AI coding agents are becoming a regular part of development. They can write code, install dependencies, run tests, and execute commands with very little supervision.
But there's a catch: the more autonomy we give them, the more we need to think about what they can access.
Imagine asking an agent to fix a bug, only to realize that the commands it runs could affect more than the project you're working on. You don't want to babysit every action, but you don't want to give it unrestricted access either.
What if we could give AI agents their own workspace, with clear boundaries around what they can access?
That's where Docker Sandboxes comes in.
What are Docker Sandboxes?
Docker Sandboxes provides isolated environments for AI coding agents. Local sandboxes use microVMs to separate the agent's execution environment from the host.
Think of it as giving your AI agent its own development machine. It can work, install tools, and execute code within that environment, while network access and credentials can be managed through explicit controls.
The idea isn't to make agents less capable. It's to let them work autonomously without automatically trusting every action they take.
Getting started
First, install Docker Sandboxes by following the official getting-started guide.
Sign in and check the available commands:
sbx login
sbx --help
Once configured, start a supported coding agent:
sbx run --name my-agent claude
Replace claude with your preferred supported agent.
That's the basic idea: instead of running the agent directly in your usual environment, you launch it through the sandbox workflow.
The exact setup depends on your operating system, agent, and authentication configuration, so follow the current documentation for those prerequisites.
Control where your agent can go
Isolation isn't just about separating files. AI agents often need network access to download dependencies, fetch documentation, or communicate with model providers.
The challenge is allowing the connections they need without leaving every destination accessible by default.
Docker Sandboxes provides network policy presets and configurable rules. You can inspect the configured policies with:
sbx policy ls
For example, if your development workflow needs the npm registry, you can allow it explicitly:
sbx policy allow network registry.npmjs.org
This illustrates an important principle: give your agent access to the resources its task requires, rather than assuming it needs unrestricted network access.
The effective policy still needs to be checked, especially if additional tools or configurations introduce their own permissions.
Keep credentials under control
Your agent may need to authenticate with a model provider or an external service. That doesn't mean you should casually place API keys in project files or expose them through shell commands.
Docker Sandboxes supports credential-management mechanisms designed to reduce direct exposure of raw credentials to the agent.
A supported Anthropic credential can be configured using:
sbx secret set anthropic
The exact credential setup depends on the provider and your environment. Follow the official credential-management guide for the supported workflow.
The key idea is simple: an isolated environment is only part of the security story. The way you provide secrets matters, too.
What does sandboxing actually protect?
Docker Sandboxes helps establish boundaries, but it isn't a guarantee that every action is safe.
- Execution: The agent runs in an isolated environment.
- Network: Policies help control outbound connections.
- Credentials: Supported mechanisms can reduce unnecessary exposure of raw secrets.
- Permissions: Anything explicitly shared with the agent still needs careful consideration.
An agent can still make mistakes within its environment. Network rules can be misconfigured, and credentials can still be exposed if they're deliberately placed somewhere the agent can read.
Sandboxing works best as one layer of a broader security approach, not as a replacement for reviewing code and managing permissions.
Give agents freedom, not unlimited access
AI coding agents are most useful when they can work without constant supervision. But autonomy doesn't have to mean giving them access to everything on your machine.
Docker Sandboxes offers a practical way to separate agent execution from the host and manage access to networks and credentials.
The principle is straightforward: give agents enough freedom to get the job done, while keeping unnecessary access out of reach.
Because building better AI workflows isn't just about what agents can do. It's also about creating the right environment for them to do it safely.
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