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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesRun an AI agent’s shell commands and file operations inside an execution environment configured for the task—not directly on a trusted host by default. Choose a local container, a VM or microVM, or a hosted sandbox according to the files, network access, credentials, and persistence the agent needs. A “sandbox” is an execution boundary, not a guarantee that the entire agent system is safe: tools running outside it and files brought back to the host can still create risk.
What a sandbox does—and what it does not
A sandbox limits the resources available to code running inside it. For an agent, those resources include files, credentials, network connections, installed tools, and sometimes persistent storage. OpenAI’s Sandbox security documentation puts the central issue plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” The practical security boundary is therefore what the environment actually exposes, not what the agent says it intends to do.
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A working directory is not, by itself, an isolation boundary. The OpenAI Agents SDK documentation says Unix-local commands run as host processes on Linux and do not have Linux OS-level confinement. Setting a working directory does not prevent such a process from accessing other files available to the host user. The documentation also notes that macOS filesystem restrictions do not provide network isolation or the same boundary as a container.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Nor does isolating one process automatically isolate the surrounding agent system. The harness may hold API credentials or control human approval and recovery; MCP servers or other helper tools may run on the host; and generated files may later be built, opened, committed, or executed outside the sandbox. Map where each component runs before deciding what “isolated” means for your setup.
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Which execution environment should you choose?
There is no documented universal winner or directly comparable benchmark across local processes, containers, VMs, and hosted sandboxes. Match the boundary to the consequences of a mistake and the sensitivity of the data exposed.
| Approach | Useful when | Boundary and trade-off |
|---|---|---|
| Local process or Unix-local SDK client | The work is trusted, or another isolation layer already contains it. | On Linux, Unix-local SDK commands run as host processes without OS-level confinement; a chosen working directory does not restrict access. macOS filesystem restrictions are not network isolation and are not equivalent to a container boundary. (OpenAI Agents SDK documentation.) |
| Docker container client | You want local execution with a chosen image and a container boundary. | Mounts, credentials, and network access still need deliberate configuration. The SDK’s Docker client supports network_mode="none" to disable networking; that also prevents exposed ports. A container’s effective boundary depends on how it is configured. (OpenAI Agents SDK documentation.) |
| Docker Sandboxes | You want a local coding-agent workflow in a dedicated sandbox environment with a private Docker Engine. | A direct workspace mount shares host files and permits writes to them. Clone mode prevents writes to the host repository through that mount, but repository contents remain readable. Local MCP servers run outside the VM and may use host permissions. (Docker’s Isolation layers documentation.) |
| Hosted sandbox | You prefer managed execution or provider features such as snapshots and storage. | Controls and limits depend on the provider. OpenAI-hosted sandbox configuration supports enabled, disabled, and restricted network modes; restricted mode requires explicitly listing permitted hosts. Review persistence, credentials, filesystem behavior, and networking for the specific service. (OpenAI Agents SDK and API documentation.) |
| Self-hosted environment or VM | You need control over the image, compute, private networking, or infrastructure. | You own the security configuration, network policy, credential brokerage, updates, and lifecycle. A VM or microVM can provide a distinct kernel boundary, but the operator must configure and maintain the surrounding controls. (OpenAI Sandbox security.) |
Use local execution only for trusted work or when another boundary contains it. For untrusted commands, choose a configured container, hosted sandbox, or externally isolated host. Use a VM or microVM when the workload’s risk and requirements justify that distinct kernel boundary; the available documentation does not establish that one option is categorically safest or fastest for every workload.
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Configure the boundary in this order
- Keep orchestration in trusted infrastructure where practical. Let the sandbox handle the provider-specific commands and files while the harness retains authentication, billing, audit, human review, and recovery state. Running the harness inside the sandbox may be convenient for a prototype, but it puts orchestration and model-directed execution in the same boundary. (OpenAI Agents SDK documentation.)
- Select the compute boundary. Use local execution for trusted work or when an external isolation layer contains it. Choose a configured Docker or hosted environment for untrusted commands; use a VM or microVM-backed environment when the workload warrants that boundary. (OpenAI Agents SDK and Sandbox security documentation.)
- Expose the smallest useful workspace. Run without a host mount if the agent does not need host files. Use a direct mount when immediate shared edits matter and you accept that the agent can read and write the shared files. Use a private clone when protecting host repository integrity matters, and move secrets out of the repository: clone mode blocks writes through the mount, not reads. (Docker’s Isolation layers documentation.)
- Set network access deliberately. Disable egress if the task does not require it. Otherwise, allow only necessary destinations and account for redirects, DNS, proxies, MCP connections, and pathways to host services. OpenAI-hosted sandboxes offer disabled or restricted outbound access; Docker Sandboxes route outbound TCP through policy enforcement. (OpenAI API and Docker documentation.)
- Keep long-lived secrets out of the agent-visible environment. Prefer a trusted broker or vault-backed proxy for third-party credentials. Do not bake long-lived application keys into images, source, or logs. A real secret injected as an environment variable is available to code running in that environment and can be read by agent-generated code. (OpenAI Sandbox security and API documentation.)
- Review outputs before using them on the host. Inspect diffs and generated artifacts, including build files, scripts, hooks, CI and IDE configuration, and agent settings that might run later. Docker advises treating sandbox-modified workspace files like a pull request from an untrusted contributor: review them before trusting them on the host. (Docker’s Isolation layers documentation.)
- Decide what persists and how to retrieve it. Establish what should survive a stop, resume, snapshot, or deletion and how outputs leave the environment. OpenAI’s SDK documentation distinguishes live sessions, snapshots, and mounted storage for continuity. (OpenAI Agents SDK documentation.)
Start a local coding-agent workflow with Docker Sandboxes
Docker’s tutorial describes this general path: install Docker Sandboxes for your operating system, sign in with sbx login, optionally import supported agent setup, authenticate the agent, then start it with sbx run <agent>. The tutorial’s integrations invoke agents in full-autonomy modes. The sandbox is the containment layer; it does not remove the need to configure workspace access, credentials, and network policy. Changes can be inspected in the working tree with ordinary tools such as git diff. (Docker’s Run your coding agent in a sandbox.)
Choose direct mount or clone mode
A direct mount makes the shared workspace immediately available for agent edits, but those edits affect the mounted host files. In clone mode, the agent works in a private clone and the repository is exposed read-only through the mount; retrieve its changes explicitly for review. This protects the host repository from writes through that mount, but does not hide its contents: ignored and untracked files such as .env may still be readable. Remove secrets from the repository or otherwise keep them outside the agent’s view. (Docker’s Isolation layers.)
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Check the processes outside the sandbox
Docker Sandboxes can use a Docker Engine separate from the host daemon, but do not assume every connected tool shares that boundary. Docker’s documentation says local MCP servers run outside the VM and may use host permissions. Identify where each MCP server, helper, credential proxy, and other tool executes, and what host access it has, before enabling the integration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply the same checks to a hosted sandbox or VM
A managed environment can reduce the need to operate compute yourself; a self-hosted VM or sandbox can give you more control over images, compute, and private networking. Neither choice removes the configuration work. For the particular service or VM, establish which files are mounted or persisted, whether the agent can reach the public internet or private services, how credentials are supplied, and what survives snapshots or deletion. Keep the harness and sensitive control-plane work outside the execution environment where practical, and broker credentials rather than exposing long-lived secrets directly.
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For any option, trace the complete path from the agent to its tools and outputs. A disabled sandbox network does not establish that a host-run helper or MCP connection is also isolated; a private clone does not establish that repository contents are secret; and a container boundary does not make files safe once they return to the host. Configure only the access the task needs and review output before relying on it.
Product details above reflect the official OpenAI and Docker documentation accessed on October 4, 2026. Configuration labels, supported integrations, and product behavior can change, so confirm the current vendor documentation before implementing a specific setup.
Quick Recap
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