An AI agent’s “office” is a bounded workspace where it can inspect files and, when permitted, run tools without gaining unrestricted access to the machine or account around it. Making that workspace read-only is useful when an agent needs to analyze material but should not alter it—but the restriction must be enforced by the execution environment, not just requested in a prompt.
The exact framework and configuration behind this first-person title are not specified, so this is a practical design guide rather than a claim about a particular setup.
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What an agent’s office actually provides
A workspace can be more than text placed in a prompt. Depending on the sandbox, it may include files, shell commands, installed packages, mounted data, network ports for previews, snapshots, and state that can be resumed later. OpenAI’s Sandbox Agents guide describes this kind of environment for work involving directories, commands, generated artifacts, previews, or resumable tasks. A brief response that needs no files or persistent state may not need a sandbox at all.
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The useful idea is an explicit boundary: give the agent the workspace and capabilities needed for its task, while keeping the rest of the host and account out of reach wherever practical.
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Separate the trusted harness from sandbox work
A robust design can keep the trusted harness—the part that routes requests, invokes models and tools, obtains approvals, records activity, and handles recovery—outside the sandbox compute that runs model-directed commands and works with files. As OpenAI puts it, “The key split is the boundary between the harness and compute.” The guide presents this as an architectural option, not a requirement for every agent.
This separation helps avoid giving code inside the workspace control over the system that decides what work is allowed. It also gives the surrounding application a place to manage audit records, approvals, and resumable run state. Exactly which components belong in each part depends on the application; the important point is to avoid treating an agent’s working directory as the whole security system.
Make “read-only” real, not just a prompt instruction
A prompt such as “do not edit these files” expresses intent, but it does not prevent a command from writing to a path. A permission is meaningful only if the layer enforcing it covers the ways the agent can act.
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- Identify every route the agent can use to read or write: SDK file APIs, shell commands, mounted volumes, and any other tools.
- Apply write restrictions at the operating-system or sandbox boundary for each relevant route; do not infer shell protection from an SDK-level permission.
- Treat extra path grants in a workspace manifest as trusted configuration. The SDK guidance warns against loading those grants from model output.
- Test the restriction using the same tools the agent will receive, and verify that a denied write leaves the protected source unchanged.
Read-only access is appropriate for review or analysis of source material, but it may prevent workflows that need to save generated files beside that material. A practical design can keep input files read-only and provide a separate writable output location, with permissions set independently.
Protect credentials, network access, and untrusted content
Filesystem permissions alone do not make a sandbox safe. OpenAI warns that “Agent-generated code can access the files, credentials, and network available to its environment.” Anything mounted into the workspace, injected as an environment variable, or reachable over the network may therefore be available to code the agent runs.
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Limit outbound connections to the endpoints the task needs, and keep application credentials and third-party secrets outside the execution environment when feasible. If an agent needs a service, consider how access is brokered rather than placing broad, long-lived credentials in its workspace. OpenAI’s sandbox security guidance covers isolated compute, approved outbound endpoints, and credential separation.
A sandbox also does not eliminate prompt injection. Malicious instructions can arrive inside web pages, files, tool output, or other external content and try to steer the agent into chaining tools or exposing data. Google Cloud recommends least privilege, a separate identity for the agent, treating user-provided and database-derived text as data rather than instructions, and isolating memory and state across users, tenants, or agents. Its AI security and safety guidance notes that human review helps but is imperfect: “Human oversight reduces risk, but it is still vulnerable to human error in approving agent suggestions.”
- Give the agent only the files, tools, identity, and network access its task requires.
- Keep one user’s or task’s files, memory, and state separate from another’s.
- Do not treat approval as a substitute for inspecting what an action will do, especially when it comes from content the agent was asked to read.
- Assume untrusted inputs may contain instructions aimed at the agent; preserve the distinction between content to analyze and instructions authorized to control the system.
Questions to settle before choosing a sandbox
“Read-only” and “sandboxed” are not single, universal settings. Before relying on a workspace boundary, establish how the chosen system behaves:
- Do filesystem restrictions cover shell commands as well as the provider’s file API?
- Are tasks, users, and agents isolated from one another?
- Can outbound network traffic be restricted to approved destinations?
- Where do credentials live, and how does the agent obtain any access it needs?
- What workspace state persists, how is it resumed, and how can it be recovered or discarded?
- What does a human reviewer see before approving an action?
- How are mounted files and other untrusted inputs handled?
These questions turn an “office” from a metaphor into a set of testable boundaries. The right configuration depends on the task and the sandbox provider; the word “read-only” by itself does not establish what commands can change.
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