Vercel CEO Guillermo Rauch on splitting models from agents

Known for its cloud infrastructure that allows developers to deploy agents without managing servers, Vercel has quietly become one of the most central companies in AI software. The company currently sees 6 million deployments a day, half of them triggered by coding agents, and more than 1 trillion tokens flow through the company’s AI gateway daily. After the company’s ShipNYC conference last week, we sat down with Vercel CEO Guillermo Rauch for his take on this moment in AI, and how platform companies like Vercel end up competing with major labs.

Here’s a lightly edited transcript. It feels like there’s a different energy in the community this year, fewer pilot programs and more focus on how to make things work well in practice. I’m sure you’ve seen that a lot with clients, but I’m curious what that journey has looked like within Vercel.

Last year was about prototyping. The sky’s the limit, unleash the agents, everyone can build, and so on. We did that, and we learned a lot because we had hundreds of agents organically developed and deployed within the company, and then you started getting into the realities of agents in production, and some of the challenges.

The biggest lesson for me was the home-run use cases, the two killer apps of agents. One is the coding agent, of course. That’s driving a lot of the token utilization in the world, but when you produce so much software, you need somewhere to put it.

The second killer app of agents is the internal agent that helps you run the company. The challenge there is, how do you securely access data? How do you audit what the agent is doing?

How do you get a trail of all of the tool calls and access controls that the agent had to incur in order to get a job done? To solve that, we came up with this framework called Eve, where you can lay out an agents’ instructions and skills in natural language. And another tool is Vercel Sandbox, where you put the agent in a little cage.

It can have the freedom still to express its intelligence, but then you can apply policy on what data it can access and what data can leave the sandbox. What sort of problems does that help you avoid? For [the] sandbox, the biggest advantage is data control.

A real risk of AI that I always think about is, when you get a coding IDE like Devin or Cursor, if you’re in the wrong setting, they may train on your entire codebase. I remember talking to the president of Airbus about this. You have decades of wealth of very specific C++ code for aerospace engineering.

Someone comes in and installs the wrong developer tool and boom, all the code goes out to the cloud for training. I’m curious to hear more about that second killer use case. We all know about coding agents, but what does an internal corporate agent look like in practice?