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To stop an AI agent from making a consequential change before a person reviews it, require approval at the tool call that would cause the change. First inventory what each tool can actually do; then set explicit approval triggers, show reviewers the proposed action and arguments, and ensure a pending call cannot execute until it is approved. For finance-related workflows, that can mean a person must approve a payment, refund, account change, or external message before the agent proceeds.
Automatic checks and human approval do different jobs: checks can reject malformed or disallowed requests, while a person decides whether a sensitive operation should happen. OpenAI’s guidance describes this distinction and, in its Agents SDK, a pause-and-resume process for tool calls requiring approval. The details below focus on designing the policy; SDK APIs and other runtimes can change, so verify current official documentation before implementation.
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What does an approval rule do for an AI agent?
An approval rule prevents a selected tool call from executing until a person has reviewed it and approved it. The rule belongs at the point where the agent is about to affect an external system, not just at the start or end of a conversation.
For example, an agent might be allowed to look up an invoice and draft a payment instruction automatically, while the tool that submits the payment requires approval. The distinction is between preparing a proposed action and carrying it out. A rule that only checks the user’s initial request or the agent’s final response may not stop a tool from changing data midway through a workflow.
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OpenAI’s Guardrails and human review guidance distinguishes automatic validation from human approval decisions. Its Agents SDK documentation describes a tool call that requires approval as an interruption: the run pauses, returns pending interruptions, and can later resume from the same run state. Treat these as documented OpenAI patterns, not a universal standard for all agent frameworks.
Which AI agent actions should require human approval?
Require review when a tool can create a sensitive, costly, difficult-to-reverse, or high-stakes effect. There is no universal dollar threshold or risk score established by the cited guidance, so set triggers around what the action does and the consequences of getting it wrong.
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| Action category | Examples to consider gating | Why review may matter |
|---|---|---|
| Payments and money movement | Submitting a payment, transferring funds, changing payment details, or issuing a substantial refund | An incorrect or unauthorized action may move money or be difficult to reverse. OpenAI’s practical guide gives payments and large refunds as examples of actions that warrant oversight. |
| Account and customer records | Changing customer or production data, closing an account, or editing a financial record | A mistaken edit can affect service, records, or later decisions. Classify the specific effect rather than relying on a tool name such as “update.” |
| Cancellation, deletion, and access | Canceling an order or service, deleting records, changing permissions, or disabling access | These actions can have consequences beyond the agent’s current session and may not be readily reversible. Order cancellations are an example in OpenAI’s guidance. |
| External communications | Sending a customer notice, payment request, or message to a bank or vendor | A sent message can create confusion or obligations even if the underlying record is unchanged. |
| Code and infrastructure | Running shell commands or changing production settings | A command can affect files, services, or data beyond the narrow task the agent appears to be performing. |
| Sensitive integrations | Calling an MCP tool that can read or change sensitive resources | Review should apply to the actual capability exposed by the integration, including tools reached through delegation. |
These are examples, not a requirement to interrupt every low-impact operation. A read-only lookup may not need the same treatment as a tool that commits a payment. Conversely, a tool with a harmless-sounding label may trigger a consequential downstream action. Record the actual side effects, reachable systems, reversibility, and likely cost of an incorrect call before deciding.
How do I set approval rules before an agent can make changes?
Build the policy around tool effects and enforce it for every side-effecting call. The sequence below turns a broad instruction such as “ask a human before important actions” into a reviewable implementation plan.
1. Inventory tools by capability
List every tool an agent can call, including tools exposed by delegated agents, handoffs, shell access, and MCP servers. For each one, note whether it reads, drafts, writes, sends, spends, deletes, changes access, or triggers another system. Also record the target system, whether the action can be undone, and what an incorrect call could cost.
Assess capabilities, not labels. A function called update_status might only change an internal draft, or it might finalize a transaction that triggers an external process. Those should not automatically share the same approval rule.
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2. Define explicit triggers
Write down which effects require approval, such as any payment submission, externally sent message, customer-data edit, cancellation, deletion, permission change, shell command, or operation on a sensitive MCP resource. Tailor this list to the application and its consequences.
If a policy uses a monetary limit, specify who sets it, what actions it covers, and how exceptions work. The official guidance supplies qualitative examples, including large refunds, but does not establish a universal amount. Do not present a locally chosen limit as an industry-wide safe threshold.
3. Separate automatic checks from approval
Use automatic validation to reject malformed input, disallowed targets, or tool arguments outside the permitted structure. Where appropriate, validate tool results as well. Then use a human review step to decide whether the particular proposed operation should proceed.
Passing an automatic check is not an approval. A well-formed request can still be unauthorized, unnecessary, or too risky. Likewise, human review is not a substitute for basic validation: reviewers should not be asked to catch every malformed argument or policy violation by eye.
4. Enforce the rule at each side-effecting tool
Attach the approval requirement to each tool call that can cause a change, including calls made after a handoff or inside a nested agent. Do not assume an agent-level input or output check covers every tool in a chain.
OpenAI’s guardrails guidance describes specific boundaries: input guardrails apply to the first agent, output guardrails to the final agent, and tool guardrails to the function tools where they are attached. That means a check at one boundary should not be treated as a blanket control over tools elsewhere in a multi-agent workflow.
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5. Give reviewers decision-grade context
Show the proposed operation, the target, and the relevant parsed arguments. For a financial action, a reviewer may need to see what account or transaction is affected, the amount and currency if applicable, the recipient, and the reason the agent gives. Display only the information needed for the decision and follow the application’s privacy and access controls.
Provide clear approve and reject choices. A rejection should have a useful reason that can be recorded and, where appropriate, returned to the agent so it can stop or propose a safer alternative. The documentation describes approve/reject handling but does not prescribe a universal review interface or list of fields; design those for the action and your users.
6. Make the pending state a genuine pause
While an action is pending, the tool must not execute. If review is delayed, retain the paused run state and decision context; after the reviewer decides, resume that same run rather than silently starting a fresh turn that may produce a different action.
For OpenAI’s Agents SDK, the documented pattern is that a tool requiring approval pauses the run and returns interruptions; the application can later resume from the same RunState. The JavaScript and Python guides describe approval flows, but API details are runtime- and version-specific. Confirm the current guide for the language and SDK version you are deploying.
7. Define rejection, timeout, and reviewer-unavailable behavior
Decide in advance what the agent and application do when a reviewer rejects a call, cannot be reached, or does not respond in the application’s chosen time window. For a consequential action, a conservative policy is to leave the action unexecuted unless an authorized reviewer approves it; provide a human escalation route if work must continue.
The cited sources do not set a universal timeout duration, staffing model, or cross-framework failure policy. Choose these according to the impact of the action and operational requirements, and document the reason. Do not let a timeout silently become approval unless that behavior has been explicitly assessed and authorized for the particular action.
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8. Test the approval boundary and its bypasses
Test both permitted and blocked calls before relying on the policy. Include ordinary approval, rejection, malformed arguments, missing reviewer, delayed review, delegated tools, and paths that might call the underlying system without going through the approval check.
OpenAI’s SDK guidance notes that malformed or uninspectable arguments in conditional approval flows require manual approval. Its safety guidance also warns that guardrails alone are not foolproof. Treat testing as implementation practice, not proof that every failure mode has been eliminated.
How should approval work in a finance-related agent workflow?
Separate research and preparation from commitment. An agent can often gather information, calculate a proposed amount, or draft an instruction without the same level of oversight as the tool that submits a payment, issues a refund, changes account details, or sends the final message.
Example: payment preparation versus submission
- Allow the agent to gather permitted facts. For example, it may read an invoice or prepare a draft using authorized, read-only tools.
- Validate the proposal automatically. Check required fields and whether the proposed target and arguments conform to your application’s rules. A validation pass does not authorize spending.
- Pause before the action that commits money. Require a human decision on the actual payment tool call, not merely on the user’s initial request or a summary after the call.
- Show the reviewer the material details. Present the target, amount, relevant arguments, and stated purpose so the decision is about the action that will execute.
- Execute only after approval and record the outcome. If rejected, keep the payment unsubmitted and follow the defined rejection path. If approved, resume the paused workflow and verify the result through the appropriate application process.
This illustrates where to put the gate; it is not a claim that every payment system or agent runtime uses the same APIs. Map the sequence to the tools and transaction model in the system being deployed.
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Example: refund or cancellation
A refund tool and a cancellation tool may affect different records or have different reversibility, even when they appear in one customer-support workflow. Set rules for each action, and make the reviewer see which transaction or service is targeted. If a tool combines cancellation and refund, assess both effects rather than treating the combined call as a single generic update.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I compare approval support across agent runtimes?
Compare behavior at the execution boundary, not just whether a product advertises “human in the loop.” The official material cited here covers OpenAI tools and SDKs; it does not establish a cross-platform ranking or universal standard.
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| Question to verify | Why it matters | What to confirm in the runtime documentation and implementation |
|---|---|---|
| Can approval be configured per tool or per call? | Different calls to the same general capability may have different effects. | Whether the rule is attached to a tool, selected conditionally, or supported through another mechanism. |
| Can a pending call execute before approval? | A review screen is ineffective if the side effect has already occurred. | That the run is paused at the call boundary and the tool has not run while approval is pending. |
| Does coverage include handoffs and nested tools? | A delegated agent can reach a capability that the top-level policy overlooks. | How policies apply to downstream agents, shell tools, MCP tools, and other integrations. |
| Can paused state be persisted and resumed? | Review may take longer than a single request or process lifetime. | How the state is stored, resumed, and associated with the original pending action. |
| What does the reviewer see? | The reviewer needs enough context to identify the target and consequences. | Whether the proposed operation and parsed arguments are available to the application’s review flow. |
| What happens on rejection or review failure? | Unclear failure behavior can create unsafe or confusing outcomes. | How the runtime represents rejection, malformed arguments, timeouts, and unavailable reviewers; no universal policy is stated in the cited sources. |
Verify the actual version and language implementation you intend to deploy. OpenAI’s JavaScript and Python approval guides and Agents SDK human-in-the-loop documentation are useful starting points for that product’s behavior, but exact APIs may change.
What mistakes make approval rules ineffective?
The most common design flaw is treating a policy label as enforcement. A rule is useful only if the call that can cause the side effect cannot bypass it.
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- Approving too early: A person approves the request in general, but the agent later changes the target or arguments. Put the decision next to the actual tool call and show the specific proposed arguments.
- Approving too late: A tool runs and the application asks for review afterward. That is notification or audit, not a pre-execution approval gate.
- Relying only on the first or last agent: Intermediate tools can still act. Apply checks at the tool boundary, including in delegated workflows.
- Using names instead of effects: A tool called “draft” may send externally, while a tool called “update” may only alter a reversible internal field. Inventory behavior.
- Conflating validation and consent: Valid syntax does not mean the operation is appropriate or authorized. Keep automatic checks and human decisions distinct.
- Ignoring MCP or untrusted content: Tools reached through integrations and external text can influence an agent’s behavior. OpenAI’s safety guide recommends keeping approvals enabled for MCP tools and treating untrusted external text as a possible source of unsafe tool behavior.
- Failing open without a deliberate decision: If a reviewer is unavailable, a timeout or service fault should not accidentally trigger the side effect. Define the failure path explicitly.
What is known—and not established—about approval thresholds?
OpenAI’s official guides recommend oversight for sensitive, irreversible, or high-stakes actions and provide examples such as order cancellations, large refunds, and payments. They do not publish a universal dollar amount, numeric risk score, reviewer staffing ratio, timeout, or framework-wide standard.
Accordingly, set local triggers based on impact, reversibility, and operating requirements, and document why the rule applies. The cited official documentation was researched on October 25, 2026; agent SDK interfaces and safety guidance can change, so check the current OpenAI documentation and the documentation for any other runtime you use.
Frequently Asked Questions
Can an AI agent request approval from a person in the middle of a task?
Yes, when the runtime supports an interrupt-and-resume workflow. In the OpenAI Agents SDK, the documented flow returns pending interruptions and allows the application to continue the same run after a decision.
Should I approve the agent’s whole task once, or each sensitive action?
A broad task approval can be insufficient if the agent later chooses a different target or operation. The useful approval unit is the specific consequential action and its relevant arguments.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhat if a reviewer rejects an AI agent’s proposed payment?
The application should preserve the rejection as a decision and keep the proposed payment from executing. Whether the agent may suggest a revised action is a separate policy choice; the revised proposal should be evaluated on its own merits.
Do guardrails guarantee that an AI agent cannot make an unsafe change?
No. OpenAI’s safety guidance cautions that guardrails are not foolproof, which is why implementation should test enforcement paths and combine automated validation with appropriate human review.
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