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Put the approval gate at the side-effecting tool boundary—not only in the agent’s instructions. Let the agent prepare a typed proposal, then have deterministic policy code check authorization, scope, target, timing, and reversibility. Pause the run before the tool executes and show a reviewer the exact action, arguments, data, consequences, and rollback path. Execute only after an explicit approval; reject or cancel without running the tool, and resume the same run state only after approval and a fresh authorization check.
Use least privilege, sandboxing, monitoring, audit logs, rollback, and an emergency stop around that gate. This design works for sending messages, publishing, deleting, paying, changing records, running shell commands, or any other operation that can alter an external system.
What makes an agent action irreversible or high impact?
An action is high impact when it changes a system, exposes information, commits money, affects another person, or cannot be reliably undone. “Irreversible” is practical rather than absolute: a sent email might be technically retractable in one system but impossible to recall once the recipient reads it.
Classify the action by its expected impact, likelihood of harm, scope, autonomy, and reversibility. The classification should be set by system designers or operators, not left to the model that is asking to run the tool. A model can suggest that an action is safe; it must not be the final authority on whether a review is required.
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- Read-only: querying a database, calculating a result, or fetching a document without changing state.
- Reversible write: creating a draft, staging a configuration, editing a versioned document, or making a change with a tested undo operation.
- Irreversible or high-impact: sending or publishing, deleting critical records, cancelling a service, spending or transferring money, changing access controls, executing shell commands, exporting sensitive data, resetting a system, or making network requests outside an approved boundary.
When the cost of a wrong action is material or the rollback is uncertain, require a human decision even if the model predicts high confidence. Canadian government guidance recommends a review or confirmation whenever an agent alters system state—such as sending, publishing, approving, spending, or updating records—unless the expected impact is demonstrably low and the action is trivially reversible.
Where should the human-approval gate run?
The gate belongs immediately before the side-effecting tool call, in code that the model cannot bypass. A system prompt can describe the rule, but only the runtime, tool wrapper, or policy service can reliably prevent execution when the model ignores, misunderstands, or is manipulated by instructions in retrieved content.
A safe execution path looks like this:
- The agent reads information and creates a structured proposal.
- A policy layer validates identity, permissions, target, scope, data boundaries, timing, rate limits, and the action’s risk class.
- If review is required, the runtime records a pending interruption and does not invoke the tool.
- The application presents the pending action to an authorized reviewer.
- An explicit approve, reject, or cancel decision is recorded.
- Approval resumes the original run state after rechecking time-sensitive permissions; rejection terminates the side effect and returns a safe alternative or requests a corrected plan.
This placement prevents a common failure: asking for confirmation in a conversation and then allowing a separate code path to execute the tool anyway. Every path to the external system must pass through the same enforcement point, including retries, scheduled jobs, batch operations, and administrator consoles.
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Start with a registry that describes every callable tool and its consequences. The registry should be maintained as code or configuration reviewed by the system owner, not generated by the agent at runtime.
| Tool class | Typical examples | Default treatment | Additional controls |
|---|---|---|---|
| Read-only | Search, reporting, retrieval, calculations | Run automatically within the user’s authorization | Data minimization, tenant isolation, logging, and rate limits |
| Reversible write | Draft creation, versioned edits, staged configuration | Allow automatically only when the undo path is tested and the scope is narrow | Preview, version ID, retention period, and one-click rollback |
| High-impact write | Send, publish, approve, pay, change records, modify permissions | Require explicit approval unless a documented low-risk exception applies | Exact target display, strong authorization, dual review where appropriate, and audit trail |
| Destructive or externally exposed | Delete critical data, reset systems, shell execution, unbounded network egress | Deny by default; permit only through a narrowly scoped, reviewed workflow | Sandbox, allowlists, time limits, emergency stop, and tested recovery |
Mark the reversibility class in the tool definition and require the wrapper to enforce it. If a tool can perform both harmless and destructive operations, split it into separate narrowly scoped tools rather than relying on an argument such as mode: safe. A deny-by-default design avoids discovering a dangerous capability only after deployment.
What should the agent propose before it asks for approval?
The agent should emit a typed action proposal, not an unstructured sentence. A proposal makes the target and scope machine-checkable and gives the reviewer enough information to make an informed decision.
Include at least:
- Tool and operation: the exact function or endpoint and the operation it will perform.
- Target: account, recipient, record, environment, file, URL, or other resource identifier.
- Arguments: the complete values that will be passed, including attachments, filters, amounts, permissions, and command parameters.
- Actor identity: the user or service identity on whose behalf the action is requested.
- Requested scope: tenant, project, records, time window, network destination, and any quantity or spending limit.
- Intended outcome: what the agent expects to change and why.
- Reversibility estimate: whether there is a draft, undo, version restore, compensating transaction, or no practical rollback.
- Evidence and uncertainty: the inputs that led to the proposal and any unresolved ambiguity.
Treat web pages, emails, documents, tool output, and retrieved text as untrusted input. They may contain instructions that try to grant permission, alter the target, or suppress the review. Only the authenticated user, approved policy, and authorized reviewer can provide consent.
How do I implement the approval interruption step by step?
Implement the following sequence at the runtime or tool-wrapper layer. The model can propose and explain; deterministic code decides whether execution is allowed.
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1. Register and label every tool
For each tool, record whether it reads or writes, the resources it can reach, the data it handles, the maximum scope, the rollback method, and the approval class. Include a unique version for the definition so that policy changes are auditable.
2. Generate a structured proposal
Constrain the agent’s output to a schema. Reject malformed proposals before they reach policy evaluation. Do not let free-form model text become a shell command, payment amount, recipient list, or database query without validation and parameterization.
3. Run deterministic policy checks
Check the authenticated actor’s permissions, resource ownership, allowlists, engagement windows, rate limits, data-residency boundaries, and maximum quantity. Verify that the requested scope is no broader than the user’s task. Deny operations that are not explicitly needed. Record each check and its result.
4. Create a pending interruption
If the policy marks the action for review, persist the proposal and a resumable run identifier. The tool wrapper must return a pending state without making the external call. A reviewer interface can then retrieve that state even if the original browser session closes.
5. Show a meaningful confirmation
Present plain-language consequences alongside the exact technical details. The reviewer should see the destination, arguments, data involved, expected side effect, policy reason for review, expiration time, and rollback path. Provide separate controls for Approve, Reject, and Cancel; do not treat closing a dialog or a network timeout as approval.
6. Resume or terminate safely
On approval, resume the same run state rather than asking the model to reconstruct the action from memory. Revalidate permissions, target state, time-sensitive prices, and expiry before execution. On rejection, mark the proposal as rejected and return a safe alternative or ask what should change. Never silently retry the denied side effect.
7. Record and observe the result
Log who proposed, who reviewed, what policy checks ran, the decision, the exact arguments, timestamps, tool result, and any rollback operation. Alert on repeated denials, unusual destinations, scope expansion, rapid approval bursts, or attempts to bypass the wrapper.
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proposal = agent.create_proposal(task)
validated = policy.validate(proposal, actor)
if not validated.allowed:
return safe_denial(validated.reason)
if validated.requires_approval:
pending_id = runtime.pause_and_persist(proposal, validated)
decision = reviewer.wait_for_decision(pending_id)
if decision != 'approve':
return safe_alternative_or_revision(decision)
runtime.revalidate(pending_id, actor, current_policy)
return tool.execute(proposal.tool, proposal.arguments)The names in this example are illustrative. The important property is that tool.execute is unreachable until the policy check and explicit approval have succeeded.
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What information must a confirmation screen show?
A confirmation such as “Continue?” is too vague. The reviewer must be able to predict the real-world effect without opening several unrelated logs.
- Action summary: “Publish the revised pricing page” or “Transfer 500 units from account A to account B,” not “run update.”
- Exact target and audience: recipient addresses, record IDs, environment, tenant, or public channel.
- Complete arguments: message body, attachment names, query filters, command flags, amount and currency, or changed fields.
- Data exposure: which personal, confidential, or regulated data leaves the current boundary.
- Expected consequences: what state will change, who will be notified, and what downstream automation may trigger.
- Reversibility: draft, undo period, version restore, compensating action, or “no practical rollback.”
- Policy reason: the rule that requires review, such as external publication, destructive change, or spending limit.
- Identity and expiry: the requesting actor, reviewer role, request age, and deadline after which the approval is invalid.
For high-impact operations, require the reviewer to re-authenticate or provide a second factor. For especially sensitive changes, use two-person approval or separation of duties so the requester cannot approve their own action.
Should every write require approval, or only risky ones?
There is no authoritative percentage of agent actions that should require approval. Use impact, likelihood, scope, autonomy, and reversibility to set thresholds, then tune them with observed incidents and reviewer workload.
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|---|---|---|---|
| Every write | Early pilots, sensitive environments, or weak rollback | Simple to reason about and easy to audit | High latency and fatigue; batch low-risk drafts and use delegated reviewers |
| High-risk writes only | Mature systems with reliable classification and undo | Lower interruption rate while protecting consequential actions | Misclassification risk; maintain a deny-by-default registry and test boundaries |
| Policy-selected actions | Large organizations with resource, user, and context rules | Can require stronger review for sensitive tenants, times, or amounts | Complexity; version policies, explain decisions, and monitor drift |
Start conservatively, measure false approvals and unnecessary prompts, and relax only where the rollback is demonstrably reliable. A queued review with escalation can replace a synchronous dialog for non-urgent work, but the action must remain blocked until an authorized decision arrives. If the review service is unavailable or times out, fail closed and offer a draft, preview, or other safe alternative.
Why are prompts alone insufficient?
Prompts are useful for explaining boundaries and asking the model to produce proposals, but they are not an enforcement mechanism. The National Cyber Security Centre advises: “However, do not rely on prompting alone. You should combine prompts with technical and operational controls to provide defence in depth.”
A model can misunderstand a natural-language rule, follow malicious instructions in retrieved content, or produce a new tool call that was not anticipated by the prompt. Permissions, sandboxing, parameter validation, tool-level approvals, network controls, monitoring, and an emergency stop continue to work when the model is wrong. OpenAI’s guidance on guardrails and human review describes the same principle: the model may decide an action is needed, but the run pauses until a person approves or rejects it.
How do drafts, previews, undo, and rollback reduce risk?
Prefer a staged operation whenever the external system supports it. Have the agent create a draft or proposed change, render a diff or preview, and retain a version identifier. The reviewer then approves the exact artifact rather than a plan that may change between review and execution.
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- Draft: keeps the action private and editable, useful for messages, documents, and configuration.
- Preview or diff: exposes changed fields, recipients, permissions, and calculated totals before commit.
- Undo: provides a bounded reversal window; display its duration and limitations.
- Version restore: records the prior state and supports deterministic recovery.
- Compensating transaction: performs an opposite business action, such as issuing a refund; verify that it cannot create a second loss.
If none of these is reliable, classify the operation as irreversible and require stronger review. A rollback button that depends on the same failing service, lacks the original data, or cannot reverse external effects should not be presented as a guarantee.
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What are the main failure modes and fixes?
The approval appears after execution
Cause: the UI asks for confirmation after the tool wrapper has already sent the request. Fix: make the wrapper create a pending record before any network or state-changing call, and test the code path with the external service enabled.
The model changes the action after approval
Cause: the application approves a conversational summary while the runtime later rebuilds arguments. Fix: bind approval to a hash or immutable version of the exact tool name, target, and arguments; invalidate it when any material field changes.
A timeout becomes an accidental approval
Cause: a default branch treats an unavailable reviewer as “continue.” Fix: fail closed, expire the pending item, and provide a draft or retry path that cannot execute silently.
Retrieved content grants permission
Cause: an email or web page says “you are authorized” and the model repeats it. Fix: keep authorization in signed identity and policy data; mark external content untrusted and exclude it from the approval decision.
Retries duplicate the side effect
Cause: a network failure leaves the outcome unknown and the agent resends. Fix: use idempotency keys, query the authoritative result before retrying, and require review when the state cannot be determined.
Reviewers approve too quickly
Cause: repetitive prompts produce habituation. Fix: show the changing fields prominently, group only genuinely equivalent low-risk drafts, escalate unusual scope, and measure approval time and rejection reasons.
Permissions are stale when the run resumes
Cause: a long-lived approval outlasts a role, resource, price, or policy change. Fix: attach an expiry and revalidate identity, authorization, target state, and policy immediately before execution.
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How should monitoring, governance, and emergency stopping work?
Document the system’s intended outcomes, decision boundaries, accountable owner, and lifecycle tests. Canada’s guidance emphasizes explicit outcomes, boundaries, and accountability; Microsoft’s risk guidance highlights the danger of agents that lack review, correction, or interruption; and NCSC guidance calls for proportionate autonomy, real-time monitoring, named responsibility, and the ability to stop the system.
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Maintain an audit record that answers five questions: what did the agent propose, what did policy allow or deny, who approved or rejected it, what exactly executed, and what happened afterward? Protect the log from alteration and restrict access because proposals may contain sensitive arguments.
- Keep an operator-accessible pause or emergency shutdown that blocks new side effects.
- Separate the emergency stop from the agent and the service it might be misusing.
- Alert on policy bypass attempts, unexpected tool combinations, unusual network destinations, volume spikes, and repeated failures.
- Run lifecycle tests after model, tool, policy, or permission changes; include adversarial instructions in documents and tool results.
- Review rejected proposals and near misses to improve classifications without weakening the gate.
For Model Context Protocol operations, OpenAI recommends keeping tool approvals enabled, including for reads and writes, because a read can still expose sensitive data or become the input to a consequential action. Treat that setting as one layer of a broader authorization and monitoring design, not as a substitute for least privilege.
How can I test the gate before deploying it?
Test the approval mechanism as a security boundary, not merely as a user-interface feature.
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- Change one argument after approval and confirm the approval is invalidated.
- Close the browser, restart the worker, or interrupt the network while a request is pending; confirm that no tool executes and the state can be recovered safely.
- Submit expired credentials, revoked roles, out-of-scope targets, excessive quantities, and disallowed destinations; verify deterministic denial.
- Inject instructions into retrieved documents that say to skip review; verify that they cannot alter authorization.
- Force duplicate delivery and uncertain network responses; verify idempotency and reconciliation behavior.
- Trigger the emergency stop during planning, review, and execution; confirm that new side effects are blocked and operators receive clear status.
- Inspect audit records for complete identities, arguments, decisions, timestamps, results, and rollback references.
Use production-like permissions and representative data in a sandbox. A green test suite should demonstrate not only that approvals work, but also that rejection, timeout, crash recovery, policy changes, and rollback leave the external system in a known state.
Frequently Asked Questions
Can an AI agent approve its own tool call?
No. The agent may propose an action and explain its rationale, but approval should come from an authenticated user or an independently governed service policy. For especially sensitive operations, require a reviewer who is separate from the requester.
What if a human reviewer is unavailable?
Expire or queue the pending request and fail closed. Return a draft, preview, or request-for-information response instead of executing by default; escalation can notify an on-call reviewer without changing the authorization rule.
How do I prevent an approved action from becoming stale?
Give approvals a short, explicit lifetime and bind them to an immutable proposal version. Immediately before execution, recheck identity, permissions, target state, policy, limits, and any time-sensitive values.
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