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For an AI agent that can change data, contact people, alter systems, or spend money, use both sandboxing and approval gates. They address different failure modes. A sandbox limits where agent-directed code can run and what it can reach; an approval gate stops a specific sensitive action before it happens. Neither control proves that an action is authorized, and neither substitutes for least-privilege credentials, network restrictions, validation, and audit logging.
Approval gates and sandboxes solve different problems
It is tempting to treat a sandbox as a complete safety mechanism or an approval prompt as a complete governance mechanism. Both assumptions are wrong.
| Safeguard | Primary control | Good uses | What it does not establish |
|---|---|---|---|
| Approval gate | Whether a proposed action may execute | Sending messages, publishing content, deleting records, spending money, changing access, deploying to production, or modifying an external system | Isolation of the agent’s other code, tools, files, credentials, or network connections |
| Sandbox | Which resources and execution environment are available | Shell commands, generated files, package installation, test runs, temporary workspaces, and other agent-directed computation | That every action is appropriate, that every network route is blocked, or that the implementation has a particular isolation strength |
| Guardrail or validation | Whether inputs, outputs, arguments, or tool behavior satisfy a rule | Schema checks, target allow-lists, dangerous-command detection, policy checks, and scope validation | Human authorization for a consequential side effect |
| Network and credential policy | Where the agent can connect and which secrets it can reach | Restricting outbound destinations, separating credentials, and brokering access to third-party services | A final decision that a proposed business action is authorized |
An approval gate is a decision point. In an agent workflow, the tool call pauses before execution; while approval is unresolved, the tool does not run. The workflow can resume from saved run state after a person or policy service approves or rejects it. A sandbox is an execution boundary: it supplies a place for commands and file changes, but its actual protection depends on the backend and its configuration.
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OpenAI describes the relationship plainly in its account of its Codex deployment: “Approvals and sandboxing work together.” The practical meaning is that the sandbox contains routine work while the approval policy controls when an action outside the permitted boundary, or an otherwise sensitive action, requires review.
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Why consequential agents need both controls
An agent can make a harmful change while remaining inside a technically isolated workspace. For example, it might delete the wrong files in its assigned project, publish an incorrect document through an allowed tool, or use a permitted account to send an unwanted message. A sandbox may limit the blast radius, but it does not decide whether the intended side effect is acceptable.
The reverse failure is also possible. An approval gate can pause a deployment command, yet the agent may still be able to read credentials, inspect unrelated files, or make outbound connections while preparing the request. Human review of one tool call does not isolate the rest of the execution environment.
Use both when an agent has any combination of write access, broad permissions, difficult-to-reverse effects, access to confidential information, network connectivity, or financial authority. For low-risk read-only tasks, automatic checks in a constrained environment may be sufficient, but that decision should follow an explicit risk assessment rather than a default assumption.
Assess risk before choosing a control
Classify each tool and workflow, not merely the agent as a whole. Four questions provide a useful first pass:
- What can the tool change? Reading a file is materially different from writing a repository, changing an identity policy, or issuing a payment.
- How reversible is the effect? A disposable test artifact is easier to undo than a public post, a deleted customer record, or a transfer of funds.
- Which identity and permissions are used? A narrowly scoped service account is safer than a personal administrator credential.
- What is the potential impact? Consider privacy, security, legal, operational, reputational, and financial consequences.
Use automatic validation for read-only and readily reversible operations when the environment is appropriately constrained. Add human review when a call has write access, broad permissions, poor reversibility, external recipients, production impact, or financial consequences. A single workflow can use different thresholds for different tools.
A practical architecture for safe agent workflows
1. Start with least privilege
Give the agent only the files, accounts, tools, permissions, and network destinations required for the task. Separate workloads that should not share data. Avoid placing long-lived application keys in the agent’s environment. OpenAI’s security guidance warns that generated code can read the files, credentials, and network available to its environment; it recommends keeping an application API key outside that environment and using a broker or proxy for third-party access where possible.
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2. Establish the execution boundary
Define exactly what the sandbox contains before enabling tools. Document the filesystem roots, process privileges, mounted volumes, operating-system identity, package sources, network egress, persistence, and cleanup behavior. “Sandboxed” is not a technical specification by itself. An SDK session’s isolation depends on its backend and configuration, so confirm the implementation rather than inferring protection from the label.
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Let the agent prepare a patch, report, email, migration, or deployment plan in a bounded workspace. Keep the protected repository, customer system, production environment, or external recipient behind a separate commit step. This separation allows automated checks and a reviewer to inspect the proposed result before the side effect occurs.
4. Validate at the side-effecting tool
Place checks next to every custom tool that can change state. Agent-level checks alone may not cover every nested call. OpenAI’s guardrails documentation states that input guardrails run only for the first agent in a chain, output guardrails only for the final agent, and tool guardrails only for the tools to which they are attached. If every write-capable tool must satisfy a rule, attach that rule to each such tool or enforce it in a shared execution layer.
5. Decide whether approval is required
When a call crosses your risk threshold, create an approval request before invoking the side effect. Show the reviewer the target, requested action, complete arguments, acting identity, and engagement or business scope. Do not ask for a vague “approve?” decision that hides what will happen.
6. Fail closed when review is unavailable
If the reviewer, policy service, or required validation is unavailable, do not silently execute a high-impact action. OpenAI’s guardrails guidance explicitly recommends fail-closed behavior for the described authorized cybersecurity workflow. Lower-risk, read-only work can have a separately documented fallback, but it should not be allowed to bypass a gate intended for consequential actions.
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Log the initiating request, agent and tool identity, arguments, sandbox and network-policy decisions, approval request, reviewer or policy decision, execution result, and any error or rollback. Records should make it possible to reconstruct what the agent proposed, what was approved, and what actually happened.
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How approval gates should work
Pause before execution, not after
The gate must sit in front of the side effect. If an agent sends the email and then asks for approval, the control is an audit prompt, not an approval gate. In an Agents SDK-style flow, an unresolved approval prevents the tool from executing. The run can be persisted and resumed after the decision, so the agent does not need to regenerate the entire plan.
Make the request specific
A useful approval record answers:
- Which tool will run?
- Which account or identity will it use?
- What exact target will it affect?
- What arguments and data will be sent?
- What is the expected result?
- What scope or authorization covers the action?
- What happens if it fails or is only partially completed?
For sensitive cybersecurity actions, OpenAI’s guidance calls for checking the target, action, arguments, identity, and engagement scope; denying out-of-scope activity; pausing ambiguous or high-risk actions; recording decisions and outcomes; and failing closed if review is unavailable.
Cover nested and indirect tools
An agent may call a planner, which calls a sub-agent, which invokes a write-capable tool. A gate attached only to the top-level agent can miss that path. Enforce approval at the boundary where the side effect occurs, and verify that delegated or nested tools inherit the same policy.
Prevent rubber-stamp review
A prompt is not meaningful oversight if reviewers lack context or receive so many similar requests that approval becomes reflexive. OpenAI’s governance work identifies approval burden, reviewer context, review practices, and evaluation of meaningful human oversight as open questions. Reduce noise by grouping related low-risk changes, showing concise but complete diffs, and routing only genuinely consequential decisions to a person.
What a sandbox must actually constrain
Filesystem and process access
Specify which directories are visible, writable, mounted, or persistent. Use a disposable workspace for generated artifacts when possible. Restrict process privileges and prevent access to host paths that are not part of the task. Cleanup should be explicit: determine whether files survive between runs and who can retrieve them.
Network egress
Decide whether the task needs network access at all. If it does, allow only approved destinations and protocols. A filesystem sandbox does not automatically block outbound traffic, DNS lookups, or data exfiltration through an allowed endpoint. OpenAI’s security guidance recommends restricting outbound endpoints and separating credentials.
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Credentials and identity
Do not assume that a sandbox hides secrets injected into its environment. Agent-generated code can access credentials available to that environment. Prefer short-lived, narrowly scoped tokens delivered through a broker, and keep the main application’s long-lived key outside the agent runtime. Record which identity was used for each side effect.
Persistence and shared state
Persistent workspaces improve productivity but increase the chance that one task can influence another. Decide whether sessions share files, caches, package stores, processes, or network identities. Isolate tenants and unrelated projects, and treat every shared mount as part of the trust boundary.
Control choices by action type
| Example action | Baseline controls | Approval recommendation | Additional checks |
|---|---|---|---|
| Read documentation or inspect a local file | Read-only permissions; restricted filesystem; no unnecessary credentials | Usually automatic | Redact sensitive output and log access |
| Generate code or a report in a temporary workspace | Sandboxed process and filesystem; controlled package and network access | Usually automatic until export or commit | Run tests, scan outputs, and review the diff |
| Modify a development repository | Writable sandbox; branch or isolated workspace; scoped identity | Review before merge or protected-branch update | Require tests, policy checks, and an auditable diff |
| Send an external email or publish content | Recipient and content validation; separate sending identity | Human or policy approval before sending | Show recipients, attachments, and final body |
| Delete records or change access | Strong identity boundaries; target allow-list; rollback plan | Human approval | Confirm scope, reversibility, and resulting permissions |
| Deploy to production or spend money | Isolated build environment; protected credentials; network controls | Human approval before the side effect | Require change window, amount or target limits, and post-action verification |
Common failure modes and fixes
“The sandbox makes approval unnecessary”
A sandbox limits resources; it does not determine business intent. Keep approval for publication, deletion, spending, access changes, and production operations even when the command runs in an isolated environment.
“The approval prompt makes sandboxing unnecessary”
Reviewing one call does not prevent the agent from reading secrets or making unrelated network requests while preparing it. Constrain the environment independently.
Overbroad sandbox permissions
A workspace that can read an entire home directory, mount production files, or reach unrestricted internet has a larger blast radius than its name suggests. Replace broad mounts with task-specific paths and allow-listed egress.
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Environment variables, configuration files, package managers, and command output can expose secrets to generated code. Remove long-lived keys, use brokered access, and redact logs.
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Checks attached at the wrong layer
Agent-level input or output checks may not inspect every tool call, especially in a multi-agent chain. Attach validation to each side-effecting tool or enforce it in a common gateway.
Review service outage becomes an automatic allow
Fail closed for actions designated high impact. Make any lower-risk outage behavior explicit, time-limited, and observable rather than allowing an implicit bypass.
Indirect execution through another party
Hard-coded restrictions can be circumvented if an agent persuades another person or service to perform a restricted action. OpenAI’s governance discussion identifies this limitation and considers monitored network channels as one way to bound that route. Treat external services and human recipients as part of the threat model.
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How to compare implementations
When evaluating an agent platform or internal design, compare the controls themselves rather than relying on product labels. Ask:
- What filesystem and process isolation does the backend provide, and which paths or mounts are exposed?
- Can outbound network traffic be disabled or limited to approved endpoints?
- Are credentials separated from the runtime, short-lived, scoped, and brokered?
- Does approval happen before the side effect, and does it cover delegated or nested tools?
- Can a paused run resume from saved state without losing the exact proposed action?
- Are approval decisions, tool arguments, network decisions, and results recorded?
- What happens if a reviewer or policy service is unavailable?
- How much operational friction will approval volume create for reviewers?
These are design dimensions, not a universal product score. Current API and SDK behavior can change, so verify the version and backend configuration you deploy.
Limits of both safeguards
No universal approval threshold or review-frequency number has been established for all agent workloads. The right boundary depends on permissions, reversibility, impact, and the quality of the execution boundary.
Current sandboxing systems may not be well suited to effectively bounding highly capable agents, according to OpenAI’s governance discussion. That is a caution about the limits of the technique, not a claim that every existing sandbox fails. Likewise, an approval prompt is only as strong as the information presented, the authority of the reviewer, and the enforcement point that blocks execution.
Use sandboxing to reduce what the agent can reach, approval gates to decide whether a sensitive effect may occur, validation to check the request, and credential and network policy to reduce exfiltration and privilege paths. The combination is stronger because each control covers a different part of the action chain.
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