AI Sandbox: How It Works, Security Boundaries, and Agent Use Cases

An AI sandbox is an isolated execution environment where an AI agent can run code, use tools, and work with files under defined resource and access limits. It separates the agent's execution from the application or machine that manages it. Its protection depends on the runtime boundary, configuration, and permissions you grant.

For developers building coding agents, a useful sandbox provides a shell, a filesystem, an execution API, and a lifecycle you can control. Some environments exist for one code snippet; others retain an agent's tools and work across many tasks.

This guide covers code-execution sandboxes for AI agents. University AI playgrounds, model evaluation environments, and regulatory sandboxes use the same phrase for different purposes.

Already choosing a provider? Start with the AI sandbox platform comparison. For Sandbox0's API, use the quickstart.

What Is an AI Sandbox Used For?#

An agent needs a sandbox when it executes code you cannot fully trust in advance. Typical workloads include:

• Coding agents: clone a repository, edit code, install dependencies, and run tests.
• Code interpreters: execute generated Python or JavaScript against user-provided files.
• Application builders: install packages, build an application, and start a preview server.
• Evaluation workers: run many independent attempts against the same starting environment.
• Personal agents: return to a user's files and installed tools over multiple sessions.

The workload determines the environment. A short calculation may need a disposable interpreter. A coding agent that accumulates repositories and toolchains needs a more complete system and a retention policy. The ephemeral, long-running, and hybrid deployment guide explains that lifecycle decision separately from where the agent harness runs.

How an AI Agent Sandbox Works#

A typical application keeps its model credentials and control logic outside the sandbox. It creates an environment through an API, sends commands or files, collects results, and decides when to pause, snapshot, resume, or delete it.

text
Application / agent harness
    │ scoped execution and file API
    ▼
Isolated runtime ── filesystem + installed tools
    │ permitted outbound requests
    ▼
Approved services

There are three separate design decisions:

A runtime can have a strong execution boundary and still be given an overly powerful API key. A persistent environment can retain files while starting fresh processes. Evaluate each decision directly.

Containers, gVisor, MicroVMs, and Restricted Runtimes#

These are architectural options, not a universal performance ranking. Docker's security documentation explains container controls and daemon exposure. The gVisor architecture documentation describes its application-kernel approach and compatibility tradeoffs. Firecracker is one microVM implementation.

Sandbox0 uses gVisor for its Linux execution boundary. Its Nomad architecture article explains how execution and durable RootFS storage fit together.

Security Boundaries Beyond Code Isolation#

Start with the smallest set of capabilities the task needs:

• Keep platform tokens and broad provider keys in the trusted application. Give agent tools narrowly scoped access to external services.
• Restrict outbound destinations and tool operations rather than assuming an isolated process cannot send data away.
• Set memory, CPU, execution, and retention limits. Decide what a runaway or abandoned task may consume.
• Avoid exposing the host Docker socket or host filesystem to untrusted code. If the task needs Docker, evaluate a Docker-in-sandbox setup.
• Record operations and make cleanup and recovery explicit.

A sandbox cannot make authorized access harmless: code given production credentials can still make production requests. Read the credential protection guide and MCP tool access control guide for two different parts of that boundary.

Run Code Through an AI Sandbox API#

For a small Sandbox0 example, install the Python SDK and set SANDBOX0_TOKEN in your trusted application environment:

bash
pip install "sandbox0>=0.10.5"

python
import os
from sandbox0 import Client

client = Client(token=os.environ["SANDBOX0_TOKEN"])

# This context creates a disposable sandbox and deletes it on exit.
with client.sandboxes.open("default") as sandbox:
    result = sandbox.run("python", "print(sum([2, 3, 5]))")
    print(result.output_raw)

This example executes Python inside the sandbox; it does not give the guest your host application's token. The execution API documentation covers command execution, supervised processes, and terminal sessions. Use reusable templates when many sandboxes need the same prepared tools.

What Does a Persistent AI Sandbox Keep?#

Ask which state is retained, rather than accepting a single “persistent” label.

Sandbox0 keeps the committed writable RootFS across successful filesystem-only pause and resume, including installed tools, configuration, and user files. Default resume starts fresh processes. Experimental process-state retention requires compatible memory: true pause and resume; it is a separate choice.

Runtime-only mounts such as /tmp, /proc, /sys, /dev, managed credentials, and template tmpfs mounts are outside offline RootFS access. Deleting a sandbox removes its current filesystem; retention is not an independent backup. See the pause and resume contract and snapshot documentation.

A Reproducible Filesystem Persistence Check#

The following is a verification recipe, not a latency benchmark. Use a disposable sandbox and a template that permits writes to the listed system paths. It checks file retention across a successful filesystem-only pause, reads and edits while compute is stopped, and the resulting files after resume.

python
import os
from sandbox0 import Client

client = Client(token=os.environ["SANDBOX0_TOKEN"])
sandbox = client.sandboxes.claim("default")
markers = {
    "/workspace/sandbox0-check.txt": b"project state",
    "/usr/local/share/sandbox0-check/marker.txt": b"tool state",
    "/etc/sandbox0-check.txt": b"system configuration",
}

try:
    for path, content in markers.items():
        sandbox.write_file(path, content)

    client.sandboxes.pause_and_wait(sandbox.id, timeout_sec=120)

    # Reuse this sandbox handle: these file calls do not wake guest compute.
    for path, expected in markers.items():
        assert sandbox.read_file(path) == expected
    sandbox.write_file("/workspace/next-task.txt", b"prepared while paused")

    client.sandboxes.resume_and_wait(sandbox.id, timeout_sec=120)
    for path, expected in markers.items():
        assert sandbox.read_file(path) == expected
    assert sandbox.read_file("/workspace/next-task.txt") == b"prepared while paused"
finally:
    # This sandbox was created solely for this check.
    client.sandboxes.delete(sandbox.id)

The file API still uses platform storage services while guest compute is stopped. Memory-paused sandboxes allow offline reads but reject mutations with 409 conflict to protect retained process state. If a mutation response is lost, inspect the resulting file before retrying. The Files API documentation covers those boundaries.

For environments that accumulate more data, automatic online RootFS growth is a separate capability from persistence. It applies to growth-enabled imports and templates; captured images and snapshots keep their geometry. The sparse device ceiling of up to 64 TiB is logical capacity, not preallocated storage or a populated-workload benchmark. Explore persistent AI sandbox lifecycle and storage for the operating model.

How to Choose an AI Sandbox Platform#

Choose against a representative task, then verify the result:

1. Run the actual language, package manager, test suite, and system tools your agent needs.
2. Check the execution boundary, credential handling, and outbound policy.
3. Identify which files and process state must survive between tasks.
4. Exercise pause, resume, timeout, and cleanup rather than testing only creation.
5. Check capacity, supported deployment options, and the complete bill.

Use the platform shortlist to start comparing Sandbox0, E2B, Daytona, Modal, Vercel Sandbox, and Cloudflare Sandboxes. Those products support different configurations; the linked official documentation and detailed reviews explain what to verify. If you want a self-managed runtime, evaluate that requirement explicitly: Sandbox0 Cloud is the supported Sandbox0 service, self-hosting is not supported, and BYOC is planned rather than available.

Compare the Complete Cost#

A useful cost model includes running compute, retained storage, plan fees or minimums, and any applicable transfer or request charges. Paused compute and retained storage are separate meters.

At Sandbox0's published rates on November 6, 2026, a sandbox configured with 4 GiB of memory running for 10 hours costs $0.60 in compute. Keeping 20 GiB of billable incremental data for one 730-hour storage month adds $0.40, for $1.00 in this example. Paused time adds no compute charge; storage remains billable. Running time includes idle time until the sandbox is paused. The shared base image is excluded from the 20 GiB example.

See the worked AI sandbox pricing breakdown and current prices for the full billing boundaries. This example is an arithmetic illustration, not a measured workload or a cross-provider price benchmark.

Frequently Asked Questions#

Is an AI Sandbox the Same as Docker?#

Docker is container tooling; an AI sandbox is the execution environment and its access and lifecycle contract. A platform can use Docker-compatible images with a different runtime boundary. Review the configured runtime, privileges, mounts, and network policy rather than relying on the container label.

Can an AI Sandbox Access the Internet?#

That depends on its network policy. Allow only the destinations and operations the task needs. Isolation does not itself determine which outbound requests are permitted.

Does Pause Preserve an Agent's Memory?#

Not automatically. Filesystem persistence and process-state retention are different capabilities. In Sandbox0, default pause/resume retains the committed filesystem and starts fresh processes; compatible experimental memory pause/resume is an explicit opt-in.

Is There a Free AI Sandbox?#

Free tiers and trial credits depend on the provider. Sandbox0 offers eligible new users $5 in usage credit after adding a credit card, subject to its eligibility rules. It is trial credit, not an unlimited free compute tier. Check the pricing page for current terms.

What Should I Build First?#

Start with one representative task and a disposable sandbox. Once code execution works, decide how to retain state, restrict tools and credentials, and recover between tasks. The Sandbox0 quickstart provides the next implementation step.