For an AI agent that can change systems, contact people, spend money, or handle sensitive data, use both safeguards. Sandboxing limits the files, processes, network, credentials, and persistence available to agent-directed work. An approval gate pauses a specific consequential action until a person or policy allows it. A sandbox cannot decide whether an email should be sent, a production change is authorized, or a purchase is legitimate; an approval gate cannot contain malicious or faulty code that runs before the gate. The right design combines a tightly bounded execution environment with risk-based approvals.
What an approval gate controls
A decision before a side effect
An approval gate is a control point in an agent workflow. Before a tool call or other sensitive action executes, the system presents the proposed target, operation, arguments, identity, and relevant context to a human reviewer or policy service. The call remains paused until it is approved or rejected.
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In the Agents SDK flow, an unresolved approval means the tool does not execute. The workflow can be resumed from saved run state after a decision, so the agent does not have to restart the entire task. This makes an approval gate a decision mechanism, not an isolation mechanism.
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Actions that normally merit a gate
- Sending an external email, message, notification, or publication.
- Deleting or overwriting data.
- Changing production configuration or deploying code.
- Granting access, rotating permissions, or modifying identity settings.
- Making a purchase, transfer, refund, or other financially consequential request.
- Running a security action whose target or scope could affect a third party.
A gate should be attached to the operation that creates the side effect. A broad agent-level prompt is insufficient when the workflow can call nested tools or hand work to another agent.
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What sandboxing controls
An execution boundary
A sandbox is an environment in which agent-directed commands, generated code, file edits, and artifacts run. Depending on the backend and its configuration, the boundary can restrict filesystem paths, processes, package installation, network egress, mounted resources, identities, and persistence.
The word “sandbox” does not specify a universal isolation strength. The Agents SDK documentation states that session isolation depends on the backend and configuration. Before relying on it, verify the actual filesystem, process, network, mount, persistence, and credential behavior of the implementation you are deploying.
What a sandbox does not decide
A sandbox can prevent an agent from writing outside an allowed workspace, but it does not establish that a permitted write is appropriate. It does not determine whether a customer should receive a message, whether a proposed deletion has been reviewed, or whether a business action is authorized. It also does not automatically block every network route or secret available to the runtime.
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Why the controls complement each other
OpenAI’s article “Running Codex safely at OpenAI,” published May 8, 2026, describes the relationship directly: “Approvals and sandboxing work together.” The deployment description separates sandbox boundaries around writes, network access, and protected paths from approval policy that determines when an action outside the sandbox requires review.
| Safeguard | Primary question it answers | Good uses | Limit |
|---|---|---|---|
| Approval gate | May this proposed action execute now? | Sending, publishing, deletion, spending, access changes, production changes | Does not isolate the rest of the agent’s code or tools |
| Sandbox | Which resources and execution environment are available? | File manipulation, shell commands, generated artifacts, packages, bounded workspaces | Does not prove that an allowed action is appropriate or that every network path is blocked |
| Guardrail or validation | Do these inputs, outputs, arguments, or behaviors meet an automatic rule? | Schema checks, policy checks, target validation, output screening | Does not provide human authorization for a consequential action |
| Network and credential policy | Where may the agent connect, and which secrets can it reach? | Allowlisted endpoints, brokered credentials, reduced exfiltration paths | Does not answer whether a proposed business action is authorized |
Using only a sandbox leaves authorization decisions to the agent. Using only approvals leaves code and tools with more opportunity to read, modify, or exfiltrate data before a reviewer sees a proposed side effect. Layering the controls addresses both failure modes.
Choose controls by risk, not by habit
Start by classifying each tool and action. Four questions determine the required strength of controls:
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- What can the tool change? Distinguish read-only operations from writes, permission changes, external communications, and financial actions.
- How reversible is the result? A local draft is easier to undo than a public post, irreversible deletion, or transfer of funds.
- What is the impact of error or misuse? Consider customer harm, privacy exposure, operational downtime, security consequences, and financial loss.
| Action profile | Typical treatment | Reason |
|---|---|---|
| Read-only, narrow scope, easily repeatable | Sandbox plus automatic validation; approval may be unnecessary | The agent cannot directly create a consequential side effect, provided permissions are genuinely read-only |
| Write-capable but local and reversible | Sandbox with path restrictions, change validation, and review before promotion | The work can be prepared safely, while a reviewer controls the transition to a protected system |
| Broad permissions, poor reversibility, or sensitive data | Sandbox, strict credential and network policy, tool-level validation, and human approval | A single mistake can affect data, accounts, or external parties |
| Financial, production, access-control, or external-communication action | Fail-closed approval gate with detailed context and an audit record | The side effect requires an explicit authorization decision |
These are design categories, not a universal approval threshold. The available guidance does not establish a single percentage, dollar amount, or review interval that applies to every organization.
A practical layered design
1. Begin with least privilege
Give the agent only the files, accounts, permissions, tools, and network destinations required for the task. Isolate workloads that should not share data. Remove unused mounts, tokens, and service accounts instead of relying on the agent to ignore them.
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2. Separate drafting from committing
Let the agent produce a patch, report, generated artifact, or message in a bounded workspace. Require validation and review before that artifact reaches a protected repository, customer, production system, or external recipient. This separation lets the agent be productive without granting it immediate authority to commit the result.
3. Put validation beside every side-effecting tool
OpenAI’s guardrails documentation distinguishes agent-level and tool-level coverage: input guardrails run only for the first agent in a chain, output guardrails only for the final agent, and tool guardrails only for tools to which they are attached. If every custom tool with side effects must be checked, attach the check to each such tool rather than assuming an upstream or downstream agent check covers nested calls.
4. Gate according to the action’s risk
Read-only and readily reversible work can often proceed under automatic checks. Escalate actions with write access, broad permissions, poor reversibility, external effects, or financial impact to stronger validation or human review. Do not make every harmless read operation interrupt a reviewer; excessive prompts create their own safety problem.
5. Make the approval request inspectable
The reviewer should see what will happen, which target will be affected, the exact arguments, the acting identity, and any relevant scope or policy result. A request that says only “Allow tool call?” encourages rubber-stamping because the reviewer cannot evaluate the consequence.
6. Fail closed for high-impact actions
If a required reviewer, policy service, or validation check is unavailable, do not silently execute the sensitive action. OpenAI’s guardrails guidance explicitly recommends failing closed for the described authorized cybersecurity workflow. For lower-risk work, you may define a separate, documented fallback; do not let that exception apply implicitly to production, financial, access-control, or external-communication actions.
7. Record the complete action path
Log the initiating request, agent and tool activity, sandbox and network-policy decisions, approval request, reviewer or policy decision, execution result, and any resumed run state. These records support incident response, accountability, and later evaluation of whether approvals are catching meaningful risks.
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How to implement an approval workflow
- Inventory tools and side effects. For each tool, document whether it reads or writes, its targets, required identity, reversibility, and possible financial or operational impact.
- Define the approval boundary. Place the pause immediately before the consequential operation, not after a wrapper has already sent a request or changed state.
- Construct a review payload. Include the target, action, arguments, identity, engagement or business scope, expected result, and any automatic validation findings.
- Run automatic checks first. Reject malformed or clearly out-of-policy requests without consuming human review. Escalate ambiguous or high-risk requests instead of guessing.
- Pause execution. Keep the tool call unresolved while awaiting the decision. In the Agents SDK pattern, the unresolved approval prevents execution.
- Resume from saved state after approval. Continue the same run only after recording the decision. If rejected, return a controlled refusal or ask the agent to produce a safer alternative.
- Verify the result. Check that the actual target and outcome match the approved request. Do not treat approval of one set of arguments as blanket permission for later calls.
- Audit and tune. Review approval volume, rejection reasons, near misses, and reviewer time. Adjust risk rules and interface context rather than simply approving prompts faster.
How to evaluate a sandbox in practice
Filesystem and process isolation
Confirm which directories are writable, whether protected paths are mounted, whether the agent can start arbitrary processes, and whether a child process inherits the same restrictions. Test attempts to read outside the workspace and to modify protected files.
Network egress
Determine whether outbound traffic is disabled, allowlisted, proxied, or unrestricted. Check DNS behavior, raw socket access, package registries, metadata services, and redirects. OpenAI’s security guidance advises restricting outbound endpoints; an allowlist should name the destinations needed for the task rather than relying on a general internet connection.
Identity and credentials
Inspect environment variables, mounted credential files, instance roles, SSH agents, and cloud metadata access. Keep the application API key outside the agent environment where possible. For third-party services, use a broker or proxy that exposes only the narrow operation and scope required, rather than placing a long-lived secret in the workspace.
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Persistence and cleanup
Establish whether files, package caches, process state, and credentials persist between sessions. A persistent workspace may be useful for a multi-step task but can also carry unreviewed or sensitive data into later runs. Define retention and cleanup behavior explicitly.
Backend and configuration drift
Document the backend that supplies the isolation and recheck behavior after upgrades or configuration changes. A session described as isolated in one deployment may have different mounts, network access, or persistence in another.
Guardrails are a third layer, not a substitute
Guardrails automate checks on inputs, outputs, tool arguments, or tool behavior. They are valuable for enforcing schemas, detecting disallowed targets, and rejecting known-bad patterns, but they do not by themselves authorize a consequential business action.
For sensitive cybersecurity actions, OpenAI’s guidance recommends 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. The same pattern applies to other high-impact domains: validate automatically, then require approval when the remaining decision is one of authorization or judgment.
Common failure modes and fixes
“The sandbox means approval is unnecessary.”
Failure: The agent can still send an allowed network request, overwrite an allowed file, or invoke a permitted API with harmful arguments.
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Fix: Keep approval gates on sensitive side effects, even when the preparatory work runs in a sandbox.
“The approval prompt is the security boundary.”
Failure: Code can read secrets, alter files, or contact services before the particular action that triggers review.
Fix: Restrict the runtime first with least privilege, filesystem and process controls, network policy, and credential separation.
Reviewers become rubber stamps
Failure: High prompt volume or poor context leads reviewers to approve mechanically. OpenAI’s governance paper identifies approval burden, reviewer context, review practices, and evaluation of meaningful oversight as open questions.
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Fix: Gate only risk-bearing actions, show concrete targets and arguments, group genuinely related decisions, and measure rejection quality and reviewer workload rather than approval speed alone.
Nested tools bypass the intended check
Failure: An agent-level guardrail covers the first or final agent while a nested custom tool performs the actual side effect.
Fix: Attach validation and approval logic to every side-effecting tool and verify the complete call graph.
Network restrictions are assumed, not tested
Failure: A sandbox blocks one interface but leaves DNS, a proxy, package downloads, metadata endpoints, or an inherited credential reachable.
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Hard-coded restrictions are treated as complete
Failure: The agent induces another party or service to perform a restricted action indirectly.
Fix: Monitor relevant network channels, evaluate indirect paths, and retain approval controls for the resulting external effect. OpenAI’s governance paper discusses this limitation and cautions that current sandboxing systems may not be well suited to effectively bounding highly capable agents; that is a warning about limitations, not a claim that every sandbox fails.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Scenario playbooks
Read-only research assistant
Use a sandbox with read-only data mounts and an outbound allowlist. Apply input and output checks for sensitive data. Approval is generally unnecessary for ordinary retrieval, but add a gate before sending a report to an external recipient or publishing it.
Code-change assistant
Run generation, tests, and package work in an isolated workspace. Keep production credentials and protected repository write access outside the runtime. Require review before merging or deploying, and verify that the approved commit, branch, and environment match the executed change.
Customer-communications agent
Allow drafting in a bounded workspace. Keep the sending capability behind a tool-level approval gate that displays recipients, content, attachments, and sending identity. A sandbox alone cannot prevent an authorized mail API call from reaching the wrong customer.
Financial or purchasing agent
Use a restricted environment and a brokered payment tool with narrow limits. Require fail-closed approval that shows payee, amount, currency, account, and purpose. Record the decision and reconcile the result with the approved request.
Authorized cybersecurity workflow
Constrain targets and engagement scope in automatic checks. Validate target, action, arguments, and identity; deny out-of-scope requests; pause ambiguous or high-risk activity; log decisions and outcomes; and fail closed if the review or policy service is unavailable.
Comparison checklist for an implementation
When comparing platforms or internal designs, evaluate these dimensions rather than relying on labels such as “secure sandbox” or “human-in-the-loop.”
| Dimension | Questions to answer |
|---|---|
| Filesystem and process isolation | Which paths, processes, mounts, and system interfaces are available, and can child processes escape the boundary? |
| Network egress | Are connections blocked, proxied, or allowlisted? Are DNS, redirects, metadata services, and package registries covered? |
| Credential separation | Which secrets and identities are visible, and can access be brokered for a single operation instead of exposing a long-lived token? |
| Approval coverage | Does review occur before the side effect, and does it cover nested tools and delegated agents? |
| Audit and resumability | Are requests, arguments, decisions, results, and resumed run state recorded and inspectable? |
| Unavailable-review behavior | Does the system fail closed for high-impact actions when a reviewer or policy service cannot respond? |
| Operational friction | How many prompts will reviewers receive, how much context is shown, and how will approval fatigue be measured? |
The cited API and SDK documentation is volatile, so verify current names and behavior in the version you deploy. The governance and agent-building publications provide design guidance, not a universal certification or performance guarantee.
What remains an open design question
No reviewed source establishes a universal approval threshold or a correct review frequency. The appropriate boundary depends on the agent’s permissions, the execution backend, the reversibility and impact of actions, and the quality of the reviewer’s context. Organizations should test whether reviewers detect meaningful risks, whether automatic checks catch out-of-scope requests, and whether the sandbox actually enforces the documented boundary.
Meaningful oversight is a system property, not the presence of a button. A high-volume prompt stream, vague requests, hidden credentials, or an unverified network boundary can undermine an otherwise well-designed control.
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For any agent that can affect production, customers, money, permissions, or sensitive data, deploy a least-privileged sandbox and put approval gates immediately before consequential side effects. Add tool-level validation, restrict network and credentials, separate drafting from committing, log the full action path, and fail closed when high-impact review is unavailable. Relax approval only for actions that are genuinely read-only or easily reversible after you have verified the sandbox and automatic checks in the actual backend and configuration.
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