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An AI agent’s “office” is a bounded workspace where it can inspect files and, when allowed, run commands or create work products. Making that workspace read-only is useful only when the restriction is enforced by the execution environment—not merely requested in the prompt. The title does not identify a specific agent framework or setup, so this guide explains the design choices without claiming a particular implementation.
What an agent’s “office” actually is
A workspace can be much more than text placed in a prompt. Depending on the sandbox, it can include files, shell commands, installed packages, mounted data, network ports, snapshots, and state that can be resumed later. That makes it useful for work involving multiple files, generated artifacts, previews, or ongoing tasks; a short answer that needs no persistent files may not need one.
The important design idea is to separate the trusted system that manages the agent from the environment where model-directed work runs. OpenAI’s Sandbox Agents guide describes this as “the boundary between the harness and compute.” The harness can handle orchestration, model calls, tool routing, approvals, tracing, audit logs, and recovery. The sandbox can provide the files and commands the agent needs. This is one possible architecture, not a requirement for every agent.
What read-only means—and what it does not
Read-only is a permission enforced by a particular layer. A prompt such as “do not change these files” expresses intent; it does not prevent an agent from using an available command or tool to change them. A sandbox or operating-system restriction can enforce permissions, but its reach depends on the backend and on every way the agent can access files.
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SDK file access versus shell access
The OpenAI Agents SDK Python documentation explains an important distinction: a read-only path grant prevents writes through the SDK’s file API, but on Unix-local Linux it does not constrain arbitrary shell commands. A script run in the shell may therefore have access that the SDK file API itself would deny. The documentation recommends using an external isolation layer, such as Docker bind mounts, when shell commands also need to be restricted. See the Python sandbox guide for the relevant permission behavior.
In practice, check the boundary rather than relying on a setting’s name. Confirm whether restrictions cover SDK file operations, shell commands, mounted directories, and any other execution path exposed to the agent. Treat extra path grants as trusted configuration: the guide cautions against loading them from model output.
How to design a safer workspace
1. Give the agent only the files it needs
Keep the workspace narrow. Provide only the inputs required for the task, and avoid mounting a broad home directory, shared drive, or unrelated project tree. Separate workspaces by user, task, or agent when their data should not mix. Google Cloud recommends least privilege, a distinct agent identity, and isolation of memory and state across users, tenants, or agents in its AI security and safety guidance.
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For a genuinely read-only input, enforce the restriction at the filesystem or sandbox boundary, including for shell access. If the task requires creating files, make a separate writable output area rather than granting write access to the source material. Verify that the chosen backend applies its controls to all execution paths the agent can use.
3. Keep credentials out of the workspace where possible
Code the agent generates can access whatever files, credentials, and network the environment exposes. OpenAI’s Sandbox security guidance puts the point plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” Avoid placing application credentials or third-party secrets in the workspace. If a task needs an authenticated service, prefer a controlled broker or narrowly scoped access over exposing a reusable secret directly to agent code.
4. Limit outbound network access
A read-only filesystem does not stop information leaving over the network. Restrict outbound connections to approved endpoints where feasible, and consider whether the task needs network access at all. File permissions, network controls, and credential handling address different risks; one does not replace the others.
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5. Treat external content as data, not authority
Prompt injection can arrive inside web pages, documents, tool output, or other content an agent is asked to process. Malicious text may try to redirect the agent into chaining tools or exposing data. Google Cloud advises treating user-provided and database-derived text as data rather than instructions. Least privilege, isolated state, and careful handling reduce exposure, but do not guarantee that an agent will resist every malicious instruction.
6. Make review specific, not ceremonial
Human approval can help catch consequential actions, but it is not a technical permission boundary and is not infallible. Google Cloud warns that “Human oversight reduces risk, but it is still vulnerable to human error in approving agent suggestions.” A reviewer should see what the agent intends to do and what data or systems the action affects; approvals should not become a routine click-through step.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to check before choosing a sandbox
“Sandbox” and “read-only” can describe different controls. Evaluate the actual execution paths and operating model rather than comparing labels alone.
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- Filesystem enforcement: Do restrictions apply to both file APIs and shell commands?
- Isolation: Are workspaces and persistent state separated between users, tasks, and agents?
- Network: Can outbound traffic be limited to approved destinations?
- Credentials: Where do secrets live, and how are they made available to a task?
- Persistence and recovery: What survives between runs, and can the system inspect or restore prior state?
- Human review: What action triggers approval, and what context does the reviewer see?
- Untrusted inputs: How are mounted files and external content isolated from instructions and other users’ data?
OpenAI’s sandbox documentation describes both the workspace capabilities and the security considerations; its controls should be understood in the context of the specific backend being used. A tool-level permission is not a complete sandbox if another execution path can bypass it.
Does a sandbox stop prompt injection?
No. A sandbox can limit the damage an agent can do by restricting its files, credentials, network, and access to other users’ state. It does not make malicious instructions inside content harmless, nor does it guarantee that the model will interpret that content correctly. The practical goal is to reduce what a compromised or misdirected agent can reach, while monitoring and reviewing actions that matter.
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