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To sandbox an AI agent in production, run its model-directed commands and file operations in isolated execution compute, separate from the trusted harness that manages model calls, tool routing, approvals, logs, and recovery. Deny outbound network access by default, expose only the workspace and services the task needs, and keep broad application credentials outside the execution environment. A sandbox limits what agent-run code can reach; it does not replace policy, human review, or monitoring.
What does an AI agent sandbox protect?
An agent can translate instructions into shell commands, scripts, file changes, package installs, and service calls. Those actions may be useful, but model-directed code should be treated as capable of doing anything available to its process: reading accessible files and credentials, using permitted network routes, and changing writable data.
OpenAI’s sandbox security documentation cautions that agent-generated code can access the files, credentials, and network available to its environment. That makes the execution boundary important, but it is only one part of a production security design.
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The harness is the trusted control plane: it runs the agent loop, calls the model, routes tools, records run state, enforces approvals, and handles recovery. The execution environment performs model-directed work such as running commands and modifying files. OpenAI’s Agents SDK documentation describes this as the boundary between the harness and compute.
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Keep sensitive control-plane responsibilities in infrastructure that the agent’s commands cannot freely modify. Give the worker only the inputs, permissions, and outputs required for its task. If the worker is compromised or behaves unexpectedly, this separation helps prevent it from taking over the orchestration layer.
Which execution environment should you choose?
Choose based on the threat model, not on a product label such as “sandbox” or “local.” Check what actually restricts filesystem access, processes, host resources, and network traffic, and whether those restrictions are enforced outside the agent’s instructions.
| Approach | What the cited documentation establishes | Practical implication |
|---|---|---|
| Agents SDK Unix-local Linux backend | OpenAI’s Agents SDK guide says it runs host processes without OS-level confinement. | Do not treat this backend as a security boundary for untrusted commands. Add external isolation or use a suitably configured alternative. |
| Agents SDK macOS backend | OpenAI’s Agents SDK guide says its filesystem restrictions do not provide network isolation. | Filesystem restrictions alone do not contain network access; evaluate and enforce egress separately. |
| Configured Docker or hosted compute | OpenAI’s SDK guide identifies these as options for untrusted commands. The sources do not establish one universal configuration or security level. | Inspect the actual runtime settings, mounts, network policy, and credential flow rather than assuming the product name guarantees isolation. |
| gVisor-based reference design | Anthropic’s reference harness uses gVisor for a syscall and filesystem boundary, alongside Docker networking and an allowlist proxy. | Use it as an example of layered controls, not as a guarantee that every deployment has the same boundary or operational properties. |
The cited documentation does not provide a common benchmark that establishes one approach as categorically safest or fastest. Your choice depends on the commands the agent can run, the value of accessible data, the services it must reach, and your ability to operate the isolation layer.
How do you design the boundary before deployment?
- Inventory what the task needs. List the files and data it may read or write, the commands and tools it can use, required external services, and effects that need a person to approve them.
- Define the workspace contract. Specify which paths are mounted, which are writable, what persists after a run, and how workspaces are separated between users or workloads. Avoid broad host mounts by default.
- Place control-plane functions outside the worker. Keep model calls, tool routing, approval decisions, run state, and recovery in the trusted harness where possible. Give the execution environment only the authority required to perform its assigned work.
- Choose isolation for the inputs you accept. If commands or code may be influenced by untrusted input, use a configured isolation layer rather than relying on instructions to the agent or a local backend that does not provide OS-level confinement.
- Write down what must be reviewed. Identify consequential actions—such as publishing, deleting, or changing production data—that should pause for approval before execution.
How should you control network access and service calls?
Treat outbound network access as a separate boundary from filesystem isolation. Start with traffic denied and allow only destinations needed for the workflow. For each tool, establish where its connection originates: an executor-side MCP may connect from your infrastructure, while a remote MCP endpoint must be reachable from the remote service.
Allow only required destinations
Use network controls that enforce a narrow egress policy, such as a proxy allowlist or a network without a default internet route paired with an allowlist proxy. The Anthropic reference harness illustrates this pattern; its exact behavior depends on that implementation’s platform, proxy configuration, and maintained allowlist. A proxy policy change can also interrupt existing connections, so test policy updates against running work.
Broker third-party access
When a task needs an external service, prefer a trusted proxy or function tool that supplies scoped access only to the approved destination. This lets the application retain control over credentials and requests rather than exposing a broad service key to arbitrary agent-run code. Confirm that the service connection is permitted from the component that actually makes it.
How do you keep credentials out of reach?
Do not put broad application API keys or third-party secrets in files, environment variables, or mounted paths that agent-run code can read. A restricted executor key may be necessary to connect a worker, but if it is visible inside that worker, code running there can read it. Narrow permission scope reduces potential damage; it does not make a credential secret from the sandbox.
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- Store long-lived credentials in a secrets manager outside the execution environment.
- Broker access through a trusted proxy or function tool when the agent needs a secret-backed service.
- Scope credentials to the required task, destination, and permissions; avoid reusable broad application keys.
- Test that the worker cannot read secrets or reach cloud metadata endpoints that could expose credentials.
What should approvals and audit logs cover?
The sandbox provides the technical execution boundary; approval policy decides when an action that crosses a boundary must stop for review. OpenAI’s published account of its Codex deployment describes these as complementary controls: the sandbox governs where Codex can write and reach, while approvals govern actions that require authorization.
Record enough context to reconstruct both what happened and why it was allowed. Useful agent-aware events include the prompt, tool approval decisions, tool execution results, MCP usage, and network proxy allow or deny decisions. Conventional process and network logs remain useful for observed activity; agent-aware events add the intent and policy context needed to investigate a run.
How do you verify the deployed sandbox?
Test the actual worker configuration, not just the design document or product defaults. Run checks from inside the workload and inspect the wrapper, startup command, flags, mounts, and network rules that are active in production.
- Filesystem: Confirm that only intended paths are visible and writable, and that workspace persistence matches the contract.
- Network: Verify required destinations work, prohibited destinations remain blocked, and secrets or metadata endpoints cannot be reached.
- Identity and state: Confirm that users and workloads cannot see one another’s files, credentials, or persisted state.
- Recovery: Test what happens when a run is interrupted, a policy blocks a request, or an execution environment must be discarded.
- Approvals: Attempt actions that should require review and confirm they pause before taking effect.
- Wrapper defaults: Inspect the exact command and configuration used to launch the agent. Docker’s Codex sandbox documentation describes a default startup command that bypasses approvals and sandboxing, so a wrapper must not be assumed to preserve the agent’s own safety controls.
Product documentation can change, and product behavior can differ by platform, version, or local versus hosted execution. Verify the settings you deploy—including secrets flow and network policy—at implementation time.
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