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An AI agent’s brake pedal is not a confirmation pop-up. It is a set of controls that limits what the agent can reach, checks proposed tool calls, previews consequential changes, and pauses execution for an enforceable approval before a side effect. The application—not the model’s promise to ask first—must control whether the action runs.
What an approval gate must do
An approval gate pauses a sensitive action before execution, shows a reviewer what is about to happen, and enforces an explicit decision. OpenAI’s API documentation describes human review as pausing a run so a person or policy can approve or reject a sensitive action. Its Agents SDK supports interruption for approval and lets the application resolve the interruption and resume the same run state. OpenAI’s guardrails documentation distinguishes that human decision from guardrails, which perform automatic checks.
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The timing is essential: an alert after an email has been sent, a record changed, or code executed is an audit signal, not an approval gate. Likewise, asking the model to request confirmation is not enforcement if the tool can still run without an approval decision. Put the pause in the application or at the tool boundary that can cause the side effect.
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A reviewer needs the proposed operation and the arguments that determine its effect. For a record update, that could mean the target record and the fields and values to change; for an external message, the recipient and message content. Bind the decision to that proposal: if the arguments or target change, require a fresh review rather than reusing approval for a different action.
#1 Best Overall
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Record the decision and enforce it in the execution path. Define what happens if the reviewer rejects, the request is ambiguous, or review is unavailable. For authorized cybersecurity workflows, OpenAI recommends failing closed on review timeout or unavailability and maintaining independent filesystem, network, identity, and project boundaries. OpenAI’s guidance supports this as a workflow-specific approach, not a universal standard for every agent.
Constrain permissions before asking for approval
Approval helps assess intent; least privilege limits what can happen if the agent is confused, manipulated, or simply makes a mistake. Define the agent’s identity and the resources, tools, and operations allowed for the task. Avoid giving it a broad user account when a narrower identity or delegated access can do the job.
- Limit resources and operations: grant access only to the relevant project, files, records, or service actions, and only for the task’s duration where feasible.
- Isolate execution: use a sandbox or other separate environment for agent-generated code, and restrict outbound network access to approved endpoints when practical.
- Keep credentials outside generated code: broker access through a trusted application or proxy rather than placing long-lived or application credentials where generated code can read them.
- Set independent boundaries: enforce filesystem, network, identity, and project restrictions outside the model’s instructions.
NIST discusses risks from broad local-user access and static credentials in agent settings; OpenAI’s sandbox guidance likewise emphasizes isolation, network restrictions, and separating application credentials from agent-generated code. See NIST’s agent identity material and OpenAI’s sandbox guidance. These controls reduce exposure, but do not make an agent immune to prompt injection or guarantee that permitted actions are safe.
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Validate calls at the tool boundary
Check proposed arguments before the tool executes, and validate results where the tool can create a side effect. A general instruction to behave safely is not a substitute for checking whether a specific request is allowed for this identity, resource, and operation.
Place enforcement next to the tool or service that performs the action. OpenAI notes that agent-level input and output guardrails do not necessarily cover every tool in multi-agent workflows. A check at the relevant boundary is more reliable than assuming a check elsewhere in the workflow will see every call.
- Reject malformed or out-of-scope targets and arguments.
- Check that the current identity has authority for the requested operation.
- Require approval when the action crosses a defined consequence threshold.
- Ensure a rejected or failed check prevents the side effect, rather than merely generating a warning.
Automatic validation and human review serve different purposes. A validator can enforce known rules consistently; a reviewer can judge context or intent that a rule cannot resolve. Neither replaces the other.
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Use dry runs and plans to make review useful
A dry run or plan preview can show intended changes before a multi-step task begins. It is useful only if the displayed plan corresponds to what the system will execute and the agent can be stopped or redirected before consequential actions occur.
Anthropic describes Plan Mode as a way to show a plan for review and editing before actions happen, with the ability to intervene during execution. Anthropic’s article on trustworthy agents also frames agents as models that direct their own processes and tool use toward a task, rather than following only a fixed script. That autonomy is why a preview should be treated as a checkpoint—not as proof that later tool calls will match the plan.
Make the preview actionable
- Show the sequence of meaningful actions and the resources each will affect.
- Explain material changes in concrete terms, not just “update data” or “send message.”
- Let the operator edit, reject, or narrow the plan before execution where the environment supports it.
- Revalidate each actual tool call, and ask again if it materially differs from what was reviewed.
- Allow intervention during execution when later steps depend on new information or create a new risk.
Plan-level review can reduce a barrage of prompts for routine steps, but it should not silently authorize an action whose target, scope, or consequence changes along the way.
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Match review frequency to risk, not habit
Requiring approval for every harmless operation can train people to click through without reading. NIST warns that repeated requests can habituate users into thoughtless approvals, weakening the accountability human review is supposed to provide. In NIST’s words, “When users become habituated to thoughtlessly approving access requests, HITL undermines the non-repudiation and accountability it was meant to provide.” See NIST’s agent identity article.
Use a risk-based policy: permit routine, bounded, reversible actions under narrow authority; require a plan or batch review for related changes; and pause for explicit approval before high-consequence, externally visible, difficult-to-reverse, or ambiguous actions. The exact thresholds depend on the system and task; the sources do not establish a universal approval policy.
OpenAI reported roughly 200 times fewer human-approval stops in Auto-review than in manual approval mode in a 2026 Codex-session comparison. In an illustrative internal snapshot, 720 out-of-sandbox actions would have interrupted users in manual mode; Auto-review reviewed them, rejected seven, and four of those continued by a safer path while three stopped for user input. OpenAI also reported that around 99% of the small fraction requiring review were approved in that described internal deployment. These are context-specific figures, not general benchmarks, safety rates, or evidence that fewer prompts alone make an agent safer; OpenAI says outcomes depend on use case, environment, and sandbox configuration. OpenAI’s Auto-review report.
Compare the control patterns
| Control | When it acts | What it can enforce | Main limitation |
|---|---|---|---|
| Model instruction to ask first | Before a tool call, if the model follows the instruction | Nothing by itself outside the model’s behavior | The tool may still be callable without an enforced decision. |
| Argument and policy validation | At or near the tool call | Known rules for identity, target, scope, and arguments | Rules may not capture contextual intent or every harmful action within allowed scope. |
| Dry run or plan review | Before a batch or multi-step execution | A chance to inspect and edit proposed work | Actual calls can diverge; tool-boundary checks and intervention remain necessary. |
| Enforced approval interruption | Before a selected side effect | A pause until an explicit approve or reject decision is resolved | Review quality depends on context, policy, reviewer attention, and what the action can access. |
| Audit or alert after execution | After the action | Detection and accountability evidence | Cannot prevent a side effect that has already occurred. |
A practical implementation sequence
- Define authority: identify the agent, task, permitted tools, resources, and operations. Grant the narrowest workable access.
- Isolate execution: run agent-generated code in a restricted environment; limit network egress and keep application credentials in a trusted broker or service.
- Validate at the boundary: check each side-effecting call’s identity, target, operation, and arguments before execution.
- Preview meaningful work: provide a plan or dry run for multi-step changes and let the operator revise it where possible.
- Interrupt consequential actions: present the exact proposed action and relevant arguments, then block execution until the approval is resolved.
- Define failure behavior: specify rejection, timeout, reviewer unavailability, and changed arguments; fail closed where the risk policy requires it.
- Test the enforcement path: verify that rejection, missing approval, invalid arguments, and scope violations actually prevent the tool from producing a side effect.
What these controls cannot guarantee
Approval gates, plans, guardrails, and sandboxes are layers, not guarantees. An allowed action can still be unsafe; a reviewer can misunderstand a proposal; prompt injection can influence a model; and vulnerabilities or overly broad credentials in tools and environments can undermine the boundary. Anthropic cautions that safeguards are not a guarantee and identifies risks across tools, harnesses, environments, and prompt injection. OpenAI’s SDK documentation likewise leaves review and enforcement to the application’s own harness. Anthropic; OpenAI.
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