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Put a mandatory, fail-closed approval gate immediately before every high-impact or irreversible tool call. The agent should propose the exact action and arguments, then pause. An authorized person reviews the target, expected effect, evidence, policy trigger and uncertainty. Only an explicit approval for that exact payload allows execution. Rejection, timeout, lost authorization or any change to the request must result in no execution. Keep an independent emergency stop and a way to revoke the agent’s credentials.
This pattern works for an agent that wants to send an email, transfer money, change an account, deploy to production, delete data, make an access decision or issue a safety-critical command. It is more reliable than asking for a human review after the action, because the consequential side effect has not happened yet.
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What a human-approval gate is—and why it must be at the tool boundary
An AI agent usually plans work, selects a tool and supplies arguments to that tool. The approval control belongs between the final tool request and the tool executor, not in the agent’s prompt and not in a later audit queue. The executor should refuse to run a high-risk call unless it receives a valid approval record tied to the request.
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- Pre-execution: the side effect cannot begin before approval.
- Explicit: a named, authorized person chooses approve or reject; opening a screen, remaining inactive or clicking a generic “continue” control is not approval.
- Payload-bound: approval covers the exact serialized arguments, target and policy version. A changed amount, recipient, file path, environment or permission set requires a new decision.
- Revocable: the organization can stop the agent and narrow or revoke its credentials independently of the agent process.
The EU AI Act’s Recital 73 says high-risk systems should be designed so natural persons can oversee their functioning, ensure intended use and address impacts throughout the lifecycle. It also calls for operational constraints that the system cannot override and for people with the competence, training and authority to intervene or stop it when it does not perform as intended. A real gate implements those ideas as an enforceable runtime control rather than a policy statement.
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Decide which actions require approval
Start with an inventory of every tool and external side effect. Include actions that look routine but can affect money, rights, safety, production availability or other people. Record the tool, the data it can change, the maximum possible impact, whether the effect can be reversed and which role owns the decision.
Typical actions to classify as high impact
- Moving funds, issuing refunds, placing orders or changing payment instructions.
- Sending external email, messages or notices that create a legal, financial or reputational commitment.
- Creating, deleting or changing user accounts, roles, credentials, access policies or security controls.
- Deploying code, infrastructure or configuration to a production environment.
- Deleting records, disabling backups or changing retention and e-discovery settings.
- Making an eligibility, employment, insurance, credit, healthcare or other regulated recommendation that can affect a person.
- Issuing a command that could affect physical safety, industrial equipment or a critical service.
Use a risk policy rather than a single word such as “dangerous.” A practical rule considers impact, reversibility, the people affected, legal or regulatory context and the role required to approve. Set a low threshold for actions that are irreversible, difficult to detect or likely to affect someone who is not operating the agent.
Example policy matrix
| Action class | Default control | Minimum approver | Extra safeguards |
|---|---|---|---|
| Read-only lookup with no external effect | Allow, subject to access policy | System authorization | Log the request and returned data |
| Reversible internal change | Approval according to scope and value | Owner of the affected system | Show rollback method and time limit |
| External communication or financial transaction | Mandatory per-action approval | Authorized business owner; separate financial approver where required | Bind recipient, amount, message and expiry to the approval |
| Access, deletion or production deployment | Mandatory approval; no batch bypass by default | Role with operational authority and relevant training | Use separation of duties, change window and independent stop path |
| Safety-critical or regulated decision | Mandatory approval with documented rationale | Competent person with authority under the applicable rule | Require evidence, uncertainty, alternatives and post-action monitoring |
Document exceptions narrowly. A service account, an emergency procedure or a “trusted” agent should not silently bypass the gate. If an emergency path is necessary, make it a separately authorized workflow with its own limits, logging and review.
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Build the approval flow step by step
1. Inventory tools and side effects
For each tool, list its complete schema, destinations, credentials and possible downstream effects. Treat a tool that writes to a database, calls an external API or sends a message as a side-effecting tool even if its name sounds informational. Identify compound operations: an agent may call a lookup, choose a target and then invoke a second tool that performs the irreversible action.
Assign an owner and a risk class to every operation. Record whether the operation supports a safe preview or dry run, how it can be rolled back and which evidence a reviewer needs. This inventory is the baseline for testing that no high-risk path is left unguarded.
2. Define the policy and approver roles
Express the policy in machine-readable rules that the gate can evaluate before execution. A rule can match tool name, environment, amount, recipient, data type, affected population, reversibility and the initiating user. The policy should return at least: approval required, required role, number of approvers, maximum approval age and any separation-of-duties constraint.
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Use roles, not an unmaintained list of personal names. Verify that the reviewer is currently authorized, trained for the action and not prohibited from approving their own request. For high-value or regulated actions, require two people or a requester–approver split when your sector rules call for it.
3. Intercept and freeze the run
When the agent proposes a covered call, the orchestrator should serialize the complete request in a deterministic form, calculate a cryptographic hash and create a pending approval. Freeze the run at this point. Do not let the model continue with a different tool, silently retry, or substitute a lower-risk-looking call while the request is waiting.
The pending record should contain the agent identity, initiating user, tool name, complete arguments, target, intended effect, evidence, confidence or uncertainty, matched policy rule, policy version, creation time and expiry. Include a human-readable preview, but retain the machine-readable payload that will be checked at execution.
4. Show reviewers enough information to decide
The approval screen should answer five questions without requiring the reviewer to reconstruct the context:
- Who is asking? Show the initiating user, agent identity, application and current credentials.
- What will happen? Display the exact tool, destination, record or account, parameters, amount, message and environment.
- Why now? Show the agent’s stated objective, relevant evidence or citations, assumptions and uncertainty.
- Which rule triggered review? Display the policy name and version, impact class, reversibility assessment and required role.
- What safer choices exist? Offer a preview, draft, sandbox, smaller amount or other non-destructive alternative when available.
Render data in a way that cannot hide material details. For an email, show all recipients, attachments and the final body. For a money movement, show source, destination, amount, currency, fees and execution date. For a deployment, show the artifact digest, environment, change set and rollback reference.
5. Require an explicit decision
Provide distinct Approve and Reject actions, and record an optional reason for either. Do not infer approval from a reviewer viewing the request, failing to respond, closing the page or pressing a generic “continue” button. A timeout is a rejection for execution purposes. If the policy requires two approvers, one approval must not release the call.
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Authentication and authorization should be checked at decision time. A person who could view a request but lacks the required role must not be able to approve it. For sensitive actions, require recent re-authentication or an equivalent strong control, and display the exact payload in the confirmation step so the reviewer cannot approve a stale summary.
6. Revalidate immediately before execution
Approval is not a permanent permit. Just before the tool executor runs, re-check the reviewer’s authorization, the policy version, target state, credential scope, expiry and payload hash. Compare the stored hash with the serialized request that is about to execute. Any drift—such as a changed recipient, amount, file, environment, model-generated argument or policy—invalidates the approval and sends the run back for a new decision.
Use a short, explicit expiry appropriate to the action. A five-minute approval for a payment may be reasonable; a production change scheduled for a later window may require a separate change record and a fresh confirmation at the window. Never extend an approval silently.
7. Execute with least privilege
Give the executor only the credentials and scope needed for the approved call. The agent should not hold a broadly privileged token that can be used through an unreviewed path. Where possible, mint a short-lived, single-use capability after approval. Bind that capability to the tool, target, payload hash, amount limit and expiry, then invalidate it after execution or rejection.
8. Record an append-only audit event
Store an append-only record for every request, decision and execution outcome. At minimum include:
- Request ID, agent and initiating user.
- Tool, complete serialized arguments, target and payload hash.
- Policy name and version, risk classification and required role.
- Reviewer identity, role, authentication event, decision and reason.
- Creation, decision, expiry, execution and completion timestamps.
- Execution result, errors, retries, downstream identifiers and observed impact.
Protect the log from alteration by the agent or the reviewer whose decision it records. Define retention, access and export rules that match your legal and operational requirements. Monitor approval latency, rejection and timeout rates, policy overrides, drift failures, emergency stops and incidents; unusual changes can reveal a broken workflow or attempted bypass.
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9. Provide and exercise an independent stop path
The stop control must work when the agent, model provider or approval service is malfunctioning. Operators should be able to pause queued runs, disable tool execution and revoke or narrow the agent’s credentials from a separate control plane. Test the stop path regularly, including while a request is awaiting approval and while a tool call is being retried. Review whether already-issued capabilities expire quickly enough to limit damage.
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The following pseudocode shows the essential ordering. The policy engine, approval service and executor can be separate services, but the executor—not the language model—must enforce the final check.
request = canonicalize(agent_id, user_id, tool, arguments, target)
policy = evaluate_policy(request, context)
if policy.approval_required:
approval = create_pending(request, policy.version, hash(request), expiry=policy.expiry)
freeze_run(run_id)
decision = wait_for_explicit_decision(approval.id)
if decision != "approve":
record_outcome(approval.id, "not_executed")
stop_run(run_id)
recheck_authority(decision.reviewer, policy.required_role)
recheck_policy(request, policy.version)
recheck_target_and_credentials(request)
if hash(request) != approval.payload_hash or expired(approval):
invalidate(approval.id)
return_to_approval(run_id)
capability = mint_single_use_capability(request, approval)
result = execute_with_capability(tool, arguments, capability)
append_audit_record(request, approval, result)In production, make canonicalization deterministic and test edge cases such as Unicode normalization, reordered fields, omitted defaults, floating-point amounts and alternate target identifiers. A reviewer and the executor must hash the same representation; otherwise a visually unchanged request can appear different, or a changed request can appear identical.
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| Design choice | Advantages | Risks and when to use it |
|---|---|---|
| Per-action approval | Strong payload binding and clear accountability | More reviewer latency; use for money, access, deletion, external communication and other high-impact calls |
| Batch approval | Lower queue volume for repetitive work | Reviewers may miss an outlier; only use for a narrowly defined, homogeneous set with fixed limits, item-level visibility and no material drift |
| Pre-execution blocking | Prevents the side effect and supports fail-closed behavior | Requires queueing, expiry and reliable recovery; it is the default for high-risk actions |
| Post-action review | Useful for sampling and incident analysis | Cannot undo harm; never substitute it for pre-execution approval where impact is high |
| Single approver | Fast and simple | Creates concentration risk; require separation or multiple approvers for sensitive or self-affecting actions |
| Multiple approvers | Improves independence and control over high consequences | Adds delay and coordination; define quorum, timeout and what happens if approvers disagree |
Reversibility changes the balance but does not eliminate the need for approval. A deletion with a reliable, tested restore may be less risky than an irreversible deletion, yet the reviewer still needs to see the restore point, retention period and data that will be affected.
Examples: email and money movement
Sending an external email
- Classify the send tool as an external side effect and require approval for recipients outside approved domains, messages containing regulated data or any legal or financial commitment.
- Freeze the agent with the final recipient list, cc and bcc fields, subject, body, attachments, sender identity and send time serialized into the payload.
- Show the initiating user, objective, source records and uncertainty. Highlight external recipients, sensitive terms and links.
- Require an authorized reviewer to approve or reject that exact message. Editing the body or recipient list after approval creates a new hash and a new request.
- At send time, re-check the sender’s authorization, domain policy, attachment scan and payload hash, then use a single-use capability.
Moving money
- Require per-transaction approval, with amount, currency, source, destination, beneficiary, fees, execution date and reference included in the payload.
- Apply limits by account, currency, beneficiary and time period. A transaction over a threshold can require a second approver or a finance role separate from the requester.
- Display the evidence supporting the payment and the agent’s uncertainty; do not present a model-generated explanation as independent verification.
- Expire approvals quickly and re-check balances, beneficiary status, sanctions or fraud controls and authorization immediately before release.
- Record the bank or payment-provider transaction identifier and reconcile the downstream result with the approval record.
Failure modes and recovery
The agent keeps acting while approval is pending
The gate is probably implemented in the user interface or prompt rather than in the executor. Move enforcement to the tool boundary, freeze the run, and make every side-effecting tool validate an approval capability.
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Store a canonical payload and hash it at proposal, approval and execution. Reject any mismatch, including changes introduced by defaults, retries, serialization or target lookup.
Approvals accumulate in a queue
Measure time to decision by risk class, add clear ownership and expiry, and offer safe previews for low-impact work. Do not solve queue pressure by making approval optional for the same high-risk action; narrow the policy or redesign the workflow instead.
A reviewer is unavailable
Let the request expire or route it to a pre-authorized backup role under the same policy. Never treat silence as consent. For emergencies, use a separately controlled break-glass process with a limited capability and mandatory retrospective review.
The approval service or identity provider is down
Fail closed for covered actions. Keep the independent stop and credential-revocation path available, and test that an outage cannot cause the executor to accept an old or unverifiable approval.
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Pause queued runs, revoke or narrow credentials, disable affected tools and preserve the append-only records. Identify approvals that were issued but not executed, executions with downstream effects and any drift or policy-override events. Restore normal operation only after policy, authorization and target state are revalidated.
Governance, standards and lifecycle checks
NIST describes the AI Risk Management Framework as voluntary guidance for incorporating trustworthiness into AI design, development, use and evaluation. Its Playbook organizes implementation activities into Govern, Map, Measure and Manage. Apply those functions to the approval system itself: govern roles and escalation, map tools and affected people, measure decisions and failures, and manage changes, incidents and residual risk.
The AI RMF Core treats risk management as continuous and timely across the AI-system lifecycle. Revisit the inventory when a tool, model, data source, jurisdiction or business process changes. Reassess policy thresholds after incidents and near misses. Retrain reviewers when the action class or evidence format changes, and verify that they still have the authority and technical competence to intervene.
EU high-risk classification and transition obligations depend on the use case, jurisdiction and current implementation guidance. The European Commission’s high-risk guidance identifies potential effects on health, safety and fundamental rights and describes obligations for providers and deployers; check the latest Commission material for the dates and rules that apply to your system. Recital 73’s requirement for competent people with authority to intervene is a useful design test even where the AI Act does not directly govern a particular deployment.
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Test before relying on the gate
- Attempt every high-risk tool call with no approval, an expired approval and a rejected approval; each must be refused.
- Change one argument, target, policy version or credential after approval; each change must force a new decision.
- Replay a request, reorder fields and alter omitted defaults to verify canonicalization and hash checks.
- Remove the reviewer’s role between approval and execution; the call must stop.
- Kill the approval service, agent process and model connection independently; the executor must remain fail-closed.
- Exercise the emergency stop during queueing, retry and execution, then verify credential revocation and capability expiry.
- Inspect audit records for completeness, immutability, clock consistency and linkage from request through downstream result.
- Run tabletop scenarios involving an incorrect recipient, a prompt-injected tool request, a compromised reviewer account and a policy change while approvals are pending.
A human approval gate is effective only when the person can understand the proposed action, has authority to decide and can actually prevent execution. Put that decision at the tool boundary, bind it to an unchanged payload, revalidate immediately before running, and maintain an independent stop path throughout the system’s life.
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