Give an AI agent two separate boundaries: a narrowly scoped identity with only the permissions it needs, and a tool interface that exposes only the functions required for its job. Put authorization and risk checks in trusted application code or downstream services, not in the model. Require a human decision immediately before consequential side effects, bind that decision to the exact normalized action and target, fail closed when a check cannot be completed, and record the entire transaction in an auditable log.
This design answers the practical zero-trust questions raised by the NIST NCCoE’s February 2026 concept paper while recognizing that the paper solicits feedback rather than defining mandatory controls. OWASP and OpenAI implementation guidance supplies concrete patterns; your thresholds must still reflect your data, jurisdiction, and business risk.
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What human approval and access control should accomplish
An agent can plan, call tools, and chain actions faster than a person. That capability becomes dangerous when the model receives a broad token, can discover unneeded functions, or can turn an ambiguous instruction into an irreversible side effect. A safe design limits authority at several independent layers:
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- Identity and delegation: every call has an attributable agent principal and, where applicable, a narrowly delegated user context.
- Policy enforcement: trusted code validates the actor, target, arguments, and action class before execution.
- Human review: a person approves a specific consequential operation before it runs.
- Auditability: intent, policy decisions, approvals, execution, and failures are recorded so the event can be investigated.
These are separate control surfaces. Removing a dangerous function from the prompt does not revoke the downstream permission, and revoking a permission does not prevent the model from attempting the call. Use both controls.
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Start with a threat-informed authority model
Distinguish planning from authority
The model may suggest an operation, but it cannot authorize itself. A tool gateway, application policy engine, IAM layer, or the downstream service must make the allow-or-deny decision. Treat every model-produced tool name, identifier, and argument as untrusted input. Validate types, canonicalize identifiers, and reject fields that the tool contract does not define.
Bind the agent to an identity
Create a distinct principal for each agent or agent role. Do not place a person’s password, long-lived API key, or a generic administrator credential in the prompt or runtime environment. When an agent acts for a user, carry the user’s authorization context through a verifiable delegation mechanism and attenuate it to the task. Credential sharing creates an accountability gap: a log may show which credential was used but not which agent, user, or approval authorized the action.
Define risk and reversibility before deployment
Classify each action in policy, including its data scope, target, reversibility, and impact. Do not let the model decide that an operation is “safe” at runtime. A read-only lookup over an approved dataset may run without interruption. Sending an external message, changing permissions, deleting data, moving money, changing infrastructure, or altering security settings normally deserves a review because the consequence is external, destructive, financial, administrative, or difficult to reverse.
OWASP’s guidance states the principle plainly: “Require explicit approval for high-impact or irreversible actions.” The exact threshold is an application decision, not a universal number.
Seven-step setup sequence
1. Inventory legitimate tasks and functions
Write a task statement for the agent and map every required operation to a function. Include the data sources, objects, fields, and maximum result size. Remove exploratory extensions and open-ended functions that are not required.
For example, a mail-triage agent might need list_messages and read_message for a labeled mailbox. It should not receive send_message, delete_message, change_forwarding, or an unrestricted search function. If a later feature needs one of those operations, add it as a new, reviewed capability rather than widening the original tool.
- Record the business owner and security owner for each task.
- Specify allowed resources, fields, geographic boundaries, and time windows.
- Document expected failure behavior and whether a human must be present.
- Version the tool contract so a new argument cannot silently expand authority.
2. Create a bounded identity and delegation
Register the agent as a principal with a stable identifier such as an internal workload identity. Give it a role that contains only the downstream permissions needed for the inventory. Separate read and write roles when possible. Use short-lived, audience-restricted credentials and rotate signing keys according to your identity platform’s policy.
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For user-directed work, represent both identities in the request: the agent principal and the delegating user. A policy should be able to answer “which agent, acting for which user, may perform this action on this target?” Preserve the user’s original scope, then reduce it further for the task. Never substitute a high-privilege service account merely because the user lacks a convenient API token.
3. Enforce policy outside the model
Place a mediation point directly before every side effect. The mediator receives a structured request containing the agent identity, delegated user, tool identifier, normalized arguments, target resource, action class, and correlation ID. It authenticates the caller, checks the policy, and only then invokes the downstream service.
In a manager-style workflow, checks around custom tools belong next to the tool that causes the side effect. A prompt instruction such as “ask before deleting” is useful context but is not a security boundary. The downstream service should still reject an unauthorized or malformed request if the mediator is bypassed.
4. Classify actions before runtime
Create a policy registry rather than inferring risk from a natural-language description. A minimal record can contain:
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|---|---|
| Action name and version | Stable reference to the tool operation and its contract. |
| Impact class | Read, write, communication, destructive, financial, administrative, permission-changing, or infrastructure. |
| Reversibility | Whether a tested, bounded undo exists and its time limit. |
| Approval rule | Who may approve, whether one or more approvers are needed, and any escalation path. |
| Resource scope | Permitted tenants, projects, records, fields, and environments. |
| Argument constraints | Allow-lists, limits, and validation rules for each parameter. |
Read-only inspection can be automatic only when its scope and data sensitivity justify that choice. A “read” that exports personal data, reveals secrets, or crosses a tenant boundary may need the same review as a write.
5. Pause for a specific approval
When policy marks a call as gated, stop before the side effect. Present the approver with the actual tool, target, material arguments, expected effect, requesting agent, delegated user, and expiration. Avoid a generic prompt such as “Allow agent?” because it hides what will happen.
Normalize the arguments first: canonicalize resource IDs, sort set-like fields, apply default values, and remove irrelevant fields. Bind the approval to a digest of that normalized request, the policy version, the target, and a short expiry. If any material argument changes, require a new decision. Use a nonce, one-time approval record, or equivalent replay protection for high-impact calls.
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6. Resume the same workflow and audit it
Approval should create an interruption with resumable state, not a second, improvised request. In the OpenAI Agents SDK pattern, the application receives the pending call, records the decision, and resumes the same run after approval or rejection. Preserve the original conversation and correlation ID while preventing the model from replacing the approved arguments.
At minimum, record:
- agent principal, delegated user, tenant, and authentication method;
- run and correlation identifiers;
- tool name and version, normalized arguments, target, and action classification;
- policy version and allow-or-deny result;
- approver identity, decision, timestamp, reason, expiry, and approval binding;
- execution start, downstream response, result status, and retry count;
- validation, timeout, logging, or policy errors.
Protect the audit stream against alteration with access controls, append-oriented storage, time synchronization, and integrity verification appropriate to your environment. NIST identifies verifiable logs and binding authorization to human identity as design questions; it does not prescribe one universal schema in this concept paper.
7. Keep review meaningful
Approval fatigue is a security problem. If people approve dozens of low-risk prompts, they may click through a dangerous one. Automate ordinary least-privilege checks and reserve interruption for actions whose impact warrants human attention. Grouping independent, low-impact reads can reduce noise, but never bundle materially different side effects under one vague approval. Provide a clear reject, modify-and-resubmit, and escalate path.
What an approval request should contain
A reviewer needs enough context to make a decision without reconstructing the run. Render a human-readable summary alongside machine-verifiable data:
- Who: agent identity, delegated user, owning team, and authentication age.
- What: exact tool and version, action class, and a plain-language effect.
- Where: tenant, environment, resource identifiers, and destination for external communication.
- With which values: material arguments after normalization; redact secrets while retaining a stable digest.
- Why now: the agent’s stated intent, relevant triggering event, and policy reason for requiring review.
- How risky: reversibility, blast radius, data sensitivity, and available rollback.
- How long: expiration time and whether the approval is single-use.
Display changes from the last attempt if the agent revises a request. Do not let a model-generated explanation replace the structured fields used by policy.
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Failure handling: fail closed without losing recoverability
Define failure behavior before enabling the agent. If authentication, policy lookup, argument validation, approval, replay protection, or required audit logging fails, do not execute the side effect. Return a typed error, retain the pending state when safe, and tell the operator what must be corrected.
| Failure | Safe response |
|---|---|
| Malformed or extra arguments | Reject before authorization; do not silently drop fields that could change meaning. |
| Unknown tool or policy version | Block and route for registration or policy update. |
| Approval timeout or expired token | Invalidate the decision and request a fresh review. |
| Target changed after approval | Recompute the binding and require approval again. |
| Downstream timeout after submission | Mark outcome unknown, prevent blind retries, and reconcile with the service before retrying. |
| Audit store unavailable | Fail closed for gated actions; queue only if the queue itself is integrity-protected and policy permits it. |
| Partial multi-step workflow | Record completed steps, stop further side effects, and expose a compensating action or operator recovery plan. |
“Fail closed” does not mean discarding context. Keep enough protected state to investigate and resume safely, while ensuring that a retry cannot reuse an old approval.
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Implementation approaches compared
Teams commonly combine a framework’s approval feature, a custom approval service, and downstream IAM. No single layer replaces the others. The following comparison uses the control questions that matter when selecting an architecture.
| Control axis | Framework approval feature | Custom approval service | Downstream IAM and service policy |
|---|---|---|---|
| Permission granularity | Usually tool- or call-level; depends on the framework contract. | Can express argument, resource, field, and business-rule constraints. | Strong for resource and operation permissions; may not understand agent intent. |
| Agent and user identity | Must be supplied and preserved by the application. | Can require both principals and validate delegation centrally. | Usually evaluates the service identity; user attribution needs explicit propagation. |
| Binding to normalized arguments and target | Available only if the integration hashes and rechecks the request. | Can make binding, expiry, and one-time use first-class. | Often authorizes the final request but may not retain a human approval object. |
| Enforcement location | Agent runtime or orchestration layer. | Dedicated gateway or policy service before the side effect. | Resource service and identity provider. |
| Failure behavior | Verify timeout, malformed-input, and logging defaults; do not assume fail-closed. | Can mandate fail-closed behavior and typed recovery paths. | Typically denies unauthorized requests; availability and audit semantics vary. |
| Audit and replay protection | Often provides run events; add immutable storage and one-time approval controls. | Can centralize decision records, digests, expiry, and replay detection. | Provides service access logs; correlate them with agent and approval IDs. |
| Human interruption volume | Easy to tune by tool or action class; poor defaults can cause fatigue. | Can risk-score and route approvals, but adds operational complexity. | Normally no human interruption; reserve IAM for automatic enforcement. |
Use framework controls for workflow interruption, a policy service for application-specific decisions, and downstream IAM as the final authority over resources. Test the combination rather than assuming that a successful framework approval implies service authorization.
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Permission and identity tests
- Call every exposed tool with an allowed and disallowed resource.
- Attempt to invoke an unexposed function and a disabled version of an exposed function.
- Use a valid agent with an expired, unrelated, or over-broad delegated user context.
- Verify that logs identify both principals and that credential substitution is rejected.
Approval integrity tests
- Change one material argument, target, policy version, or tenant after approval and confirm denial.
- Replay an approved token and confirm it is single-use or otherwise rejected.
- Submit malformed JSON, unknown fields, duplicate keys, and boundary values.
- Let the approval expire, then verify that execution stops and a new decision is required.
Resilience and audit tests
- Make policy, identity, approval, downstream, and audit dependencies time out independently.
- Confirm no gated side effect runs when logging cannot establish the required record.
- Force a downstream ambiguous timeout and verify reconciliation before retry.
- Inspect whether an operator can reconstruct intent, decision, arguments, and outcome from the audit trail.
Operating the controls over time
Review the task inventory whenever a prompt, tool schema, model, downstream API, tenant boundary, or business process changes. Expire unused permissions and approval routes. Monitor denied calls, argument-validation failures, approval latency, rejection rates, repeated retries, and changes in interruption volume. A sudden increase in approvals may indicate a workflow regression; a sudden decrease may indicate that a gate was bypassed.
Keep policy and tool versions tied to deployments. Roll out changes in a non-production environment with representative identities and resources, then verify that the downstream service still enforces the intended scope. Train approvers to inspect destination, target, and irreversible effects rather than merely the model’s explanation.
Common mistakes and their corrections
“The system prompt says the agent must ask first.”
Correct it with a pre-side-effect enforcement point. Prompts guide behavior; they do not authenticate actors or prevent a crafted tool call.
“The model classified this as low risk.”
Use a versioned policy registry and trusted code to classify the action. Treat the model’s label as an untrusted hint at most.
“One service account is simpler.”
Separate agent and user identity, attenuate delegation, and grant the service only the minimum resource permissions. Simplicity that destroys attribution is an audit and containment liability.
“We can approve the workflow once.”
Bind approval to each consequential operation, or to a narrowly defined, independently safe batch whose members and limits are visible. A changed target or argument requires a new decision.
“More prompts mean more safety.”
Place automatic controls around routine checks and reserve people for meaningful risk decisions. Excessive prompts produce reflexive consent.
“Logs can be added later.”
Design the event fields, integrity protection, retention, and correlation IDs before launch. Without them, a failed or disputed action may be impossible to reconstruct.
A practical minimum design
For a first production release, implement one narrowly scoped agent, a small versioned tool set, a distinct workload identity, explicit user delegation, a policy gateway, and a single approval flow for writes and external communication. Make the gateway reject unknown arguments and stale approvals, make the downstream service enforce its own permissions, and require an auditable event before execution. Expand capabilities only after tests demonstrate that the new tool, identity scope, approval rule, and recovery path work together.
Frequently Asked Questions
Should every agent action require a person to click approve?
No. Approval is for actions whose impact, sensitivity, or irreversibility merits human attention. Scoped, read-only work can be automatic when policy allows it; automatic identity, argument, and downstream authorization checks still apply.
Can an approval cover several tool calls?
Only when policy defines a bounded batch in advance, shows every material target and limit, and treats the batch as one clearly understood risk. If a target, argument, or action class changes, obtain a new approval.
What should happen if the downstream API times out after approval?
Treat the result as unknown, prevent an automatic blind retry, and reconcile with the downstream system using the correlation or idempotency identifier before deciding whether another attempt is safe.
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How long should approval records and agent logs be retained?
Set retention according to your legal, contractual, incident-response, and data-minimization requirements. The cited guidance does not establish one universal period; document the rationale and protect the records against unauthorized alteration.
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