#312 Jonathan Wall: AI Agents Are Reshaping the Future of Compute Infrastructure

Jonathan Wall (0:00)

I thought it would be interesting to apply the lens of infrastructure to kind of the coming agentic revolution. Got together with some former teammates from my previous startup and hired some new folks, started Runloop.

Craig Smith (0:12)

Right, and so the agent operates in its own virtual machine, not necessarily a web page, not necessarily a browser, or is it a browser?

Jonathan Wall (0:24)

Agents work differently enough and have different enough compute patterns that they need their own new compute primitive. And that is core to what we're building here at Runloop. We call that our dead box. So there are some folks that make agent builders, and that is super useful and super necessary. Some of them are paired to different frameworks, like the folks at Langchain have a cool agent builder that is tightly coupled to Langchain, for example. But what we're playing is a little lower. It's a little closer to the infrastructure layer. And we ultimately want to be the runtime where the agent is deployed and where it executes.

Craig Smith (1:00)

So what I generally do is have you introduce yourself, give your background as far as it's relevant, and how you got to Runloop, and then talk about what Runloop is. And then we'll talk. There's some of the stuff in the materials that was sent over that was interesting to me. You talk about agents changing the shape of compute. Yeah. And I'd like to hear all of that. But let's start with the introduction.

Jonathan Wall (1:38)

Okay. Yeah. Hello. It's great to meet you, Craig. My name is Jonathan Wall. I am the founder of Runloop AI. I suppose the CEO as well. I cut my teeth early in my career at Google. I joined Google not long after it went public, where I was the tech lead of the Google File System. So that was the seminal early days of huge scalable distributed systems. So I had a front row seat and seeing like kind of an explosion of the Internet being realized and new technology being built for it. Around 2009, another engineer and I went out and founded the Google Wallet. So that was the first NFC solution for tap and pay. When you see people tapping phones to pay, that was the technology we created in 2009

At the time, I didn't think about it in as much detail, but it was picking up on the next big platform revolution and technology at the time, which was the mobile phone and the kind of the new capabilities that form factor enabled. Went from the build out of the Internet and at scale infrastructure to working on payments and mobile. From there, I left and started a company with another gentleman from the Google Wallet effort called Index. And we did secure payments infrastructure for tier one retailers. And that company was ultimately acquired, it was named Index. It was ultimately acquired by Stripe and became the Stripe Terminal product. So Stripe's car present product. So I spent a little bit of time at Stripe. And after leaving Stripe, I found the next big platform change or the next big technology revolution of AI. And thought about how I wanted to participate in this particular revolution. Given a lot of my background as an infrastructure, I thought it would be interesting to apply the lens of infrastructure to kind of the coming agentic revolution. Got together with some former teammates from my previous startup and hired some new folks. Started Runloop and we're really trying to build a platform as a service to be the place where you run these agents. And much like the infrastructure build out of GFS back in the day was necessary for the Internet, we think that agents work differently enough and have different enough compute patterns that they need their own new compute primitive. And that is core to what we're building here at Runloop. We call that our dev box.

Craig Smith (4:00)

Yeah. And there has been this kind of explosion of meta-platforms that sit under agents on top of foundation models, to orchestrate and have agents talk to each other or talk to models and all of that. I'm familiar with Boomi. I don't know if you know them. I don't think I know that one. Yeah. And there's another company. I just had a conversation with them yesterday called Dev Rev. And they have a product called Computer. And a lot of these, they have a platform where you can build agents and then manage agents and then have agents work with each other. How does your company fit into that emerging infreco system? Yeah.

Jonathan Wall (4:55)

So you're pointing out that there's a few different layers to this stack. So there are some folks that make agent builders, and that is super useful and super necessary. Some of them are paired to different frameworks, like the folks at Langchain have a cool agent builder that is tightly coupled to Langchain, for example. But where we're playing is a little lower. It's a little closer to the infrastructure layer. And we ultimately want to be the runtime where the agent is deployed and where it executes. I think if you contrast the needs of an agent to that of a traditional server, traditional servers, they get a request. It's usually schemat, right? People use REST or maybe GRPC and protobuffers and REST and JSON, right? It executes some deterministic code. Usually it'll store some state in a database that also is strictly schemat. And the way agents work is a lot different. Agents, they need access to a lot of tools. They need access to context to perform their task. And they have unpredictable behaviors compared to traditional deterministic code. They might use a lot of resources. They might write code for themselves and need to execute it. And what you really need for these agents is a runtime, a place for the agents to execute where that stuff is safe. And you also are able to empower the agents with all the tools they need. And we think that the dev box, which is effectively an execution sandbox, it's more or less giving each agent its own computer, is the right pattern for this. So we can set up the sandbox with security boundaries and isolations. So that if the agent happens to do something unexpected or potentially dangerous, it's isolated on its own sandbox environment. On one side of the coin, you're saying, hey, I want to prevent the agent from doing something accidentally bad. The flip side of the coin is you can say, agents perform better when you give them tools. If I give this agent its own computer, that is effectively the most powerful tool. And by having its own sandbox, it can go to the Internet, it can download documents, it can learn things it needs, it can parse documents, it can keep track of its own process. Not to anthropomorphize it, but like when you're working, you probably have a notepad next to your laptop, you have your own computer, you can go find information, you can keep notes about what it is, you're trying to accomplish maybe a to-do list and check it off. And we think in this manner, agents work a lot more like people and they really need their own computer to succeed. And that's the compute primitive we're really trying to bring to market.