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A prompt telling an agent to “ask first” is not an enforcement boundary. A reliable design combines a restricted execution sandbox, policy checks that run in code, explicit human approvals for consequential decisions, and an audit trail containing the request, proposed call, decision and result.
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Runtime permissions and approval gates solve different problems
Runtime permissions answer what the software is technically able to do. They constrain the execution plane: which files are readable or writable, whether outbound network access exists, which commands or packages can run, which tool endpoints are exposed, and which credentials are mounted. OpenAI describes this boundary in its Codex deployment as controlling “where Codex can write, whether it can reach the network, and which paths remain protected” (OpenAI, “Running Codex safely at OpenAI,” May 8, 2026).
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Approval gates answer when execution must stop for a person. A gate can apply even when the requested action is technically inside the sandbox, and it can be required when an action would cross the sandbox boundary. The same OpenAI article states that approval policy determines when Codex must ask before an action, including work outside the sandbox. A reviewer’s approval does not create isolation; it is a decision control layered on top of the runtime boundary.
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Use both controls when the impact of an action warrants it. A read-only search over a disposable workspace may need neither a human prompt nor broad credentials. A production database migration may need a narrowly scoped connection, deterministic validation, an explicit approval and a complete record of who approved it.
Map the control architecture before writing policy
Most agent systems have several enforcement points. Assign each responsibility to the layer that can actually enforce it rather than relying on an instruction in the model context.
Execution plane
The execution plane is the sandbox, container, virtual machine or host account in which commands and file operations occur. It should enforce path permissions, process privileges, package installation rules, environment variables, network egress and credential visibility. Keep control-plane credentials, production secrets and administrative sockets outside the workspace whenever the task does not require them.
Agent harness
The harness owns the agent loop: it selects tools, routes calls, applies approval rules, records traces, pauses a run and restores it after a decision. OpenAI’s sandbox guidance describes the harness as owning routing, approvals, tracing, recovery and run state, while the sandbox is the execution plane for files, commands, packages and workspace activity (OpenAI, “Sandbox Agents”).
Tool server or API
A tool server can enforce permissions again at the point where an external action occurs. This is important for agents that can be called through more than one application: a policy in one user interface should not be the only barrier protecting a shared deployment or data service. Server-side decisions can return allow, ask or deny outcomes, as described in Anthropic’s managed-agent permission policies (Anthropic, “Permission policies”).
Human reviewer
The reviewer supplies accountable judgment for decisions that code cannot reliably evaluate, such as whether a customer-facing message is appropriate, whether a change window is acceptable, or whether an unusual transfer is legitimate. The reviewer should see the proposed action and enough context to make a decision, not merely an opaque “approve?” button.
Step 1: inventory every capability and data path
Start with a capability register for one agent task. Record each tool, the data it can read, the systems it can modify, and the identity under which it runs. Separate capabilities that are often bundled together:
- Read versus write access to each filesystem path or data store.
- Local command execution versus outbound network access.
- Queries that return information versus calls that create, delete or publish something.
- Actions affecting a single test object versus an account, customer, production system or financial balance.
- Short-lived, task-scoped credentials versus reusable administrative credentials.
For each capability, identify the smallest useful scope. A documentation agent may need to read a repository and write a generated file in one directory, but not read a home directory or alter deployment settings. A support agent may need to look up an order but not issue a refund. This inventory is a practical design step; the cited platform documentation describes tool filters and read-only annotations, but does not prescribe a universal inventory worksheet.
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Classify side effects
Mark whether an operation is reversible, how quickly damage can spread, and whether a person is accountable for the outcome. “Delete a temporary file” and “delete a customer record” are both write operations but should not share a policy class. Include indirect effects: a code-generation tool that commits to a shared branch, a notification tool that sends an email, or a deployment tool that changes production all create external consequences even if the call itself looks like a routine function invocation.
Identify data boundaries
List personal data, secrets, regulated records and proprietary material separately. Restrict both the agent’s ability to read them and the destinations to which tool results can be sent. Network permission should be expressed as an allowlist of hosts or services where possible, not as a blanket “internet on” switch.
Step 2: establish the narrowest runtime boundary
Translate the inventory into enforcement settings before you define approval prompts. Grant only the paths, destinations, tools and credentials required for the task.
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- Choose a workspace. Give the run a dedicated directory or container. Make unrelated paths inaccessible rather than merely hidden from the model.
- Set filesystem modes. Mark source directories read-only when the agent only needs to inspect them. Expose a separate writable output directory for generated artifacts.
- Control network egress. Disable network access for offline tasks. For tasks that require it, allow only the documented hosts and ports, and log blocked requests as well as successful ones.
- Expose tools selectively. Register only the tools in the task’s capability set. Do not expose an administrative tool and rely on a later prompt to keep the model from selecting it.
- Scope credentials. Use task-specific identities with the minimum role and a short lifetime. Keep production and control-plane credentials outside the sandbox unless a defined operation requires them.
- Separate execution from control. The process that evaluates policy and records approvals should remain protected if the agent’s workspace is compromised.
A sandbox reduces the blast radius of a mistaken or manipulated tool call, but it does not prove that an agent is safe in every scenario. It can still misuse every capability granted inside the boundary, leak data through an allowed destination or exploit a vulnerable tool. Pair isolation with policy checks and review.
Step 3: define an approval policy that can be enforced
Write the policy in terms of tools and action attributes, not vague intentions. For each tool or operation, choose one of three outcomes:
- Automatically permitted: low-impact, read-only or easily reversible calls that fit the task scope.
- Prompt-and-pause: calls that need a person to inspect the proposed arguments, target and context before execution.
- Denied: calls outside the task’s purpose, outside the allowed resource scope or prohibited by law or organizational policy.
OpenAI’s Realtime API documents approval settings named always, never and filtered approval based on tool names or read-only annotations (OpenAI API Reference, “Server events | MCP approval policy”). Anthropic’s managed-agent documentation presents automatic, approval-based and server-evaluated permission behavior, with allow, ask or deny outcomes (Anthropic, “Permission policies”). These are platform examples; their configuration names and semantics are not interchangeable.
A practical risk rubric
No cited source establishes a universal approval threshold. Set your own policy using explicit factors and document the rationale. A useful editorial rubric is:
- Impact: Could the action affect production, customers, regulated data, money or public communications?
- Reversibility: Is there a tested rollback, or is the result permanent?
- Scope: Does the call touch one object, a team resource or an entire organization?
- Uncertainty: Are the target, arguments or expected result ambiguous?
- Accountability: Does a named person or role need to attest that the action is appropriate?
Require review when the combined risk exceeds the tolerance for that workflow. Treat this as an organizational recommendation, not an industry mandate. A team may automatically allow a reversible staging change but require approval for the same operation in production.
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Make read-only distinctions explicit
Read-only annotations are useful only when they are accurate. A tool that is labeled read-only but triggers a subscription, sends a message or updates a record defeats the policy. Verify the implementation at the server and record the classification in the capability register.
Step 4: pause before side effects and resume the same run
A reliable manual approval is an execution state transition, not a conversational suggestion. When a gated call is selected, the runtime should:
- Stop before invoking the tool. Do not perform a partial side effect while preparing the prompt.
- Present the proposal. Show the tool name, normalized arguments, target resource, relevant data, expected effect and any policy reason for the gate.
- Identify the reviewer. Record the person or service making the decision, their role and the time of the decision.
- Return an explicit result. Use a structured approve or reject outcome, with an optional reason. Do not infer approval from silence, a timeout or a natural-language reply that the runtime cannot validate.
- Resume deliberately. Continue the preserved run only after approval. On rejection, terminate or return to a safe planning state according to the workflow policy.
The OpenAI Agents SDK documents this interruption pattern with saved RunState, and covers approvals in top-level tools, handoffs and nested agent tools (OpenAI Agents SDK, “Human-in-the-loop”). Preserving run state prevents the agent from starting a new, inconsistent conversation after a reviewer acts. It also gives the reviewer a stable proposal to inspect.
Handle rejection and expiry
Define what happens when a reviewer rejects, edits, times out or loses access. A rejection should not silently retry the same call with altered arguments. Require the agent to explain the rejection to the caller or produce a revised proposal. Expired approvals should be treated as no decision, especially when the target, credentials or deployment window may have changed.
Guard against time-of-check changes
Revalidate critical facts immediately before execution. A file, account balance or deployment target may change while a human is reviewing the proposal. The final tool invocation should confirm that the approved arguments still match the current resource and policy. If they differ, pause again.
Step 5: layer deterministic guardrails around human review
Automated guardrails are best for checks that can be evaluated consistently in code: argument types, path containment, host allowlists, maximum amounts, required fields, data classifications and prohibited commands. Human review is better for accountable judgment. OpenAI’s guidance separates guardrails for automatic checks from human review for approval decisions (OpenAI, “Guardrails and human review”).
Pre-approval checks
Run inexpensive validation before showing a request to a reviewer so obviously invalid proposals do not create approval fatigue. Examples include rejecting a path outside the workspace, a destination outside the network allowlist or an amount above a hard maximum.
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Validate again after approval and immediately before execution. The Agents SDK notes that, by default, function-tool input guardrails run after approval and just before execution; developers can enable pre-approval checks, with validation performed again after approval (OpenAI Agents SDK, “Human-in-the-loop”). This second check protects against changed state and against a proposal being modified between review and invocation.
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Keep guardrail failures distinct from rejections
A policy validator denying an invalid path is different from a person rejecting a legitimate request. Record separate outcomes so operators can see whether a workflow is too permissive, too restrictive or simply producing poor arguments. Do not convert a hard deny into an approval request unless policy explicitly allows an exception path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 6: build an audit trail that explains every decision
At minimum, retain the original user request, the selected tool and arguments, the applicable permission and network decisions, the approval identity and result, the execution result, timestamps and correlation identifiers. Include blocked attempts, not only successful actions. OpenAI describes logs used to inspect the original request, tool activity, approval decisions, tool results and relevant network-policy decisions or blocks (OpenAI, “Running Codex safely at OpenAI,” May 8, 2026).
Make records useful to an investigator
Store the policy version and tool definition used at decision time. Preserve the exact arguments shown to the reviewer and the exact arguments executed. If an agent transforms or redacts data for display, retain enough metadata to explain the transformation without exposing secrets. Protect logs from modification by the agent and restrict who can read sensitive tool results.
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Track approval requests by tool, approval and rejection rates, guardrail failures, blocked network calls, expired decisions and time from request to execution. A spike in approvals for a supposedly routine tool may indicate poor task decomposition or an overly broad permission class. A high rejection rate can signal that the proposal lacks context or that the tool should be denied automatically.
Compare runtime designs using the same axes
When evaluating platforms or an internal implementation, compare the enforcement point, scope granularity, approval behavior, resume and rejection semantics, and auditability. The following examples reflect the behaviors documented in the cited platform materials.
| Design element | Enforcement point | Typical scope | Decision behavior | Resume or failure behavior | Evidence to retain |
|---|---|---|---|---|---|
| Sandbox boundary | Operating system, runtime or execution harness | Filesystem paths, network reach, commands, packages and workspace activity | Permission is enforced automatically at execution | Unauthorized operations are blocked; the run can handle the error according to its policy | Allowed and blocked operations, including relevant network-policy decisions |
| Harness approval policy | Agent harness before a tool call | Whole tools, named tools, read-only classifications or action filters | Automatic permission, prompt-and-pause, or denial | Approved calls resume from preserved run state; rejected calls stop or follow a defined recovery path | Proposed call, reviewer identity, approval result, reason and execution result |
| Server-evaluated permission | Tool server or API | Server-defined tools, resources and policy filters | Server returns allow, ask or deny outcomes | The client proceeds only on allow; ask invokes the configured review path; deny prevents execution | Server policy decision, request, response and any block |
| Deterministic guardrail | Harness or tool implementation | Arguments, paths, hosts, amounts, data classes and other machine-checkable constraints | Validation passes or fails | Failure prevents execution or sends the run to a defined correction path; critical checks run again before execution | Rule version, failed condition and resulting action |
Do not treat these rows as interchangeable products or a security ranking. A sandbox limits capability, an approval gate controls timing and accountability, and a guardrail checks a condition. A robust system may use all three for one operation.
Design approval prompts that support a real decision
Keep the approval view concise but complete. Show:
- The user’s stated objective and the agent’s proposed next step.
- The exact tool and normalized arguments, including resource identifiers.
- What will change, who or what will be affected and whether the operation is reversible.
- Relevant validation results, policy class and any warnings.
- The identity and scope under which the call will execute.
- Clear actions for approve, reject and request revision.
Never ask a reviewer to approve a broad natural-language plan when the runtime can present the concrete call. If several calls are independent and equally low-risk, your policy may allow a batch decision; otherwise gate each consequential side effect separately. A single approval for an evolving plan can become stale as the agent changes its arguments.
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Common failure modes and fixes
The prompt says “ask first,” but the agent still acts
The instruction is not enforced by the runtime. Remove the side-effecting tool from automatic execution, route it through an approval-capable harness or server, and deny direct access from alternate paths.
The sandbox is broad and approvals are rare
Narrow filesystem and network scopes first. Approval should not compensate for excessive capability. Split a powerful tool into smaller read and write operations so policy can distinguish them.
Approvals appear for harmless reads
Check the tool’s read-only annotation and implementation. If the call has no side effect, classify it correctly or add a filtered policy for that tool. Keep an approval option when a read can expose sensitive data or trigger a billable operation.
The reviewer approves one thing and another executes
Bind the approval to a hash or immutable representation of the tool name, arguments and target. Re-run validation after approval and pause again if any material field changes.
A rejected call is retried indefinitely
Persist the rejection and its reason in run state, cap retries and require a materially different proposal. Escalate repeated failures to an operator rather than letting the model search for an unapproved route.
Logs cannot explain what happened
Correlate the original request, model turn, tool proposal, policy evaluation, reviewer action and tool result under one run identifier. Record blocked network requests and guardrail failures, not just successful calls.
A nested agent bypasses the parent policy
Propagate the same permission and approval context to handoffs and nested tools. The Agents SDK explicitly documents approval points in handoffs and nested agents; test those paths rather than assuming a top-level gate covers them.
Roll out safely and test the policy
- Start in a non-production workspace. Use representative tools and data with credentials that cannot affect production.
- Exercise every policy class. Test automatic, approval, denial, timeout, malformed-argument and out-of-scope cases.
- Test alternate routes. Attempt direct tool-server calls, nested-agent calls and retries to verify that the same boundary applies.
- Inspect the reviewer experience. Confirm that the proposal contains enough context and that approve and reject results are unambiguous.
- Verify state recovery. Interrupt a run, approve it later and confirm that it resumes the intended step without duplicating earlier side effects.
- Review evidence. Ensure logs contain policy versions, identities, decisions, blocks and results while protecting secrets.
- Promote gradually. Expand paths, hosts or tool classes only after observed behavior matches the intended policy.
Revisit the inventory whenever a tool changes, a new credential is added, a network destination is introduced or the agent is given a new objective. Permissions and approval rules describe the current system; they do not remain correct automatically as capabilities evolve.
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A production-ready policy should let an operator answer five questions for any proposed action: Where is it enforced? What exact resource can it reach? Does it run automatically, pause for review or fail closed? How does rejection or interruption affect the run? Which record proves what happened? If any answer depends solely on the model following a sentence in its prompt, that part of the design is not an enforcement boundary.
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