We have all watched a coding agent write perfect Python in 10 seconds, only to wait 3 minutes for the CI/CD pipeline to boot up a VM to test it.
The intelligence is fast. The infrastructure is slow.
By 2026, the problem with autonomous software engineers is no longer the LLM context window. The problem is execution. When an agent writes code, it needs a place to compile, run, crash, and debug it. It needs a sandbox. And standard cloud infrastructure simply was not built to support the rapid iterative loops of AI.
This is the Sandbox Bottleneck.
The Sandbox Problem
When an AI agent (like OpenHands or Devin) suggests a fix to a pull request, it requires validation.
If you give an agent root access to your primary servers, it will eventually execute rm -rf / or drop a production database. If you use traditional VMs like AWS EC2, the boot times (minutes) will destroy the agent’s context window. If you try to run multiple agents locally, you will melt your MacBook.
Agents need ephemeral, perfectly isolated environments that boot instantly. They need a sandbox where they can build an entire full-stack application (frontend, backend, database), test it, and then have the whole thing evaporate without a trace.
If an agent alters the file system during a failed experiment, the next attempt must start from a clean slate. Standard VMs do not wipe cleanly without slow re-provisioning.
Infrastructure for Agents
This is why we built PrevHQ. We realized that agents do not need a better prompt; they need a faster execution environment.
PrevHQ is essentially the “Vercel Preview for Backend/AI.” It is infrastructure built explicitly for agents. When OpenHands needs to test a feature, it requests a sandbox via our API.
Instead of waiting for a heavy VM, PrevHQ provides an instant, stateful preview container. The agent pushes code, the environment builds immediately, and the agent receives a public URL to verify its work. If the code breaks, the sandbox is destroyed, and a fresh one is provisioned instantly.
We do not just provide compute. We provide disposability.
Engineering Porn: The Dreadnought Pipeline
To make this work for AI agents, we had to rethink how containers boot. We call our architecture the “Dreadnought pipeline.”
Traditional container registries pull layers sequentially over HTTP. This is too slow for an agent waiting for feedback.
With Dreadnought, we pre-warm hypervisors with common base images (Node, Python, Postgres). When an agent requests a sandbox, we clone the file system snapshot using lightweight copy-on-write mechanisms.
This shaves 40 seconds off container boot times. An agent goes from generating code to executing it in milliseconds. The infrastructure gets out of the way of the intelligence.
The Vercel Preview for Backend/AI
You are no longer building apps; you are building the factories that build the apps.
To deploy an OpenHands secure sandbox, you must abandon the idea of static servers. You must treat infrastructure as a disposable commodity that is created and destroyed thousands of times a day.
PrevHQ gives your agents the speed they need to iterate and the isolation you need to sleep at night.
FAQ: Deploying OpenHands Sandboxes
How do I deploy OpenHands in a secure sandbox?
To deploy OpenHands in a secure sandbox, use an ephemeral container platform like PrevHQ. The platform provisions isolated environments that boot instantly, allowing the agent to execute untrusted code without accessing your host network or persistent data.
What is the best infrastructure for AI agents?
The best infrastructure for AI agents provides instant, ephemeral execution environments. Standard VMs boot too slowly. Platforms that offer stateful, disposable sandboxes enable the rapid iterative loops that autonomous coding agents require.
How do you prevent an AI agent from escaping its sandbox?
You prevent an agent from escaping by enforcing strict network isolation and using secure hypervisors. The sandbox must be fully containerized without access to internal VPCs or long-lived credentials, ensuring that any malicious or accidental code execution is contained.
Why is traditional CI/CD too slow for autonomous agents?
Traditional CI/CD pipelines are designed for human commit cycles, often taking minutes to provision runners. Autonomous agents generate code in seconds and require immediate execution feedback to fix errors, making slow boot times a critical bottleneck.