What is an AI agent sandbox?
Updated October 24, 2026 .md
Short answer: An AI agent sandbox is an isolated computer where an agent can run code, install packages and use tools without touching your own machine, accounts or other users. A good one is a separate VM with its own kernel, holds only the secrets the task needs, limits what it can reach, and can be stopped, copied or thrown away.
Why agents need one#
A coding or computer-use agent does what a developer does: it runs shell commands, installs dependencies, starts servers and reads web pages. Two things make that risky on a machine you care about:
- Mistakes. A wrong
rm, a bad migration or a runaway process hits whatever the agent can reach. - Untrusted input. Instructions hidden in a web page, an issue or a file the agent reads can steer it (prompt injection). Then the agent acts with all the permissions it has.
A sandbox limits the damage to a machine that holds nothing else and that you can throw away.
What to look for#
| Property | Why it matters | In a Sandboxer sandbox |
|---|---|---|
| Isolation boundary | A shared kernel is a bigger attack surface than a hypervisor | Cloud Hypervisor microVM with its own kernel, one per sandbox |
| Secrets | The agent can only leak what's inside | --no-env starts with none of your secrets; add only what the task needs with -e |
| Network reach | Limits what a misled agent can contact | Can't reach other sandboxes directly or our internal networks; outbound internet is open by default |
| A full OS | Real tasks need packages, Docker, services | Ubuntu 24.04, root via sudo, systemd, Docker, /dev/kvm |
| Lifetime | Long tasks shouldn't die mid-way; idle ones shouldn't bill | No time limit on paid plans; stop saves memory and costs nothing |
| Disposable | Start clean, throw away after | Create in seconds, sbx delete when done, fork to try again |
| Observable | You need to see what it did | Command history, process list, streamed agent events |
Container, gVisor or microVM?#
| Approach | Boundary | Trade-off |
|---|---|---|
| Container (Docker) | Linux namespaces and cgroups on the shared host kernel | Light and fast; a kernel bug or a mounted Docker socket can break out |
| gVisor | A user-space kernel that intercepts system calls | Stronger than a plain container; some system calls and tools behave differently |
| MicroVM (Firecracker, Cloud Hypervisor) | Hardware virtualization; each sandbox has its own kernel | Strongest of the three; slightly more overhead per sandbox |
More detail: Container vs microVM for untrusted code.
Give an agent a sandbox in three commands#
curl -fsSL https://sandboxer.com/install | sh # sbx CLI; signs you in
sbx new --no-env -e ANTHROPIC_API_KEY="$ANTHROPIC_API_KEY" # a VM with only this key
sbx prompt sb_k3v9x2mq --provider claude-code "Clone the repo, run the tests, fix what fails"Or let your own agent create and use sandboxes as tools through the MCP server built into the CLI, sbx mcp. It gives Claude Code, Claude Desktop, Cursor, Codex or any MCP client tools to create sandboxes, run commands, move files, publish a port and stop the sandbox. See MCP server.
FAQ#
What is an AI sandbox?#
An isolated environment where AI-generated code or an AI agent can run without access to your own computer, data or credentials. It can be a container, a gVisor sandbox or a virtual machine; a VM with its own kernel gives the strongest isolation.
Is a Docker container a safe sandbox for an AI agent?#
It's better than running on your laptop, but containers share the host's kernel. For untrusted or agent-written code, a microVM with its own kernel is a stronger boundary. Never mount the host's Docker socket into an agent's container.
What should an agent sandbox not contain?#
Anything you wouldn't hand to the agent's worst input: your SSH keys, long-lived cloud credentials, production database access and personal browser sessions. Pass only short-lived, narrowly scoped keys for the task.
How long should a sandbox live?#
As long as the task needs. Short code-execution calls can use throwaway sandboxes. Coding agents often need hours or days; then you want a sandbox you can stop and resume without losing work.
How do I let my own agent create sandboxes?#
Use an API or an MCP server. With Sandboxer, sbx mcp adds sandbox tools to any MCP client, and the REST API at https://sandboxer.com/api/v1 works from any language.
More guides
For running AI-written code we rank Sandboxer, Freestyle and E2B first: each gives code its own VM with an API or SDK and can pause with memory. Pick Daytona for Windows or GPUs, Modal or Northflank for GPUs at scale, E2B or Northflank to run in your own cloud, and Vercel Sandbox if your app lives on Vercel.
For coding agents we rank Sandboxer first: a full Ubuntu VM with Claude Code and Codex preinstalled, Docker, and stop-and-resume that keeps the agent's session in memory. Freestyle and Amp Orbs also keep memory across pauses. Pick E2B for prebuilt agent templates on open source, Docker Sandboxes for free local runs, and Codespaces if you live in GitHub.
The best Daytona alternatives depend on why you're switching. For a full Linux microVM on every sandbox with memory-saving stop and Docker, we rank Sandboxer first. E2B suits open source and self-hosting, Modal massive short-job fleets, Northflank bring-your-own-cloud with GPUs, and Vercel Sandbox apps on Vercel.
If you're leaving E2B for longer sessions or a fuller machine, we rank Sandboxer first, then Freestyle: both give each sandbox a full Linux VM that keeps memory across pauses, with Docker and no short session cap. Pick Daytona for Windows or GPUs, Northflank for bring-your-own-cloud, and stay on E2B if you need open source.