---
title: "AI Agent Sandbox: Containers, MicroVMs and Wasm Compared"
url: https://syntaxradar.com/radar/sub-millisecond-sandboxes-docker-wasm-micro-vms-untrusted-ai-execution
author: "Abdul Rehman, Lead Systems Architect and Technical Director"
publishedAt: 2026-10-25T08:10:00.000Z
dateModified: 2026-10-29T10:33:58.900Z
category: "#DEV-SYSTEMS"
topic: "ai agent sandbox"
primarySource: https://github.com/firecracker-microvm/firecracker
summary: "AI agent sandbox options explained: how containers, Firecracker microVMs, gVisor and WebAssembly isolate AI-written code, and what each one costs you."
---


# AI Agent Sandbox: Containers, MicroVMs and Wasm Compared

**Short answer:** Docker says its Wasm workloads feature is deprecated, while Firecracker and gVisor remain the documented stronger boundaries for untrusted code.

**Takeaway:** Pick the lightest isolation wall that matches how untrusted the code is.

**Key figures (as reported by the source):**
- Wasm runtimes Docker Desktop installs: 7
- Firecracker default memory per microVM: 128 MiB
- AWS services running Firecracker: Lambda and Fargate

An AI agent sandbox is a walled-off space where code written by an AI runs without touching anything it should not. The sandboxing options that exist today are containers, lightweight virtual machines, a userspace kernel called gVisor, and WebAssembly.

This matters because AI agents now write and run programs on their own: a chart from a spreadsheet, a data clean-up script, a quick calculation. That code can have bugs, and an attacker can trick an agent into producing harmful code. The sandbox decides how far the damage can spread.

### What is an AI agent sandbox, and why do agents need one?

A sandbox is an isolated environment with strict limits on files, network access and memory. The program inside can do its work, but it cannot read your passwords, wipe your disk or reach other customers' data.

Agents need one because they act on text they did not write, and the sandbox is where that risk gets contained. A web page, an email or a document can contain instructions that steer the agent.

The design question is where to put the wall. A wall close to the program is cheap and fast but has more places to leak. A wall around a whole virtual computer is stronger but heavier.

### Are Docker containers safe enough for untrusted AI code?

A container packages a program with its files and runs it as an ordinary process on the host computer, fenced in by features of the operating system. Containers start quickly and are easy to manage. Their weakness is that every container on a machine shares one operating system core, called the kernel. The kernel is the part of the operating system that decides what every program may touch.

If code inside a container finds a bug in that shared kernel, it can reach the host and every neighbour. Security tools such as seccomp (a filter on which requests a program may make to the kernel), AppArmor and SELinux shrink the risk. The [gVisor documentation](https://gvisor.dev/docs/) describes these as rule-based mechanisms, and gVisor was built because rules alone leave the host kernel exposed to the application.

For code you wrote and trust, a container is a sensible choice. For code an AI produced after reading untrusted text, many teams want a stronger wall.

### How do microVMs and gVisor isolate untrusted code?

A microVM is a small virtual machine that has its own kernel and runs on hardware-enforced virtualization. [Firecracker](https://github.com/firecracker-microvm/firecracker), the best-known example, is open source and was built at Amazon Web Services for AWS Lambda and AWS Fargate. Its README says it leaves out unneeded devices to keep each microVM's memory use and attack surface small, and its default setup is 1 virtual CPU and 128 MiB of memory. Because a microVM carries its own kernel, code inside it talks to that private kernel and not the host’s, and a bug it finds there stays inside the small virtual machine.

The [paper that introduced it](https://www.usenix.org/conference/nsdi20/presentation/agache) describes the problem it solves: traditional virtualization gives strong security with high overhead, while containers give minimal overhead with weaker security. The authors say Firecracker is deployed in Lambda and Fargate, supporting millions of production workloads.

gVisor takes a middle path. According to its documentation, it is an application kernel written in Go that handles the program's system calls itself, so the host kernel is not directly exposed. It avoids the hardware layer that a full virtual machine needs. The documentation states the price: reduced application compatibility and higher per-system-call overhead, with some system calls and files not implemented.

![A server technician in a data centre aisle checking a row of racked machines](https://images.unsplash.com/photo-1563986768609-322da13575f3?w=1600&auto=format&fit=crop&q=85)

### Can WebAssembly work as an AI agent sandbox?

WebAssembly, or Wasm, is a compact instruction format that started in web browsers. A Wasm module runs inside a runtime that the [WebAssembly security documentation](https://webassembly.org/docs/security/) says separates it from the host using fault isolation techniques. Its memory is a separate region with a fixed maximum size, and every access outside that region is bounds-checked and ends in a trap, which stops execution.

A module also has to declare its functions up front, and it can reach the outside world only through the interfaces its host provides. That narrow interface is why Wasm is attractive for running untrusted snippets: a module can do only what the host chooses to hand it. If the host offers no file or network functions, the module has no way to ask for them.

The same page lists limits. Race conditions, timing side channels and some nondeterminism are not prevented by the design. Wasm is also a runtime boundary, and it suits code compiled to Wasm. The arbitrary Python packages and native tools many agents want to call do not fit.

### Did Docker Wasm replace microVMs?

No. [Docker's documentation](https://docs.docker.com/desktop/features/wasm/) states that Wasm workloads in Docker Desktop are deprecated and will be removed in a future release, and that the feature is no longer actively maintained. While it lasts, enabling it installs seven Wasm runtimes (including wasmtime, WasmEdge and Wasmer), and Wasm services can sit beside normal containers in one Compose stack. It also requires the containerd image store.

That setup is a convenience for developers testing Wasm services on a laptop, and the documentation makes no claim about replacing virtual machines or serving as an agent sandbox.

![A glass safe door standing open in a dim vault room](https://images.unsplash.com/photo-1550751827-4bd374c3f58b?w=1600&auto=format&fit=crop&q=85)

### Which sandbox should you pick, and what does it not cover?

Trusted internal scripts can run in a hardened container. Code generated from untrusted input for many customers on shared machines is where microVMs and gVisor are used. A secondary comparison by [SoftwareSeni](https://www.softwareseni.com/firecracker-gvisor-containers-and-webassembly-comparing-isolation-technologies-for-ai-agents/) places Firecracker or Kata Containers at the strongest end, gVisor in the middle and plain containers at the low-threat end, and names E2B (Firecracker) and Modal (gVisor) as users. That article is a vendor-style blog and gives its own figures.

A sandbox controls what code can reach. An agent that is permitted to send email or call an API can still be talked into misusing that permission, so network limits, narrow credentials and human approval for risky steps remain separate jobs. The same reasoning applies to agents that drive a whole computer, covered in our piece on [computer-use agents](/radar/computer-use-ai-agents-operating-systems-claude-operator), and to the control loops around them in [deterministic state graphs for agents](/radar/deterministic-state-graphs-preventing-infinite-loops-ai-agent-workflows).

A stronger wall usually costs memory and start-up time, which is why Wasm and containers stay popular for trusted or compiled code.

Firecracker's README says its minimal design decreases startup time and memory footprint, but Firecracker publishes no boot-time figure.

gVisor's documentation is more direct: it names higher per-system-call overhead, so programs that make many system calls can run poorly. It also says full virtual machines can struggle to reach high density and may need extra agents, larger footprints and slower start-up.

Wasm sits at the light end because there is no operating system to boot.

### What to watch next

One open question is whether Wasm runtimes grow beyond compiled code, since that is the main thing holding them back as agent sandboxes. Another is whether gVisor's compatibility gaps narrow, because its documentation names them as the main cost. A third is independent, published benchmarks that state hardware and method, which would replace the vendor figures now in circulation.

For a team choosing today, the practical step is to write down what the agent's code may read, write and reach, then pick the lightest isolation that covers that list.

## Frequently asked questions

### What is an AI agent sandbox?
An AI agent sandbox is an isolated environment where code written by an AI agent runs with strict limits on files, network access and memory. If the code is buggy or has been steered by an attacker, the damage stays inside the sandbox instead of reaching the host machine or other users' data.

### Is Docker enough to sandbox untrusted AI-generated code?
A standard container shares the host's kernel, so a kernel bug can let code escape to the host. Tools like seccomp and AppArmor reduce that risk but do not remove it. For code produced from untrusted input, many teams add a microVM or gVisor for a stronger boundary.

### What is the difference between Firecracker and gVisor?
Firecracker runs each workload in a small virtual machine with its own kernel, backed by hardware virtualization. gVisor is a userspace kernel that handles system calls itself without a virtual machine. According to its documentation, gVisor trades some application compatibility and per-call speed for lower fixed costs.

### Is Docker Wasm still supported?
According to Docker's documentation, Wasm workloads in Docker Desktop are deprecated, no longer actively maintained and will be removed in a future release. While available, they install seven Wasm runtimes and can run alongside regular containers. Docker does not present the feature as an agent sandbox.

## Source
Based on Firecracker, gVisor, WebAssembly and Docker documentation. https://github.com/firecracker-microvm/firecracker

---
Published on SyntaxRadar (https://syntaxradar.com). Canonical URL: https://syntaxradar.com/radar/sub-millisecond-sandboxes-docker-wasm-micro-vms-untrusted-ai-execution. Last modified 2026-10-29T10:33:58.900Z.