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“Which Settings Control AI Agent Autonomy, Human Review, and Action Limits?” The answer is several controls, not one master switch: the sandbox sets technical boundaries, approval policy determines when actions need review, network policy limits destinations, and administrator-enforced requirements can override local preferences. For OpenAI’s Agents API, the session’s review field controls automatic review of supported requests, but it does not set sandbox or network permissions. The right controls depend on whether you are configuring Codex or building an agent application with an API or SDK.
At a glance
| Control | What it limits or decides | Scope | Applies to |
|---|---|---|---|
| Sandbox / execution boundary | Where an agent can read or write, and whether it can reach the network | Execution environment | Codex; configure separately in API applications |
| Approval policy | Whether an action that needs approval pauses for a decision or is automatically reviewed | Codex policy or API session, depending on product | Codex and supported Agents API requests |
| Network policy | Which outbound destinations or web-search modes are allowed | Execution environment / managed policy | Codex configuration examples |
| Managed requirements | Which settings administrators require over local preferences | Organization or workspace | Managed Codex environments |
| Guardrails and human review | What an API application validates and which sensitive tool actions pause for a decision | Application-defined | Agents SDK / API applications |
The key distinction is between permission and review: a sandbox or network rule constrains what the agent can technically do, while review rules determine how certain proposed actions are handled. Neither should be treated as a substitute for the other.
A useful way to compare settings is to ask five questions: What does it technically permit? Does it trigger human or automatic review? Does it constrain files, network, tools, or broader workspace access? Is it local, session-level, or administrator-enforced? And does it apply to Codex, or must an API application implement it separately? These questions prevent a common configuration mistake: assuming that a review option also limits technical access, or that a workspace default is an enforced execution boundary.
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Which settings control what an agent can do?
For Codex, think in layers: sandbox boundaries establish the execution limits, approval policy governs requests that cross or challenge those limits, and network policy restricts outbound access. Administrators can add another layer by enforcing requirements that local preferences cannot override. OpenAI describes the combination of approvals and sandboxing as working together to control risk: “Approvals and sandboxing work together.” — OpenAI, Running Codex safely at OpenAI, May 8, 2026.
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OpenAI’s safety overview gives configuration examples including approvals_reviewer, sandbox_workspace_write.writable_roots, allowed_sandbox_modes, allowed_web_search_modes, and [experimental_network]. These names are examples in OpenAI’s managed-configuration context, not universal settings for every agent product or deployment. Check the documentation for the exact client and environment before copying a configuration. See OpenAI’s explanation of how Codex is run safely.
Sandbox: the technical boundary
A sandbox defines the agent’s execution boundary: for example, which paths it may write to, what remains protected, and whether network access is available. OpenAI’s safety example adds ~/development as a writable root. A writable root is a permission boundary, not an approval decision: it defines where writes are allowed rather than deciding whether a person must approve each permitted write.
When evaluating an agent setup, ask what the sandbox allows before asking how often it asks for approval. If a path or capability is outside the allowed boundary, the approval policy may determine whether a request to cross that boundary is stopped for review. But approval does not itself create a safe boundary; it is one part of the system for handling actions that require a decision.
In practice, distinguish an action inside the boundary from an attempt to cross it. A write under an allowed root is technically permitted by that boundary; a request for access beyond it raises a different question about how the configured approval flow handles the request. This is why broadening a writable root and changing an approval reviewer should be evaluated separately: one changes the area an agent can affect, while the other changes the handling of requests requiring approval.
Approval policy: when an action pauses
An approval policy decides whether a proposed action that needs authorization must stop for a decision, or can be handled through an automatic reviewer where supported. In Codex, OpenAI’s managed-configuration example uses approvals_reviewer = "auto_review". An action may be approved once or for a session, depending on the approval flow described by the product. A one-time approval applies to the action at hand; a session approval can permit relevant actions during that session, so readers should pay attention to the scope offered in the interface rather than treating all approvals as permanent.
Approval is not the same as restricting an action. It is a decision point applied to an action or request. A configuration that permits broad file or network access but relies only on review has a different risk profile from one that restricts those capabilities technically and also reviews exceptional requests.
Network policy: where requests can go
Network policy determines whether the agent can reach external destinations and which destinations or search modes are permitted. OpenAI’s Codex safety example discusses allowing cached web search and a managed policy that permits expected destinations while requiring approval for unfamiliar domains. This is a separate control from file-write boundaries and approval settings.
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Do not assume that granting or denying a writable path also grants or denies internet access. Likewise, the Agents API session review option described below does not change network restrictions. Network access should be configured in the environment or policy layer that actually governs the agent’s outbound connections. When a connection is denied or paused, check whether the cause is the network allowlist, the search mode, or an approval rule; changing file permissions will not resolve those network decisions.
What does review: "auto_review" do in the Agents API?
In an OpenAI Agents API session, review: "auto_review" enables automatic approval or denial of supported requests that would otherwise need approval. The setting applies to review handling; it does not configure the environment’s sandbox, grant file access, or change network rules.
The session field accepts "auto_review" or "none". If the field is omitted, it defaults to "none". When the session resumes, its review setting persists. Importantly, "none" means automatic review is disabled; those requests are not thereby routed to the application for a person to approve manually. Browser-origin and sign-in requests continue to use the application’s existing handlers. The specifics are documented in the Agents API session approval guide.
This distinction matters when designing an application: setting review: "none" is not equivalent to implementing a human approval queue. If the application needs a person to approve an action, its own workflow must intercept the action and pause execution accordingly. Also treat the setting as a session-level review choice, not a general policy for every capability in the environment: it neither defines which tools exist nor determines which files or network destinations those tools can reach.
When should actions get human review?
Use human review when a sensitive or consequential tool action needs a person or application policy to make the final decision. Examples include edits, shell commands, cancellations, and sensitive MCP calls in an API-built agent. Automated input, output, and tool checks can catch certain classes of problems, but they are distinct from a human deciding whether a specific side effect should proceed.
OpenAI’s Auto-review explainer describes the reviewer as assessing a proposed action in context, including its risks and relationship to the user’s intent. Its design intent includes blocking actions with serious or difficult-to-reverse consequences, such as exfiltrating data, exposing secrets, deleting data, weakening security settings, running untrusted code, or following conflicting instructions from untrusted content. That is a description of intended behavior, not a guarantee that every harmful action will be detected. Review is not a reason to remove technical boundaries or application-level safeguards.
That list points to practical review criteria: consider whether an operation exposes private information, changes or deletes important data, weakens a security control, executes code from an untrusted source, or acts on instructions that conflict with the user’s intent. The more consequential or difficult to reverse the side effect, the more important it is to define an explicit decision path. Automated review can be part of that path, but the organization still needs to decide which capabilities are technically available and what happens when a request is denied.
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For Codex, configure the approval and sandbox layers as complementary safeguards. For an API application, decide which tool calls can proceed automatically and which should pause, then implement that decision in the application’s tool or approval workflow. The Auto-review explanation discusses the intended role of automated review; the Agents SDK guide distinguishes guardrails from human review.
Do Codex settings carry over to API-built agents?
No. The Agents SDK and Responses API do not automatically inherit Codex Auto-review or Codex’s managed sandbox controls. An API application must provide its own enforcement path for tool permissions, automated checks, and any human approval workflow it needs.
The SDK guidance distinguishes guardrails—automated checks on inputs, outputs, or tool behavior—from human-in-the-loop review, which pauses an action so a person or policy can approve or reject it. A guardrail can validate a request or tool call; a review step can hold a consequential side effect until a decision is made. They may be combined, but one does not automatically provide the other.
For each tool, identify the side effects it can cause, decide which require a pause, and ensure the application does not continue the action before that decision. This is especially important for operations that change data or systems, such as edits, shell commands, cancellations, and sensitive MCP calls. Do not assume a Codex session setting enforces the same behavior in an API integration.
A sound implementation separates three tasks: validate inputs and outputs with automated checks where appropriate; constrain the tool’s capabilities to the access the application intends to provide; and pause sensitive side effects until an authorized person or policy approves them. The first task is a guardrail, the second is an execution or tool boundary, and the third is human review. Naming those responsibilities makes it easier to test them independently and to find a failure if an action proceeds unexpectedly.
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Can workspace administrators override local settings?
In managed workspaces, enforced requirements take precedence over workspace starting defaults, which in turn take precedence over a member’s local starting choices. A local preference can therefore be superseded by an administrator-enforced requirement.
Workspace administrators can set starting model, reasoning level, speed, Fast Mode availability, and new-chat behavior. These are defaults or access controls; they do not make a model available if the workspace does not have access to it, and they do not override enforced requirements. Cloud access, role access, and other capabilities also depend on the plan and workspace settings. These workspace choices should not be confused with sandbox and approval controls that determine what an agent may do during execution. See the Codex plan and workspace guidance for plan-specific details.
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For troubleshooting, separate a starting choice from an enforced rule. A starting model or reasoning level influences how a new chat begins; an enforced requirement is an administrative constraint that can take priority over that choice. Access to a feature is a further question: a setting cannot make an unavailable model, cloud capability, or role-protected feature available. If a preferred value seems to revert, check the workspace’s enforced requirements first, then its defaults and the member’s local selection.
How should you choose and combine the controls?
Start by identifying the product surface, then configure technical boundaries before relying on review behavior. This keeps the layers distinct and makes it easier to find the source of an unexpected permission or pause.
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- Define the technical boundary. Specify the writable paths and network reach that the environment should allow. In Codex managed configuration, consult the documentation for relevant settings such as writable roots, sandbox modes, and allowed web-search modes rather than assuming those keys apply everywhere.
- Choose the review behavior. Decide which requests should trigger approval, whether supported requests can use automatic review, and which actions must pause for a human or policy decision. Do not mistake disabling automatic review for creating a manual approval route.
- Check organization policy. In a managed workspace, confirm which settings are enforced and which are only starting defaults. If a local choice has no effect, an administrator requirement may take precedence.
- For API agents, implement the controls in the application. Add automated guardrails for input, output, or tool behavior as appropriate, and separately implement a human-in-the-loop pause for sensitive side effects. Codex controls do not automatically transfer.
- Review changes against the intended boundary. When a setting changes, check its effect on file access, network reach, approval routing, and administrative enforcement separately. A change in one layer should not be assumed to alter the others.
For a final check, test each layer with the behavior it is supposed to govern: verify the intended paths are writable, confirm network destinations and search modes, see whether a request requiring review is routed as expected, and confirm that a denied API tool action does not execute anyway. In managed deployments, verify both the configured local choice and any organization requirement that may supersede it. Testing these questions separately is more informative than treating a single successful or blocked action as proof that every control is working.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much does automatic review reduce human interruptions?
OpenAI reports that Codex sessions in its internal Auto-review deployment stopped for human approval roughly 200 times less often than sessions using manual approval. The same explainer says Auto-review approved around 99% of the small fraction of actions that needed review. These are figures from OpenAI’s internal deployment, not universal rates, guarantees, or forecasts for another environment.
The source explicitly notes that the ratios depend on the use case, environment, and sandbox configuration. It also gives an illustrative internal snapshot: in manual mode, 720 out-of-sandbox actions were encountered; Auto-review handled them automatically, rejected seven, and of those, four continued by a safer path while three stopped for user input. This example is a qualified snapshot, not a general benchmark. Treat the figures as context for that deployment rather than as a prediction of how many approvals a particular team will see.
The two percentages describe different things: the roughly 200-times figure compares how often sessions stopped for human approval, while the around-99% figure describes Auto-review’s decisions on the small fraction of actions that needed review. Neither number says that 99% of all agent actions were approved, nor establishes that an unfamiliar deployment will see the same rate. The 720-action example likewise illustrates outcomes in one internal setting, not a target an organization should expect to reproduce.
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What should you remember about agent autonomy settings?
There is no single autonomy control that defines an agent’s full authority. Sandbox and network rules constrain what it can reach, review rules determine how requests needing approval are handled, and organization requirements may override local choices. In API-built agents, the application must separately implement guardrails and human review; a Codex setting does not silently provide them.
Use the setting that matches the problem: adjust the sandbox or network policy to change technical reach, approval policy to change how eligible actions are reviewed, workspace requirements to enforce organization-wide choices, and application guardrails or human review to govern API tools. Keeping these responsibilities separate makes the agent’s limits easier to understand and audit.
Frequently Asked Questions
Does review: "none" send an Agents API request to a human?
No. It disables automatic review and does not itself route supported requests into a manual approval queue. An application that needs human sign-off must build and enforce its own pause-and-decision workflow.
Can automatic review guarantee that an agent will not delete files or expose secrets?
No. OpenAI describes those as examples of serious actions the reviewer is intended to block, not outcomes it guarantees in every case. Technical limits on writable paths, network access, and tool capabilities remain important.
Why might my local Codex setting not take effect in a workspace?
A workspace administrator may have enforced a requirement that outranks both workspace starting defaults and member-local starting choices. Some settings are defaults or access controls rather than permissions to use otherwise unavailable models or capabilities.
Does the Agents API review setting affect browser sign-in requests?
Browser-origin and sign-in requests continue to use the application’s existing handlers. The session review field is not a replacement for those handlers or a general-purpose browser permission setting.
Does review: "auto_review" change an agent’s sandbox or network access?
No. It enables automatic approval or denial for supported requests that need review. Sandbox and network access are controlled separately by the relevant execution environment and policies.
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Agent autonomy is the combined effect of technical boundaries, review behavior, network policy, and administrative enforcement—not a single setting. Configure each layer for the product you actually use, and build separate guardrails and human-approval paths for API applications.
