Conductor Launches Parallel AI Agents in Vercel Sandbox Cloud

Conductor, a six-person startup, faced a problem common to many who build developer tools: how to scale compute without being tied to local hardware. Their solution for managing a fleet of AI coding agents required a cloud layer. They chose Vercel Sandbox, and now developers can run multiple agents in parallel and close their laptop.

The Challenge of Local Execution

Conductor was originally an application that a developer would run on their own laptop. With one or two agents, it worked perfectly—intuitively and quickly. But when teams wanted to scale—run more agents, give them more time to work in parallel on different branches—problems emerged. The fan would whir at maximum, the CPU would overheat, the battery would drain in twenty minutes. Most inconvenient: closing the laptop lid would stop the agents completely.

Engineers at Notion and Linear who adopted Conductor early quickly felt these constraints. They saw the tool's potential—each agent works on a separate branch of the codebase, and they could all run simultaneously. But they couldn't leave their laptop running all day. The demand for cloud execution came quickly and loudly.

Cloud Solves the Problem

The solution is called Cloud Workspaces, and it's built on Vercel Sandbox. The idea is simple: when a developer opens a new workspace, agents no longer start on their machine but spin up on a remote cloud server. Your laptop can be turned off. The interface remains the same—for the user it looks identical to the local version.

Users don't see a difference between the local and cloud version.

They simply open a workspace the way they always have and get to work. It's magic, because Vercel Sandbox is very fast.

The workflow has now changed:

• Open multiple workspaces with agents on different codebase branches in parallel
• Close your laptop and go to a meeting or head home
• Come back two hours later and see what the agents accomplished
• Discuss with the team: which changes should be merged to the main branch, which need to be redone

Why They Chose Vercel

For a young startup, transitioning from the local world to the cloud is a big decision. The early users work at major companies: Linear, Ramp, Notion. They want to know who you're trusting with their code and data. It's not just a technical choice of provider—it's a conversation about reliability for the long term.

Conductor evaluated several sandbox providers. They chose based on criteria: cold start speed, snapshot support, long-term reliability, quality of technical support, and developer experience for their team. Vercel won on all fronts.

Charlie Holtz, CEO of Conductor, worked at Vercel before, so he knows what he's talking about: "There are so many cloud sandbox providers on the market. We needed one that works fast and reliably, and one that can give us serious technical support. Vercel checked all those boxes and gave us genuinely good developer experience."

Model Agnosticism

Conductor deliberately didn't lock itself into one model or framework. It currently supports Claude Code, Codex, and other coding agents. This is a strategic choice for the future: every three months something new happens in AI, and Conductor's cloud layer remains universal. If tomorrow the perfect new coding tool appears, Conductor can add it without rebuilding the entire infrastructure.

What This Means

Parallel AI agents are finally moving from labs to production cloud. This is no longer an idea from a presentation—it's a real tool that engineers at Notion, Linear, Ramp, and Life360 use every day. For developers, it means one thing: sleep soundly. Your agents will work while you drink coffee, go to meetings, or take time off. Local hardware no longer limits your scale.