How to choose a sandbox for code-running AI agents
An AI agent that writes and runs code should use a sandbox instead of running directly on the host machine. E2B, Daytona, , and gVisor all address the same basic need: giving the agent a disposable place where mistakes are contained. The practical decision starts with lifespan.
A fresh sandbox for every task is cleaner, but it may start more slowly; a warm pool can be faster, but old state may leak into later runs. State is another tradeoff. Some workflows benefit from installing once and reusing them, while others need a fully clean environment every time.
and network access also need limits. Full network isolation sounds safer, but it can block work such as pip install. s like E2B and Daytona are easier to start with, but they mean sending code execution to someone else’s .
Key points
- Code-running AI agents are safer when they run inside a sandbox instead of on the host machine.
- Fresh sandboxes are clean but can be slower to start; warm pools are faster but may carry old state.
- Reusing installed can save setup time, but it reduces the clean-room guarantee.
- Strict network limits improve safety, but they can block package installs such as pip install.
- s are easier to adopt, while gives more control over .