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To stop an AI agent before it changes something consequential, require human approval at the tool call that would create the side effect. First inventory what each tool can actually do, then define which effects require review, show reviewers the target and parsed arguments, and make sure a pending call cannot run before a decision. Automatic checks can catch malformed or disallowed requests; they do not replace a person deciding whether a sensitive action should happen.
What does an approval rule do?
An approval rule pauses an agent’s proposed operation before a tool performs it, so a person can approve or reject the call. It is an execution control: the review must happen at the point where the agent is about to use a tool that can change data, send something, spend money, or otherwise cause an external effect.
This is different from asking a person to review an agent’s final answer after the operation has already happened. It is also different from an automatic guardrail, which checks whether input, output, or tool behavior meets programmed conditions. OpenAI’s Guardrails and human review guide draws that distinction directly: use guardrails for automatic checks and human review for approval decisions.
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Which AI agent actions should require human approval?
Require review when a tool can cause a sensitive, costly, difficult-to-reverse, or high-stakes effect. The right trigger depends on what the action does in your application; official guidance gives examples such as order cancellations, large refunds, and payments, but does not establish a universal dollar amount or risk score.
| Effect of the call | Default rule to consider | What to inspect before deciding |
|---|---|---|
| Read-only lookup with no sensitive exposure | May be allowed without human review if access is otherwise appropriate. | Whether the tool can reveal sensitive information or reach more systems than its name suggests. |
| Change customer or production data | Require approval when the change has meaningful impact or is hard to reverse. | Target record, fields or settings to change, and the proposed values. |
| Send an external message | Require approval if sending could make a commitment, disclose information, or affect a customer or third party. | Recipient, message content, attachments, and whether the action sends immediately. |
| Payment, refund, or other money movement | Require approval based on the application’s financial risk policy. | Amount, currency if applicable, recipient or order, and the reason for the transaction. |
| Cancel, delete, or revoke | Require approval when the action is consequential or difficult to undo. | Exactly what will be cancelled, deleted, or revoked, and the scope of the effect. |
| Access changes or shell commands | Require review when the action can expand access, affect a system, or execute consequential operations. | Principal and permissions being changed, or the command and its execution context. |
| Sensitive MCP action | Keep approval enabled for sensitive operations exposed through MCP. | Server, resource, operation, and arguments the tool call will use. |
These are decision prompts, not a universal classification standard. Label a tool by its actual effects, not its friendly name: a tool called “update” might trigger an external action, while a tool called “lookup” may expose sensitive data. Record whether each effect is reversible, what systems it can reach, and the likely cost of an incorrect call before choosing a review rule.
How do I write approval triggers for my application?
Turn the action inventory into explicit, testable rules. A reviewer and an engineer should be able to look at the proposed call and agree whether it must pause, rather than interpreting a vague instruction such as “review risky actions.”
1. Inventory the tools and their effects
For every callable tool, document what it reads, what it can change, which systems it reaches, and whether the result can be reversed. Include tools exposed through handoffs, nested agents, and MCP servers—not just the functions directly listed beside the first agent.
Describe effects in concrete terms. For example, “changes a customer’s subscription status” is more useful than “updates account.” If the tool performs more than one kind of operation, list those separately; a read action and a cancellation action may need different rules even if the same tool supports both.
2. Define the conditions that require review
Write triggers around effects: changing customer or production data, spending or moving money, sending a message externally, cancelling or deleting something, changing access, running shell commands, or operating on sensitive MCP resources. Then specify any application-specific conditions that make an action more or less consequential.
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Do not invent a threshold simply because a rule seems easier to configure with a number. OpenAI’s cited approval guidance does not set a general payment or refund amount, a standard risk score, or a universal definition of “large.” If your organization uses a threshold, decide it from your own impact, reversibility, and operating requirements, and document the rationale.
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3. Separate automatic checks from the approval decision
Use automatic validation to reject malformed requests, disallowed values, or tool arguments that fail your application’s rules. Where appropriate, validate tool results as well. Put a human decision in the flow when the question is whether the proposed operation should happen—not merely whether it has a valid shape.
This separation makes the policy easier to understand. A validator might establish that an amount is present and formatted correctly; an authorized reviewer decides whether that payment to that recipient should proceed. Passing a validator is not permission to perform a sensitive action.
4. Attach the rule where the effect occurs
Apply approval to each tool call that can create the relevant side effect. An input check at the start of a run or an output check at the end is not a substitute for controlling a tool call in the middle. In OpenAI’s documented setup, input guardrails cover the first agent, output guardrails cover the final agent, and tool guardrails apply only to the function tools to which they are attached.
Trace the complete workflow: handoffs, nested agents, shell tools, and MCP calls can all be paths to an operation. Confirm that the approval policy follows the call to the boundary where it can actually change something. If another agent or service executes the action, do not assume the first agent’s checks automatically govern it.
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A call awaiting approval must not execute while the decision is pending. The reviewer needs enough information to decide, the application needs to record approve or reject, and the original paused run should continue only after that decision is applied.
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What the reviewer should see
Show the proposed operation, the target, and the relevant parsed arguments in a form a reviewer can inspect. For a data edit, that means making clear which record and values are involved; for a message, the recipient and content; for a payment, the amount and recipient or order. These are implementation recommendations, not a prescribed universal review interface.
Avoid presenting only a generic tool name or a vague summary. The reviewer needs to understand the actual request, including any important scope, before choosing. Make the reject path available and give the agent a useful rejection reason if the workflow can safely use it to revise its proposal. The reason should not itself authorize a different operation.
What the OpenAI Agents SDK flow looks like
In the OpenAI Agents SDK, a tool can be configured to require approval. When the agent reaches that call, the SDK pauses the run and returns a pending interruption rather than executing the call. The application can present it for review, record an approval or rejection, then resume the same run state.
The documented model is important because it preserves the relationship between the proposed action and the decision: the call is pending, a decision is made, and the paused run is resumed. OpenAI’s Agents SDK Human-in-the-loop documentation describes this interruption-and-resume pattern for JavaScript and Python. Exact APIs and configuration labels can change, so use the current official guide for the language and SDK version in your application.
What if review is delayed?
If the reviewer may not respond immediately, persist the paused run state so the application can resume it after a decision. Do not silently start a fresh agent turn and treat it as equivalent to the pending call; preserve the original pending operation and its context. Decide how rejection, timeout, and an unavailable reviewer should behave for your application, because the cited guidance does not establish universal timeout durations or failure policies.
How should approval work across agents, handoffs, and MCP?
Approval coverage must include the tools that can act after a handoff and within nested workflows. A top-level agent’s input and output checks do not necessarily surround every downstream tool call, so trace which agent or service can ultimately invoke each side-effecting operation.
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OpenAI’s safety guidance recommends keeping approvals enabled for MCP tools and treating untrusted external text as a possible source of unsafe tool behavior. Text from a message, retrieved document, or other outside source can influence what an agent proposes; do not let that content bypass the same action-level policy applied to other tool calls.
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How do I test approval rules before relying on them?
Test both calls that should proceed and calls that must be blocked pending review. The goal is to show that the rule holds at the execution boundary, including when inputs are malformed, a reviewer rejects a call, or the action is reached through a delegated path.
- Test an allowed case. Confirm the intended low-risk call follows its allowed path and does not accidentally invoke a separate side effect.
- Test a triggered case. Submit a call that meets a written approval condition and verify that the operation remains pending rather than executing.
- Inspect the review context. Confirm the reviewer can see the proposed action, target, and relevant parsed arguments needed for a decision.
- Test approval and rejection. Verify that approval resumes the pending run as intended and rejection does not execute the tool. Check that the rejection outcome is handled safely.
- Test malformed or uninspectable arguments. OpenAI’s SDK documentation says conditional approval flows require manual approval when arguments are malformed or cannot be inspected. Confirm your implementation does not treat uncertainty as permission.
- Test delayed and unavailable review. Exercise the timeout or unavailable-reviewer behavior you have chosen. Ensure a paused action does not run merely because the review path failed.
- Test every route to the effect. Include handoffs, nested agents, shell tools, and MCP calls that can reach the operation, plus any path that might execute without passing the approval check.
OpenAI’s safety guidance cautions that guardrails alone are not foolproof and recommends combining controls. Treat test results as evidence about the paths you exercised, not proof that all possible failures have been removed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I compare approval features in agent runtimes?
Compare implementations by how reliably they enforce the pause and how well they handle your workflow, rather than by the presence of an “approval” label alone. OpenAI’s official materials describe its own SDK and safety guidance; they do not establish a cross-framework standard or independently compare all runtimes.
| Question to evaluate | Why it matters | What to verify |
|---|---|---|
| Can approval be configured per tool or per call? | Different calls from one tool may have different effects. | Whether the configuration can express the distinctions your policy needs. |
| Is execution prevented until a decision? | A review notification is not useful if the side effect has already occurred. | That the pending call cannot execute before approval. |
| Does coverage include nested agents, handoffs, shell tools, and MCP? | Side effects may occur downstream of the first agent. | Which tool boundaries the control actually covers. |
| Can paused state be persisted and resumed? | Human review may take longer than one uninterrupted run. | How the original run is preserved and resumed after a decision. |
| What arguments can a reviewer inspect? | Approval requires understanding the target and proposed operation. | Whether relevant parsed arguments are available to the application. |
| What happens on rejection, timeout, or unavailable review? | Failure behavior can determine whether an operation is blocked or accidentally allowed. | Whether the behavior is explicit and can be tested. |
These are evaluation criteria, not claims that every runtime supports the same controls. Check the current official documentation for the runtime, language, and version you intend to use before relying on a particular configuration or state API.
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What limits should I account for?
Official implementation guidance does not establish universal approval thresholds, timeout durations, staffing levels, or a standard that applies across frameworks. Those decisions depend on the consequences and reversibility of the operation and the way your application is operated.
Approval also cannot compensate for a policy that omits a side-effecting tool, a reviewer who cannot see what is being proposed, or a run path that bypasses the check. Use a documented action inventory, clear trigger conditions, automatic validation, and tests of both normal and failure paths. Treat the controls as layered safeguards rather than a promise of risk-free behavior.
Frequently Asked Questions
Can an AI agent continue working while a tool call waits for approval?
That depends on the runtime and how your application handles the paused run. Keep any other work from indirectly performing the same pending side effect, and ensure approval is tied to the specific proposed operation rather than a general permission.
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Set an expiration or escalation policy based on how long the proposed action remains safe and relevant. The official guidance does not prescribe a universal timeout; when review is unavailable, define a fail-safe outcome rather than letting the call proceed by default.
What should an approval audit record include?
As an implementation practice, retain enough context to reconstruct what was proposed and what decision was made: the action and target, relevant arguments, decision, reviewer, and time. Protect sensitive data in those records and follow the retention and access requirements that apply to your application.
Can one approval authorize several actions in an agent workflow?
Only if your policy deliberately defines the approval scope and the reviewer can see that scope before deciding. A general approval of a plan should not implicitly authorize later operations with different targets or consequences.
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