Vercel Sandbox now supports running Docker inside a sandbox. That is a meaningful shift for anyone building agents or test pipelines that depend on containerized services.
The core capability: an agent can install Docker, start the daemon, and serve a containerized application, all from within the sandbox. It can also build container images, install system packages, and modify files. None of that touches your host system.
This matters because agents often need real service dependencies to do useful work. Think Redis or Postgres as test dependencies. Previously, wiring those up safely required extra orchestration on your end. Now the sandbox handles the isolation layer, and the agent handles the rest.
The other practical use case called out in the source is validating container images before they go anywhere near production. You can let an agent build and probe an image inside the sandbox, confirm it behaves correctly, and only then promote it. The blast radius of a misconfigured image stays contained.
For product engineers building on top of AI agents, this removes a category of risk that has historically made containerized workflows awkward to automate. The agent gets real Docker, real system access inside the boundary, and zero ability to escape that boundary onto the host.
What to do with this today: If you have an agent workflow that currently spins up external services for integration tests, or one that needs to validate a Dockerfile before deployment, try routing that work through Vercel Sandbox. Install Docker, start the daemon, and let the agent do the container work in isolation. You get the full Docker toolchain without the cleanup cost or the host exposure.