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Set the gate in the execution path, not in the model’s reasoning. An AI agent may propose an action, but a separate policy and authorization service must decide whether that exact operation is permitted, whether a human approved it, and whether the approval is still valid. Classify actions by impact and reversibility, expose only the capabilities the task needs, show the precise operation for review, bind approval to its actor, target and parameters, and deny execution when any validation or audit step fails.
This design addresses the risk OWASP calls excessive agency. Its LLM06:2025 guidance says to “Utilise human-in-the-loop control to require a human to approve high-impact actions before they are taken.” A gate limits what can happen; it does not replace least privilege, downstream authorization, monitoring or rate limits.
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What an approval gate must do
An approval gate is a preventive control between an agent’s proposal and a consequential tool call. The model can interpret a request, plan steps and prepare parameters. It must not be the final authority on whether an operation is allowed. A policy service or execution component evaluates the request independently, checks authorization and approval state, and either releases the call or rejects it.
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The approval must describe the operation that will actually run. A person approving “clean up the customer database” has not necessarily approved a specific delete query, set of records or retention exception. A reliable gate turns the proposal into a normalized, reviewable action and binds the decision to that exact action.
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- Proposal: the agent requests a named operation with structured parameters.
- Classification: policy code determines the risk level from the operation, target, scope and context.
- Preview: the reviewer sees the destination, affected records or people, material parameters and expected consequence.
- Decision: an authenticated approver accepts or rejects the exact request.
- Enforcement: an independent executor verifies authorization, approval scope, expiry and replay status immediately before execution.
- Evidence: the system records the request, policy result, approval identity, execution result and any rollback or interruption.
OWASP’s AI Agent Security Cheat Sheet similarly says, “Require explicit approval for high-impact or irreversible actions.” That is guidance, not a law or a universal implementation standard.
Which AI agent actions should require human approval?
Trigger review by the consequence of the operation, not by a vague label such as “agentic.” A connector may expose harmless reads alongside writes, sends, deletes and permission changes. Assess each operation separately.
Actions that normally warrant a gate
- Sending external email, messages or notifications, especially to customers, regulators or large lists.
- Executing code, scripts, queries or infrastructure changes outside an isolated test environment.
- Deleting database records, files, accounts, logs or backups.
- Transferring funds, initiating payments, changing payout details or approving expenses.
- Changing permissions, roles, credentials, security settings or access-control policies.
- Publishing content, changing a production configuration or deploying software.
- Recovering an account, resetting authentication factors or changing a legal, medical or financial record.
- Exporting, disclosing or bulk-processing sensitive personal, confidential or regulated data.
These examples align with OWASP’s high-impact examples—sending email, executing code, deleting database records and transferring funds—but the exact boundary depends on your environment and authorization model.
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Read-only operations with narrow scope, no external side effect and easy independent recovery may be eligible for automatic execution. Examples include retrieving a public document, checking a service status or calculating a draft that is not sent or committed. “Low risk” must be an explicit policy decision; an unmapped action should not silently inherit automatic approval.
Questions to ask about every operation
- What is the impact if the parameters are wrong or the action is maliciously induced?
- Can the operation be reversed, and can recovery be performed independently of the agent?
- What privilege does it exercise, and whose authorization context does it use?
- Does it cross a trust boundary or contact an external party?
- How many records, accounts, devices or people can it affect in one call?
- Does it read, transform or disclose sensitive data?
- Is there a reliable way to stop, roll back or reconcile a partial result?
Impact, irreversibility, privilege and scope are emphasized in the cited OWASP guidance. The other questions are practical assessment dimensions for your team, not a standardized scoring formula.
Step 1: Inventory actions, not just tools
Create an inventory in which each row is one callable operation. Do not list only “CRM connector” or “cloud API”; split the connector into operations such as read_customer, update_customer, export_customers and delete_customer.
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- Name the operation and version. Use a stable identifier rather than a free-form tool description.
- Record the target. Identify the resource, tenant, environment, account or recipient that can be affected.
- Describe data access. Note the fields read or written and their sensitivity.
- Measure scope. Capture maximum records, recipients, amount, duration or geographic reach per call.
- Document side effects. Include external visibility, financial effect, notifications, irreversible changes and downstream jobs.
- List recovery. State whether an independent rollback, restore or reconciliation procedure exists.
- Map required identity and privilege. Specify the user, service account or delegated role needed at execution.
OWASP identifies excessive functionality and excessive permissions as distinct risks. A tool that offers broad, unused capability enlarges the blast radius even when the agent is behaving as intended.
Step 2: Set an explicit risk policy
Maintain a centrally controlled map from operation types and context to risk levels. The policy service should receive structured facts—not the model’s statement that an action is safe—and return the required control.
| Risk level | Illustrative policy treatment | Typical examples |
|---|---|---|
| Low | Automatic execution is possible only when the operation is explicitly mapped as low risk, narrowly scoped and independently authorized. | Read-only status lookup or retrieval of public information. |
| Medium | Require an authenticated human approval in the workflow. | Limited internal update or a reversible change with meaningful operational impact. |
| High | Require explicit approval, a detailed preview and fresh authorization immediately before execution. | External communication, code execution, production change or sensitive-data export. |
| Critical | Require explicit approval plus step-up authentication, short expiry, replay protection and a recovery plan. | Payment initiation, privilege change, bulk deletion, account recovery or production deployment. |
| Unmapped | Deny or route to approval; never default to automatic execution. | A newly introduced operation or materially changed parameter set. |
This conservative low-risk-only bypass pattern is an illustrative example from OWASP’s cheat sheet, not a universal standard. Set thresholds appropriate to your organization, document exceptions and require a policy owner to approve changes.
Use context, not only operation names
The same operation can move between levels. Updating one internal test record differs from updating thousands of production records. Sending a message to a private queue differs from sending it to customers. Include environment, tenant, recipient class, volume, amount, data sensitivity and time-of-day or geographic restrictions where relevant.
Require step-up authentication for critical actions
For payment initiation, privilege changes, bulk deletion, account recovery and production deployment, require the approver to authenticate with an additional factor or stronger identity proof. A logged-in session alone may not establish that the person consciously authorized a high-consequence action.
Step 3: Constrain the agent’s capabilities
Approval is not a substitute for least privilege. Give each agent and tool only the functions, resources and data needed for its assigned task.
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- Expose specific operations instead of a general-purpose shell, unrestricted SQL endpoint or arbitrary HTTP client.
- Use allowlisted targets, fields, methods, recipients and environments.
- Separate read and write credentials; use distinct identities for production and test systems.
- Pass the user’s authorization context where applicable, and enforce authorization again in the downstream system.
- Set per-call and per-workflow limits for records, recipients, amounts and duration.
- Remove unused tools and permissions rather than relying on prompts to discourage their use.
Monitoring can reveal undesirable behavior and rate limits can reduce the number of harmful calls before detection. Neither one prevents an excessive operation by itself, so retain a preventive gate for actions that require approval.
Step 4: Build a meaningful approval preview
The reviewer should be able to answer “What exactly will happen, to whom, where and with what consequence?” without reconstructing the agent’s conversation. Render structured fields from the normalized request, not an untrusted natural-language summary.
Include these fields
- Operation: the exact versioned action, such as
delete_customer_records. - Actor: the requesting agent, user and delegated identity.
- Target: tenant, environment, resource identifiers and recipient destinations.
- Scope: record count, amount, recipients, fields and time range.
- Material parameters: filters, message content, code hash, deployment version or payment amount.
- Expected consequence: what will change, be disclosed, sent or committed.
- Policy reason: the rule that raised the gate and any applicable limit.
- Expiry: the time by which approval must be used.
For large scopes, show a deterministic query or a signed digest plus a way to inspect the full set. Approval of “the current search results” is unsafe if the results can change before execution.
Step 5: Bind approval to the exact action
Approval should produce a short-lived authorization artifact—such as a signed decision record or opaque token—that the executor can verify. Bind it to all values that determine the effect:
- the authenticated actor and, where relevant, the requesting user;
- the specific tool and operation version;
- the target resource, tenant and environment;
- normalized parameters, including recipient lists, filters, amounts and code or content hashes;
- the approval identity, timestamp, policy version and risk level;
- an expiry time and a unique request or nonce identifier.
At execution, recompute the normalized request and compare it with the approved values. If a recipient, amount, query, code hash, target or other material parameter changes, reject the call and create a new approval request. Do not let the model edit the approval artifact or decide that a change is immaterial.
Prevent replay and duplicate execution
Mark an approval artifact as consumed for one-time operations. Use a nonce, idempotency key or equivalent replay check, and reject requests presented after expiry. For irreversible actions, combine short validity with a server-side record that prevents reuse. If the downstream API lacks idempotency, require an explicit duplicate confirmation and reconcile the result before retrying.
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Step 6: Enforce the gate outside the model
The execution component must be the final choke point. A typical request path is:
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- The policy service resolves the operation, context and risk level.
- If approval is required, the workflow presents the normalized preview to an authenticated reviewer.
- The reviewer approves or rejects; the service stores the decision and issues a short-lived artifact.
- The agent submits the original request and artifact to the executor.
- The executor independently checks identity, downstream authorization, policy version, target, parameter digest, expiry, nonce and approval status.
- Only after every check passes does the executor call the downstream system.
Never place the final check in a prompt, tool description or agent callback. A compromised prompt, model error or tool response must not be able to skip the policy service. Downstream systems should enforce their own authorization as a second boundary.
Fail closed
Deny execution when risk classification, policy lookup, approval validation, identity verification or audit logging is unavailable. A timeout must not be interpreted as approval. Provide a safe queue or manual recovery path so operators can investigate without allowing an unvalidated call through.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 7: Preserve an audit trail
Store an append-only or otherwise tamper-evident record sufficient to reconstruct the decision and result. Capture:
- the original user request and agent proposal;
- the normalized operation and parameter digest;
- policy version, risk classification and reason for the decision;
- approver identity, authentication strength, decision, scope and timestamp;
- artifact identifier, expiry and replay or consumption status;
- executor identity, downstream authorization result and execution timestamp;
- the downstream response, affected-resource identifiers and partial-failure details;
- rollback, interruption, duplicate confirmation or incident-response actions.
Protect logs from modification by the agent and restrict access to authorized investigators. Avoid copying sensitive payloads into broad logs when a secure reference, digest and controlled evidence store can provide the same traceability.
Worked patterns for common high-risk actions
External email
Separate drafting from sending. The agent may prepare a draft automatically, but the send operation should include the final recipients, subject, body, attachments and sender identity in the preview. Recompute a digest at execution; any recipient or content change requires renewed approval. Apply recipient-count limits and prevent hidden blind-copy expansion.
Database deletion
Require a deterministic selection query, environment name, estimated count and retention impact in the preview. Use a narrow deletion operation rather than unrestricted SQL. Require step-up authentication for bulk or irreversible deletion, a one-time artifact and an independently verified backup or recovery plan. Reject execution if the selected set changes between approval and execution.
Payment or funds transfer
Bind approval to source account, beneficiary, currency, amount, schedule and idempotency key. Use step-up authentication and a short expiry. Enforce transaction and daily limits in the payment system, not only in the agent layer. Treat a retry after an uncertain response as a duplicate-risk event requiring reconciliation.
Production deployment
Show the environment, artifact or commit hash, configuration changes, migration steps, expected downtime and rollback reference. Require an approver who is authorized for that environment, and block execution if the artifact hash or target cluster differs from the approved values.
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How do I stop an AI agent from taking a risky action without approval?
Put the agent behind an execution proxy that is the only component allowed to reach consequential tools. The proxy should reject any request without a policy decision and, where required, a matching, unexpired approval artifact. Remove direct credentials from the model runtime, use narrowly scoped service identities, and enforce authorization again in each downstream system. Then add replay protection, rate limits, monitoring and tamper-evident logs. Human review alone is insufficient if the agent can call a second route that bypasses the gate.
Common bypasses to test
- The model changes a tool name, API route or parameter after approval.
- A supposedly read-only endpoint accepts an undocumented write flag.
- The agent invokes a shell, arbitrary HTTP client or alternate credential.
- A policy service outage causes the caller to treat “unknown” as “approved.”
- A valid approval token is replayed for a second execution.
- A dynamic query selects more records after approval than it showed in the preview.
- A retry repeats a non-idempotent payment, message or deletion.
For each case, verify that the executor rejects the request, records the reason and leaves the downstream system untouched.
Regulatory and standards context
The European Commission notes that “AI agent” is used inconsistently and broadly describes systems that process environmental input and execute actions affecting their environment. Agents are addressed through the AI Act’s existing AI-system and general-purpose AI provisions; they are not automatically a separate legal category. Whether a deployment is high-risk depends on its intended purpose and whether it falls within a listed use case or a regulated product.
Commission guidance gives application dates of 2 December 2027 for certain high-risk use cases, 2 August 2028 for certain AI systems embedded in regulated products, and 2 August 2026 for certain transparency rules. These dates can change and may interact with national or sector requirements. Check the current official sources—the Commission’s FAQ on AI agents and Navigating the AI Act—for the deployed system’s purpose and jurisdiction. Do not classify every agent as legally high-risk merely because it can use tools.
NIST’s AI Agent Standards Initiative describes voluntary standards, protocol and research work, including agent authentication, identity infrastructure and enterprise identity and authorization activity. The cited page does not prescribe a finalized approval-gate standard, so use it as context rather than a compliance checklist.
Quick Recap
Deployment checklist
- Every callable operation has an owner, target, scope, privilege and recovery description.
- Only explicitly mapped low-risk operations can bypass review; unmapped operations fail closed.
- High-impact and irreversible operations show a complete, structured preview.
- Approval is bound to actor, tool, target, normalized parameters, policy version, timestamp and expiry.
- The executor—not the model—checks authorization, approval, expiry and replay status.
- Critical operations require step-up authentication and one-time authorization.
- Credentials, tools, records and environments are limited to the minimum required.
- Retries are idempotent or require duplicate confirmation and reconciliation.
- Policy failures, logging failures and service timeouts deny execution.
- Audit records include proposal, decision, approval identity, execution result and recovery actions.
- Tests cover alternate routes, parameter changes, prompt injection, replay, scope expansion and partial failure.
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