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This guide lays out a practical design: classify every tool, assign one of four gates, enforce least privilege and deterministic deny rules, persist drafts and decisions, resume durable runs after approval or rejection, and monitor the system for drift and bypass attempts.
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What human-in-the-loop approval actually controls
An autonomous agent runs a self-directed loop: it plans, calls tools, observes results, adjusts its plan, and repeats until the task is complete or it needs human input. Approval is the boundary between deciding what to do and causing an external effect.
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Approval is not the same as asking a user an occasional conversational question. A production control must identify the exact operation, preserve the state of the run, record who decided and why, and prevent the side effect until the decision is valid.
Start with an action and consequence inventory
List every operation the agent can perform, including indirect effects through handoffs or nested agents. For each tool, record what can go wrong, who is affected, whether the result can be undone, and what information is involved.
| Action example | Potential consequence | Reversibility | Typical starting gate |
|---|---|---|---|
| Read a public knowledge base | Usually limited to incorrect context | Reversible | Notify or no interactive gate, with logging |
| Create an email draft | Incorrect wording or figures prepared for later use | Reversible while still a draft | Draft |
| Send an email to a customer or employee | Reputational, privacy, contractual, or operational impact | Hard to reverse | Confirm or mandatory review, depending on content |
| Rename or edit a local file | Data loss, broken build, or unintended disclosure | Sometimes reversible | Confirm; mandatory review for critical repositories |
| Delete records | Permanent loss and possible compliance exposure | Often irreversible | Mandatory review |
| Submit a purchase, refund, or transfer | Direct financial loss or fraud exposure | Hard to reverse | Confirm or mandatory review with a named approver |
| Call an external API that changes state | Third-party side effects, quota use, or data disclosure | Depends on the API | Confirm or mandatory review |
| Read sensitive customer, health, legal, or employment data | Privacy and access-control exposure | Not reversible once disclosed | Mandatory review or a tightly scoped policy gate |
Classify consequences, not just tool names. A read operation can be high risk if it returns regulated information, while a write can be low risk inside an isolated sandbox. Microsoft’s guidance is to require review for actions that are difficult to reverse or affect people, money, or compliance, and to route sensitive or ambiguous cases to a person.
Choose one of four approval gates for each action
Use the smallest control that matches the consequence. Making every tool mandatory-review creates alert fatigue; making every tool automatic removes meaningful oversight.
| Gate | When to use it | What the agent may do | What the reviewer receives |
|---|---|---|---|
| Notify | Low-value, reversible actions where awareness is useful but a stop is disproportionate | Execute, then emit a notice and audit event | Action summary, result, and link to logs |
| Confirm | Moderate-consequence actions with a clear, authorized intent | Prepare the call, then wait for an explicit approval | Tool name, arguments, evidence, expected effect, and approve/reject controls |
| Draft | Content that carries an organization’s voice, commitments, or numbers | Write to a draft state in the destination system; do not publish or send | Rendered draft, source records, changes from any previous version, and recipient or target |
| Mandatory review | Clinical, legal, financial, safety, irreversible, or otherwise regulated decisions | Do not execute until a named qualified role signs off | Full context, evidence, confidence where available, policy basis, diff, identity of approver, and rationale |
Make the mapping explicit in policy code or configuration. Do not let the model choose to downgrade its own gate. A deterministic policy service should calculate the required gate from the tool, arguments, user role, target, amount, data classification, and environment.
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Define the reviewer contract
A reviewer can make a reliable decision only when the proposed operation is small, concrete, and inspectable. Treat the approval record as a contract between the agent and the person who authorizes it.
- Exact identity: show the tool name, version or identity, operation, and every argument that will be sent.
- Target: identify the account, recipients, file path, record set, environment, or external service.
- Evidence: show the source documents, records, calculations, and retrieval timestamps used to form the proposal.
- Confidence and uncertainty: expose model confidence when available, but never treat a confidence score as a permission substitute.
- Before and after: render a textual or structured diff for files, records, messages, configuration, and amounts.
- Consequence: state what will happen on approval, whether it can be undone, and any estimated cost or recipient impact.
- Scope: keep one review unit small enough to read. Grouping unrelated actions into one approval hides risk and encourages rubber-stamping.
- Decision fields: capture approver identity, role, timestamp, decision, rationale, policy version, and tool-call identifier.
For content, persist the item as a draft in the destination system itself. A draft email in the mail system, a pull request in the code host, or a pending payment in the finance system remains visibly unreviewed if the chat session disappears. A status flag shown only in the agent interface is not durable control.
Build the approval path before enabling tools
- Define risk tiers and approver roles. Name who may approve each class of action. Separate the person who requests work from the person who can authorize a payment, production change, clinical instruction, or legal commitment.
- Issue least-privilege credentials. Give the agent only the tools, data scopes, and operations needed for its task. Use separate credentials for read, draft, and commit operations; deny all other operations by default.
- Add deterministic deny rules. Block prohibited recipients, paths, amounts, data classes, environments, and API methods in code or policy infrastructure. These checks must not depend on the model following an instruction.
- Attach a gate to every side-effecting tool. Use Notify, Confirm, Draft, or Mandatory review as a declared policy outcome. Treat missing policy as a stop condition, not as permission.
- Generate a review packet. Before execution, materialize the arguments, evidence, confidence, diff, target, and predicted consequence into a durable approval record.
- Pause before the side effect. The runner must return an interruption or pending state without invoking the external operation.
- Resolve exactly once. Accept, reject, or let the request expire. Bind the decision to the specific tool identity, arguments, run, and policy version so an approval cannot be replayed against a changed request.
- Resume from durable state. Reload the original run state, apply the decision, and continue the same top-level run. Do not reconstruct intent from a summary message.
- Write an audit event. Store the proposal, decision, evidence references, approver, timestamps, retries, and resulting side effect in an append-only or otherwise protected log.
Implementing pauses with the OpenAI Agents SDK
In the OpenAI Agents SDK, a tool can be marked needs_approval=True for an always-gated operation, or supplied with a callable policy for conditional approval. When the policy requires review, the runner emits an interruption instead of executing the tool. API names and availability can change with SDK releases, so verify the current SDK reference before shipping.
A minimal pattern looks like this:
send_invoice = FunctionTool(
name="send_invoice",
description="Send an invoice to an approved customer",
params_json_schema=invoice_schema,
on_invoke_tool=send_invoice_impl,
needs_approval=True,
)
result = Runner.run(agent, input=user_request)
if result.interruptions:
for interruption in result.interruptions:
show_to_reviewer(interruption)
# The application waits; no invoice has been sent.
# After an authorized decision, update the durable run state:
state.approve(interruption)
# or:
state.reject(interruption, reason="Amount exceeds the delegated limit")
resumed = Runner.run(agent, run_state=state)The important property is sequencing: the interruption occurs before send_invoice_impl runs. Your application should display the interruption, collect the decision, call state.approve(...) or state.reject(...), and resume the original top-level run with the updated RunState. Keep the state in durable storage if approval may take minutes, hours, or days.
Approval can be sticky for the same tool identity when explicitly configured. Use that sparingly. A sticky decision may reduce repetitive prompts for a tightly bounded operation, but it must not silently cover changed arguments, a new target, a different environment, or a higher amount.
Design the approval interface
The reviewer screen should make the safe decision the easy decision. Put the proposed effect first, then the evidence needed to verify it.
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- Show a clear title such as “Send 1,248.00 GBP refund to account 4821,” not “Tool call pending.”
- Display all arguments in both human-readable form and an expandable structured view.
- Separate facts retrieved from systems, model-generated interpretation, and assumptions.
- Render file, record, message, and configuration diffs with additions, deletions, and unchanged context.
- Show the policy rule that triggered review and the role required to approve it.
- Provide Approve, Reject, and Escalate actions; require a reason for rejection or escalation.
- Make expiration and timeout behavior visible. An unanswered request should fail closed for consequential actions.
- Prevent an approver from editing hidden arguments. If a change is needed, reject the proposal and make the agent create a new one.
For asynchronous review, send a link or queue item containing an opaque approval identifier, not credentials or sensitive data in a notification. Re-authenticate the reviewer when the decision is opened, check that the role is still valid, and invalidate the request if its arguments, evidence, target, or policy version changed.
Handle nested agents and handoffs
Multi-agent systems must preserve approval semantics across delegation. A research agent may hand a task to a purchasing agent; an orchestration agent may call another agent through Agent.as_tool(). If the inner agent can trigger a side effect, its approval request must surface on the outer run rather than bypassing the application’s review surface.
The OpenAI Agents SDK surfaces approval requests from handoffs and nested Agent.as_tool() calls on the outer run. Use one approval queue and one audit trail, while retaining the identity of the inner agent that proposed the operation. The record should include the complete chain: outer run, handoff, inner agent, tool identity, arguments, and final decision.
Do not allow a handoff to change the required gate. A delegated agent may narrow scope, add evidence, or prepare a draft; it may not turn Mandatory review into Notify. If an inner agent rejects a request, the outer agent should receive a structured rejection and either revise the proposal or stop, rather than repeatedly retrying the same call.
Stop safely when intent or policy is unclear
Ambiguity is itself a reason to pause. If the agent cannot determine which policy applies, who the recipient is, whether a spending cap is per transaction or per day, or whether a file is production-critical, it should ask for clarification or escalate to a qualified person.
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For example, an expense agent should stop when an amount exceeds a known cap but the applicable exception policy is unknown. It should present the amount, policy conflict, evidence, and choices; it should not infer an exception from conversational pressure.
- Fail closed when a required policy lookup is unavailable.
- Reject malformed, incomplete, or extra arguments before showing an approval.
- Require a fresh review after material changes to the proposal.
- Cap retries and repeated clarification loops.
- Provide an emergency stop that disables side-effecting credentials or queues new approvals for manual handling.
Enforce least privilege outside the model
Prompt instructions are useful but are not a security boundary. Use credential scopes, network egress controls, filesystem permissions, API allowlists, amount limits, and environment separation to constrain what an agent can do even if it is manipulated or mistaken.
A practical separation is read, prepare, and commit:
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- Prepare: create drafts, patches, simulations, or pending transactions without publishing them.
- Commit: a separate operation available only after the required gate and policy checks succeed.
Keep production credentials out of exploratory agents. Run code changes in a sandbox, require a reviewed pull request before merge, and block direct production writes. For external APIs, allow only named methods and approved hosts; log request and response metadata without copying secrets into the approval record.
Monitor approvals as an operating control
Approval workflows degrade as models, data, users, integrations, and regulations change. Track both safety outcomes and usability so you can detect silent bypasses or reviewer fatigue.
| Metric | What it can reveal | Useful breakdowns |
|---|---|---|
| Escalation rate | How often the agent cannot safely proceed | Tool, policy, model, user role, and workflow |
| Approval and rejection rates | Whether proposals are trusted, over-blocked, or routinely unsafe | Gate, approver role, risk tier, and reason |
| Review latency | Queue bottlenecks and tasks waiting too long | Business hours, severity, and reviewer group |
| Override rate | How often people bypass or alter normal controls | Override reason, tool, and policy version |
| Repeated bypass attempts | Prompt manipulation, faulty retry logic, or a compromised integration | Agent, session, target, and denied rule |
| Complaint and incident rate | Real-world harm not visible in approval statistics | Recipient, customer segment, data class, and outcome |
| Groundedness and evidence quality | Whether proposed actions are supported by current records | Source system, retrieval age, and task type |
Review samples of both approved and rejected calls. A high approval rate is not proof of safety if reviewers cannot see the relevant evidence or are approving too many bundled actions.
Test the failure modes deliberately
Test the complete pause, decision, and resume lifecycle in staging and again after changes to models, tools, policies, or integrations.
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- Rejection: verify that no side effect occurs and the agent receives a structured reason.
- Timeout: ensure the request expires safely and cannot be approved with an old token.
- Duplicate approval: submit the same decision twice and confirm the second is idempotent.
- Changed arguments: alter an amount, recipient, path, or payload after approval and require a new review.
- Malformed arguments: reject invalid schemas before the reviewer sees an executable proposal.
- Retries: simulate network failures around the commit and verify idempotency keys prevent duplicate effects.
- Lost session: restart the application and resume from durable state without reconstructing the run from chat text.
- Handoff: trigger a nested-agent approval and confirm it appears in the outer queue and audit trail.
- Privilege failure: remove a credential or scope and verify the agent stops rather than requesting broader access.
- Prompt injection: place instructions in retrieved content that attempt to suppress approval or alter the target.
- Reviewer compromise: test expired roles, revoked accounts, and unauthorized approval links.
Where automated review fits—and where it does not
A separate reviewer agent can reduce synchronous prompts at a sandbox boundary by checking arguments, policy, and evidence. It can be useful for low- or moderate-risk volume, but it is not a deterministic security guarantee and can be misled by the same context failures as the acting agent.
Keep human sign-off for high-impact, regulated, financially material, or hard-to-reverse actions. If an automated reviewer is used, bound its authority, give it independent policy inputs where possible, log its decision as automated, and red-team it with malformed arguments, prompt injection, misleading evidence, and repeated retries. A human should remain able to stop the queue and take over.
A practical rollout plan
- Phase 1: observe. Run agents with read-only tools, log proposed side effects, and measure what would have required approval.
- Phase 2: draft. Enable draft creation in destination systems while keeping send, publish, merge, delete, and commit operations disabled.
- Phase 3: gate. Turn on Confirm for moderate-risk actions and Mandatory review for regulated or irreversible actions. Start with a small group of trained approvers.
- Phase 4: harden. Add deterministic denies, credential separation, idempotency, timeout handling, nested-agent tests, and emergency stop procedures.
- Phase 5: tune. Use rejection reasons, latency, complaints, and bypass data to adjust thresholds without weakening the highest-risk controls.
Anthropic’s public-API analysis found that nearly half of agentic activity was software engineering, a domain where patches, pull requests, and deployments naturally benefit from draft and review boundaries. The same analysis reported that, on the most complex tasks, Claude Code stopped for clarification more than twice as often as humans interrupted it. That pattern supports designing explicit check-ins rather than assuming an agent will always know when to stop.
Operational checklist
- Every tool and side effect has a documented risk tier.
- Notify, Confirm, Draft, or Mandatory review is selected by policy, not by model preference.
- Named approver roles and separation of duties are enforced.
- Least-privilege credentials and deterministic deny rules are active.
- Approval packets show exact arguments, targets, evidence, confidence where available, and diffs.
- Draft status is stored in the destination system.
- Execution pauses before side effects and resumes from durable state.
- Approvals are bound to tool identity, arguments, run, policy version, and expiration.
- Nested agents and handoffs use the same review surface and audit trail.
- Rejection, timeout, duplicate, retry, malformed-input, and emergency-stop paths are tested.
- Decision identity, timestamp, rationale, and tool-call ID are retained.
- Escalation, approval, rejection, latency, override, complaint, drift, and bypass metrics are reviewed.
- Controls are reassessed after model, data, workflow, integration, or regulatory changes.
The result is not an agent that asks permission for everything. It is an agent that can work independently inside a clearly bounded zone, prepares consequential actions in a form a person can verify, and cannot cross the critical boundary without an accountable decision.
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