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Give an AI agent only the tools and delegated authority its task requires, enforce authorization in trusted application or downstream systems, and pause before consequential side effects for a decision tied to one normalized action. Human approval is an additional control—not a substitute for least privilege, identity binding, policy enforcement, or tamper-evident auditing.
What a safe agent-control design must accomplish
An agent can plan, call tools, and carry context across several steps. That flexibility is useful, but it also creates a path from an ambiguous request to an external side effect. A secure design separates what the model proposes from what the system permits.
Your control design should answer five questions for every tool call:
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- What exactly will happen? Normalize the operation, target, and material arguments before policy evaluation.
- Is the operation allowed? A trusted policy enforcement point—not the model—must make this decision.
- Does a person need to decide? Require approval for consequential, difficult-to-reverse, or security-relevant effects.
- What evidence remains? Record the request, policy decision, approval or denial, execution result, and relevant identity context.
The NIST NCCoE February 2026 concept paper frames questions about applying zero-trust principles, establishing least privilege when actions are not fully predictable, binding agent and human identity, and creating verifiable logs. It solicits feedback; its questions are not normative requirements. OWASP and OpenAI implementation guidance provide practical patterns, but your thresholds must reflect your data, application, and governing environment.
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Map the two independent control surfaces
There are two different places to reduce authority, and securing only one leaves a gap:
- Agent-side capability: the tools, extensions, functions, parameters, and data sources the runtime exposes to the model.
- Downstream authority: the permissions held by the identity that reaches a database, mail service, ticket system, cloud API, or other target.
Restrict both. Removing a function from the prompt or tool registry is not enough if the runtime can still invoke a generic HTTP client with broad credentials. Conversely, a narrow tool list does not protect a downstream service if its token can administer an entire tenant.
Step 1: Inventory tasks and tools before deployment
Start with a task inventory, not a list of available APIs. For each legitimate task, document the smallest operation that can complete it and the data boundary it needs.
Build a task-to-function map
| Task | Minimum capability | Remove |
|---|---|---|
| Summarize incoming mail | Read messages in the selected mailbox or folder | Send, forward, move, label, and delete functions |
| Create a draft response | Read the conversation and create a non-sent draft | Send, publish, or change mailbox settings |
| Open a support ticket | Create a ticket in an approved project with validated fields | Delete tickets, alter project permissions, or administer the service |
OWASP recommends minimum necessary extensions and granular functionality. Prefer separate functions such as create_draft and send_message over one broad mail_action function whose behavior depends on model-supplied arguments. Keep read and write operations separate so policy can classify them differently.
Specify targets and data boundaries
Record allowed repositories, folders, projects, tenants, recipients, and record types. A function that reads “the mailbox” or “the database” is difficult to constrain later. Make the target an explicit, policy-checked field and reject values outside the delegated scope.
Remove open-ended escape routes
Do not expose shell execution, arbitrary network requests, unrestricted file access, or a generic administrative SDK when a task can use a narrow wrapper. If an integration genuinely needs a broad primitive, put a separate policy gateway in front of it and classify every operation that gateway receives.
Step 2: Create a bounded identity and delegated scope
Give each agent an identifiable principal. The principal can be a workload identity, service identity, or equivalent account that your authorization system can distinguish from a human and from other agents. Never give an agent a person’s password, session cookie, or a shared high-privilege account.
Preserve the user’s authorization context
When an agent acts for a user, carry a verifiable link between the agent principal and the delegated user. The downstream service should be able to evaluate both identities and the delegation’s limits. Delegation should be attenuated: an agent may receive a subset of the user’s permissions, a shorter lifetime, fewer resources, or fewer operation types.
Credential sharing creates accountability gaps, as NIST warns. OWASP recommends execution in the user’s context with minimum privilege. In practical terms, the audit record should answer “which user authorized this delegation?” and “which agent instance performed this call?” without relying on a free-text username supplied by the model.
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Use short-lived, narrowly scoped credentials
Issue credentials for the specific workflow or session where possible. Bind them to allowed audiences, resources, operations, and expiry. A token that can call an entire administrative API defeats a later approval prompt: the process still has standing authority if the prompt is bypassed or misconfigured.
Keep identity data out of model-controlled fields
The model may propose a recipient, project, or record identifier, but it must not choose the principal, privilege set, or approving user. Derive those from the authenticated session and server-side delegation state. Reject requests that attempt to override identity claims through tool arguments or natural-language instructions.
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A model can suggest an action and explain why it believes the action is safe. It cannot grant itself permission. Authorization belongs in the application, tool boundary, IAM policy engine, or downstream service that can authenticate the caller and inspect the real target.
Use complete mediation
Every side-effecting call should pass through the enforcement point, including retries, background jobs, resumed runs, and calls initiated after a handoff between agents. Do not authorize only the first conversational turn. Re-check the actor, target, operation, and current policy immediately before execution.
Validate the actual arguments
Policy should evaluate normalized arguments rather than a model-generated explanation. Normalize case, aliases, resource paths, recipient formats, and numeric limits before comparison. Reject malformed, missing, or ambiguous values. For example, a policy allowing refunds up to a limit must evaluate the parsed amount and order identifier, not the text “small refund” in a rationale.
Place checks beside the side effect
OpenAI guidance for manager-style workflows places checks around custom tools next to the tool that causes the side effect. The same principle applies to other runtimes: a planner may request send_message, but the send wrapper or downstream mail service must enforce recipient, content, tenant, and approval requirements.
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An agent-generated label such as “low risk” is untrusted input. A separate classifier can help route work, but the final decision must use trusted policy data, authenticated identity, target state, and declared action properties.
Step 4: Classify actions before runtime
Declare the risk and reversibility of each operation in policy or tool metadata. OWASP says the reversibility class should be declared on the action, not inferred by the agent at runtime. Classification should consider impact, scope, external visibility, data sensitivity, and how reliably the effect can be undone.
| Action category | Typical treatment | Why |
|---|---|---|
| Read-only inspection | Permit automatically when identity, target, and data scope are valid | It has no direct external side effect, though sensitive-data access still needs authorization and logging |
| Draft or staged write | Permit or review according to data sensitivity and audience | The result is usually reversible before publication |
| External communication or publication | Require review when recipients, content, or reputation create material impact | Once sent or published, recall may be incomplete |
| Destructive operation | Require explicit approval and strong target confirmation | Deletion or overwrite may be irreversible |
| Financial action | Require approval tied to amount, account, and recipient | Money movement and contractual effects are high impact |
| Administrative or permission change | Require approval and an independent policy check | It can expand authority or affect many users |
| Infrastructure or security change | Require approval, change controls, and rollback planning | Availability and security consequences can be broad |
These are starting categories, not universal thresholds. A read operation over regulated records may deserve more scrutiny than a low-value internal draft. Define the criteria in your environment and version the policy so an audit can show which classification was in force.
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Step 5: Design a specific, replay-resistant approval
When an action is gated, pause before the tool executes. Present the approver with the exact tool, target, and material arguments that will be sent to the enforcement point. A generic prompt such as “Allow the agent to continue?” does not provide meaningful consent.
Bind approval to a normalized action
Create a canonical representation containing at least:
- Agent principal and delegated human identity
- Tool or operation name and version
- Normalized target resource
- Material arguments, including amounts, recipients, filters, and scope
- Policy decision and risk classification
- Creation time, expiry, and a unique approval identifier
Hashing or otherwise signing this representation can help detect changes between approval and execution. The approval service and executor must compare the same canonical form; an approval for one recipient, amount, or resource must not be reusable for another.
Set expiry and replay protection
High-impact approvals should expire quickly enough to prevent stale decisions and should be single-use. Bind the approval to the pending run, tool call, and target. Reject duplicate submissions, changed arguments, and approvals presented after policy or resource state has changed.
Show material context without overwhelming the approver
Include the reason the workflow requested the action, but distinguish explanation from authority. Show who requested the work, which identity will execute it, what data will leave the system, who will receive it, and what cannot be undone. Avoid dumping an entire transcript when a concise, accurate action summary is sufficient.
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Step 6: Pause, resume, execute, and audit the same run
In the OpenAI Agents SDK approval pattern, a pending approval creates an interruption and resumable run state. The application approves or rejects the pending tool call, then resumes that run. Preserve the pending call rather than asking the model to recreate it from memory; regeneration can change arguments after approval.
Reference workflow
- The agent proposes a tool call.
- The application normalizes arguments and identifies the agent and delegated user.
- The policy layer checks identity, target, scope, risk, and current resource state.
- If approval is required, the application records a pending decision and presents the exact action to an authorized approver.
- The approver approves or rejects that specific pending call.
- The application verifies approval integrity, expiry, and single-use status, then re-checks policy.
- The tool executes with the bounded downstream credential.
- The application records the result and resumes the same workflow with the tool output.
Fail closed at every decision boundary
If policy lookup, approval verification, argument parsing, identity resolution, or required audit logging fails, do not execute a consequential action. Return a clear error, preserve enough state for safe recovery, and require a new decision when the original approval can no longer be trusted. A timeout must not silently become “approved.”
Record an audit trail that can be verified
Capture the request and execution as related events. Useful fields include:
- Unique run, tool-call, approval, and execution identifiers
- Agent principal, delegated user, approver, and authentication context
- Original and normalized arguments, target, policy version, and risk class
- Approval or denial decision, timestamp, expiry, and reason
- Execution result, downstream request identifier, and error details
- Links to preceding and following events needed to reconstruct the workflow
Protect the trail against unauthorized alteration and make verification practical for incident responders. NIST identifies verifiable logs and binding authorization to human identity as open design problems; it does not prescribe one universal schema in the February 2026 concept paper. Choose retention, access, redaction, and integrity controls appropriate to your environment.
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Step 7: Keep human review meaningful
Approval is valuable only when a person can notice and reject a bad action. NIST warns that frequent prompts create consent fatigue and reflexive approvals. Route routine, low-impact decisions through automatic least-privilege checks and reserve human attention for actions whose consequences justify it.
Reduce unnecessary interruptions
- Allow scoped read-only work to proceed automatically after policy validation.
- Group genuinely independent low-risk operations only when the approval clearly lists each operation and your policy permits grouping.
- Do not group actions when one item can change the risk of another, such as adding a recipient and then sending data.
- Escalate unusual targets, amounts, recipients, or privilege changes instead of prompting for every normal case.
Make rejection and recovery safe
A rejection should stop the pending side effect without granting the model a bypass path. Return a structured reason that helps the workflow ask for a safer alternative, such as creating a draft instead of sending a message. Do not expose sensitive policy internals merely to explain a denial.
Implementation approaches and their trade-offs
Teams commonly combine a framework’s approval feature, a custom approval service, and downstream IAM controls. They are complementary layers, not interchangeable products.
| Control area | Framework approval feature | Custom approval service | Downstream IAM or service policy |
|---|---|---|---|
| Permission granularity | Usually follows the framework’s tool and argument model; split tools for finer control | Can model operation, field, resource, and risk explicitly | Uses the target system’s resource and action permissions |
| Agent and delegated-user identity | Must be supplied and verified by the application | Can maintain a central delegation and approval record | Can enforce authenticated caller and delegated claims at the resource boundary |
| Binding to normalized arguments and target | Requires application logic to preserve and validate the pending call | Can issue a single-use decision over a canonical action | Re-evaluates the actual request received by the service |
| Authorization location | Runtime interruption and tool wrapper | Policy gateway or approval API before execution | Downstream service or IAM enforcement point |
| Failure handling | Configure the application to deny on timeout, malformed arguments, or unavailable approval state | Can define explicit fail-closed contracts and health checks | Depends on the service’s deny-by-default and outage behavior; verify it |
| Audit and replay protection | Run state and application logs need correlation and integrity protection | Can centralize immutable event linkage, expiry, and single-use checks | Provides resource-side records but may not contain the human approval context |
| Human interruption volume | Controlled by tool metadata and workflow design | Can route, batch, and escalate according to organization-wide policy | Often automatic; human review must be added where required |
No single layer proves that the whole workflow is safe. A framework interruption can improve user experience, while downstream authorization prevents a forged or stale approval from producing an unauthorized effect.
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Suppose an assistant summarizes mail, drafts replies, and sends messages for a user.
Tool inventory
read_message: read only, limited to folders the user delegated.create_draft: write a draft in the user’s mailbox; no external delivery.send_message: external communication; requires recipient and content policy checks and, for the chosen risk class, human approval.- No delete, forwarding-rule, mailbox-administration, or arbitrary HTTP function.
Identity and policy path
The runtime authenticates as an identified agent principal linked to the user session. The mail service receives a delegated, least-privilege token. Before sending, the wrapper normalizes recipients, attachments, and mailbox target; checks that the user may send to those recipients; verifies content and data-loss rules; and creates a pending approval containing the exact message.
Approval and execution
The approver sees the recipients, subject, attachment names, data classification, sending identity, and expiry. Approval is bound to the canonical message and pending run. After approval, the wrapper re-checks policy, sends once, records the mail-service request identifier, and resumes the workflow with the result. If the message changes, the approval is invalid and a new decision is required.
Testing before production
Test the controls as adversarially as the agent itself. Include at least these cases:
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- A prompt asks the agent to call a function that is not in its allowed tool set.
- A valid tool is given an out-of-scope tenant, folder, project, recipient, or record.
- Arguments are malformed, ambiguous, duplicated, or changed after approval.
- The delegated user’s permission is revoked between planning and execution.
- An approval expires, is replayed, belongs to another run, or is presented by another approver.
- The policy service, approval interface, identity provider, or audit sink is unavailable.
- The model labels a destructive operation as low risk.
- A retry or resumed run attempts to execute an already completed side effect.
- A user denies an action and the agent tries an alternate tool with equivalent effect.
Verify that each case produces a deny or safe pause, a useful operator-visible error, and an audit event. Also test that ordinary approved work remains usable; an approval system that users routinely bypass or blindly accept is not effective oversight.
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Operational governance and maintenance
Version policies and tool contracts
Record the policy and tool-contract versions used for every decision. Review changes to scopes, resource selectors, risk classes, approval routing, and downstream credentials as security-sensitive changes.
Monitor for authority drift
Compare the inventory of exposed functions and effective downstream permissions over time. Alert when a new extension appears, a token gains a broader scope, an agent begins using an unusual target, or approval rates approach automatic rubber-stamping.
Review incidents and near misses
Use correlated run and tool-call identifiers to reconstruct what the model proposed, what policy allowed, what a person approved, and what the service executed. Update the task inventory and action classification when a new failure mode appears; do not solve every incident by adding another prompt.
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Answers to the design questions teams ask first
How can zero-trust principles be applied to agent authorization?
Authenticate every agent and delegated user, authorize each request against the specific target and operation, minimize standing privilege, and re-check policy at the side-effect boundary. Do not trust a model’s plan, a previous turn, or a network location as proof of authority.
How do we establish least privilege when actions are unpredictable?
Constrain the action vocabulary and resource scope even when the sequence is open-ended. Use narrow tools, explicit target selectors, bounded arguments, short-lived delegation, and downstream checks. If a required action cannot be described or constrained, treat that uncertainty as a reason to redesign the tool or require stronger review—not as permission to expose a generic administrator function.
Can a human approval prompt replace authorization?
No. A person may approve an action they are not entitled to authorize, or a stale approval may be replayed. Identity, scope, policy, and downstream enforcement must still reject an unauthorized call.
What should happen when logging is unavailable?
For consequential actions, fail closed. Do not execute without the required audit evidence. Provide a recovery path that preserves the pending state and requires a fresh, verifiable decision if the original approval expires.
How often should agents ask for approval?
Ask when the impact, irreversibility, external visibility, or privilege change warrants human judgment. Automate ordinary least-privilege checks and low-risk work. Measure denials, expiry, repeated approvals, and bypass attempts to detect consent fatigue and adjust thresholds.
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