a few months behind Google’s own preview of LaMDA 2 through AI Test Kitchen at Google I/O 2022, itself a chat window layered over a model. Type something in, get something back. That was the right call, and not because anyone had done deep interface research. A transformer model is trained to do one thing: predict the next token given everything before it. Feed it a prompt, get a completion, feed it more, get another. A chat window is close to the most direct expression of that loop made visible. It was an honest match for what those models were good at, an admission of what the technology could do yet, not a considered design choice.

Models have gotten more capable and efficient since, and what we ask of them has gotten far more complex. Somewhere in that gap, the chat window stopped being an honest match and became a habit. We have spent the years since watching agentic AI show up everywhere: code editors, browsers, the operating system itself, the enterprise tools people already had open. Through all of it, we kept handing people the same box. Our reluctance to move past it is a real drag on adoption, and it is one of the bedrocks of our point of view at Vycari.

Anyone who works in this technology every day already knows a specific version of this problem: the blank box itself is hard. Staring at an empty chat window and guessing how to phrase what you want, what the agent can do, which words trigger the behavior you’re after, is its own skill. It is hard for people who spend all day in this field. It is much harder for someone who does not, who has no reason to have built a mental model of what an agent can do from looking at an empty rectangle.

My co-founder Chris Perry made a version of this same argument last week, coming at it from adoption and product history rather than the interface argument here. Ours is a two person company, and we did not coordinate this; we just keep running into the same wall from different angles. It is part of why I like building with him: he thinks about a problem from the side I don’t, and by the time we compare notes we have usually converged without meaning to. His post is worth reading alongside this one.

Agents outgrew the box

Last January I wrote about how everything becomes an agent; the argument underneath was really about decision making. An agent earns its keep when the input is hard to predict ahead of time, when the alternative is branch logic encoding what a developer guessed the user would need, written months before that user showed up. Treat an agent as a piece of your program that makes the call in the moment, with the actual input in front of it, and it looks like a much bigger part of how software gets built than a chat window would suggest.

None of that requires typing into a box. An agent deciding which tool to call inside a checkout flow, a support queue, or a scheduling tool does not need a text field to do its job well. In a lot of what we build at Vycari, the agent takes a backseat to the interface on purpose. Nothing beats a solid interface that is familiar and delightful to use, and an empty chat box waiting to be filled is neither of those things for most people.

Chat is the hobbyist’s interface

I wrote a few weeks ago about why not everyone wants a hobby, and this is where that argument meets this one. A chat interface rewards people who already know how to prompt, who enjoy the back and forth, who get a small thrill from coaxing a good answer out of a model. That is real and worthwhile. It is also a hobby, and most people do not want one. They want the thing handled.

This is not a new problem for our industry. Unix was a command line. So was DOS, VMS, Ultrix, and SunOS, the whole family of operating systems that ran the serious computing of their era. Knowing the right incantation, the right flags, the right order of operations was a hobby in its own right, and it kept computers in the hands of people willing to learn that hobby. The graphical interface Xerox PARC prototyped, and that the Macintosh and then Windows brought to everyone else’s desk, did not make computers more powerful. In most ways it made them less flexible than a shell. What it did was let someone finish a task without first learning a syntax and a vocabulary of magic commands, as Chris put it. Nobody wants to read a manual to cross the next thing off their list. That was true of computing in 1984, and it is true of agents now.

Look at the products getting the most attention right now, open source projects like OpenClaw and Hermes, or the frontier lab flagships like Gemini Spark, Claude Cowork, and ChatGPT Work. Nearly all of them are built on the same premise: you will chat with the agent. My own Gemini Scribe started the same way, a chat window bolted onto Obsidian. Sometimes the chat happens over voice instead of text, but underneath the voice layer is the same box, just one you talk to instead of type into.

Meeting people where they already are

I do not think most people want to learn a new modality to get value out of AI, and I do not think they will, even though some of us genuinely enjoy it. Agentic AI is a shift on the scale of the ones we have already lived through: personal computers, the internet, the web, mobile, and now AI. I made a version of this list once before, and each of those transitions asked people to change how they worked. The ones that won met people with an interface they already understood, not the ones that made them learn a new vocabulary first. I want to be careful here: I am not arguing for skeuomorphism, for software that pretends to be the paper or the switch it replaced. I am arguing for good, intuitive design, interfaces that borrow the mental model people already carry and build the new capability on top of it. Every time this industry has moved toward a more familiar interface and a design language people already recognized, it created more value, not less, because it stopped spending people’s attention on the tool and let them spend it on the task.

That is the standard I want agent products held to. Not “does the model perform,” because at this point almost all of them do. The question is whether the software finds you where you already are, understands what you are actually trying to accomplish, and gets you back to your day.

Did you have to come back

Early in my career at Google, one of the metrics we watched for search was whether you came back. If you searched, clicked a result, and never returned, that meant you had found what you needed and moved on. I think agents deserve the same measurement. Did you get the thing done? Did you have to come back and try again? The best outcome is that you do not come back, not because the product failed you, but because you already had what you wanted and were on to the next thing.

In our own applications at Vycari, that translates into something concrete. It means the agent understood the request on the first try, without a round of clarifying questions, without asking you to drop into a chat window to add detail it should already have had. Better still, it means the agent acted on your behalf and you never had to watch it happen. In my ideal world, people using what we build will know this is agentic AI about as much as they know it is written in Python: not at all, not because we are hiding it, but because it will not be a fact that matters to them.

We should not be building toward the box. We should be building toward wherever the user already is, and toward what they are actually trying to get done. Agents are not a hobby. They are here to help people get through their day, and the sooner our interfaces act like it, the sooner more people will actually use them.