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Put approval controls at the point where an AI agent is about to change something—not in a prompt asking the model to remember to check first. Before a tool runs, application or orchestrator policy should verify the action, target, arguments, identity, and scope. Let routine, narrowly authorized actions proceed; pause ambiguous or high-impact actions for an informed human decision; and block prohibited actions outright.
What a reliable approval system does
A review request is useful only if the agent cannot bypass it and the system checks the proposed action before the side effect. OpenAI’s guidance puts it plainly: “Put validation next to the tool that creates the side effect.” A log written after execution can help explain what happened, but it cannot prevent an unsafe change.
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Keep three controls distinct:
- Deterministic authorization: Application or orchestrator policy decides whether an action may execute, needs approval, or is prohibited.
- Human review: An authorized person evaluates a specific proposal and approves or rejects it.
- Model-generated check-ins: A conversational agent may ask a user a question, but that request is not itself an authorization control.
Microsoft recommends meaningful oversight, deterministic controls, and least privilege. Its guidance says to require approval for high-risk or irreversible actions. These are design recommendations, not a measured guarantee that any particular approval workflow will reduce risk by a stated amount.
Decide which actions are allowed, reviewed, or blocked
Start by listing each tool the agent can call and the downstream operations it can trigger. Assess the consequences of each operation in your own environment, including its impact, reversibility, scope, sensitivity, and privilege. A read or draft is not equivalent to sending a message, changing access, spending money, exporting information, deleting data, or applying a bulk update.
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Set three explicit outcomes, with no permission granted by default:
- Allow: A bounded set of routine actions that can run without an interruption, within a defined task and permission scope.
- Require approval: Actions that are ambiguous, high-impact, privileged, bulk, destructive, or difficult to reverse. Microsoft specifically identifies bulk updates, destructive or high-impact changes, and regulated data as cases for additional controls.
- Deny: Operations the agent must not perform, regardless of what the model proposes or what a user says in a conversational turn.
Do not ask the model to determine whether its own proposed action is permitted. Enforce the rules in the application, orchestrator, or downstream authorization layer. The examples above are categories to assess, not a universal risk classification; teams need to map them to their systems and impact model.
Put the check in the execution path
For every operation that could change external state, evaluate the proposed call immediately before the tool executes. Check the actual target, operation, arguments, calling identity, and applicable task or engagement scope—not just the agent’s summary of its intentions. This matters in chained workflows: OpenAI notes that input and output guardrails do not automatically inspect every custom tool call.
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- Validate it against policy. Confirm the caller, arguments, target, and requested scope are authorized and that the action is not prohibited.
- Route by outcome. Run an allowed action; hold an action requiring review; or reject a denied action without invoking the side-effecting tool.
- Verify the executed result. Record what the downstream system actually did, not only what the agent intended or reported.
The policy check must cover nested or chained calls that can produce the same consequential change. If a high-risk action cannot be authorized or reviewed, fail closed: keep it paused rather than allowing it to run because a reviewer is unavailable.
Give reviewers a decision-ready proposal
A reviewer should be able to understand the exact action and its consequences without guessing what the agent intends. Show the target, operation, relevant arguments, acting identity, affected data or records, and the scope or policy that makes the action eligible for approval. Include only context that helps make the decision.
Provide explicit approve and reject choices, preserve the decision, and offer a way to correct or amend a proposal when the workflow supports it. Do not ask reviewers to submit passwords, PINs, payment-card details, social security numbers, or other secrets in a review response; Microsoft’s computer-use guidance specifically warns against entering sensitive information into review requests.
An approve button alone does not establish meaningful oversight. The reviewer needs authority to reject, enough context to assess the request, and evidence that the action ultimately executed matches the one approved.
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- Rejection: Do not execute the rejected proposal. If a revised action is later suggested, validate it as a new proposal.
- No response or reviewer unavailable: Keep a high-risk action paused or fail closed. Do not treat silence or a timeout as approval.
- Pause and resume: Preserve the pending run and its state so an authorized decision can resume that same run. Resuming saved state also helps avoid repeating work already completed before the pause.
- Stop or revoke: Give an operator a way to pause or stop autonomous behavior, and test how to disable the agent, rotate credentials, invalidate tokens, and remove stale permissions.
Microsoft documents a computer-use workflow that stays paused until a response or its configured timeout; OpenAI describes approval interruptions with resumable state. These are implementation examples, not substitutes for defining your own timeout, rejection, escalation, and recovery behavior.
Limit the agent’s identity and permissions
Give agents distinct identities and task-scoped access. Allowlist only the tools and operations needed for the job, use the narrowest effective roles, and periodically reassess whether those permissions are still necessary. Microsoft’s Entra Agent ID least-privilege guidance discusses scoped roles, allowlisted actions, approval for bulk updates, additional controls for high-impact steps, audit logging, and revocation; it asks teams to adapt the pattern to their architecture and requirements.
Make revocation practical, not theoretical. Test that disabling an agent, rotating credentials, invalidating tokens, or removing a permission actually prevents further actions. Keep the ability to stop the agent separate from its normal task permissions, so an operator can intervene when the workflow itself is not behaving as expected.
Keep records that connect approval to outcome
Use a stable correlation ID to tie together the original task, proposed action, policy evaluation, human decision, tool call, and downstream result. Microsoft recommends capturing agent identity, role, effective scope, action, resource, correlation ID, and the represented user where applicable. It also recommends logging plans, tool calls, decisions, and outcomes.
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- Which agent acted, and what human or service authorized its effective scope?
- What exact action, target, and arguments were proposed, and which policy applied?
- Was review requested, approved, rejected, or left pending until timeout?
- What tool call ran, and what did the downstream system actually change?
- Can the agent’s access be revoked now?
Protect the logs as well as collecting them: restrict access and govern retention, because records may contain sensitive operational details. NIST describes evaluation probes that compare agent claims against curated documents and produce machine-readable trails connecting decisions to evidence. NIST presents this as development work, not a finalized standard or a demonstrated guarantee.
Do not mistake a model prompt for a fail-safe
Microsoft says its computer-use review requests can depend on probabilistic model behavior: a request may not appear when a person would want a pause, or may appear when one is unnecessary. The documentation cautions, “Don’t rely on human review or clarification requests as a fail-safe or as a guarantee that the system always requests human input before proceeding.” Use such prompts to improve interaction, but keep deterministic authorization gates on consequential operations.
Content an agent reads from web pages, files, or screenshots may also be adversarial. Microsoft warns that indirect prompt injection can exploit content encountered during agent interactions, and advises trusted, isolated environments and validation for computer-use agents. Treat incoming content as data to assess, not as authority to override policy.
Examples of implementation patterns
OpenAI Agents SDK and API
OpenAI’s documentation describes approval interruptions that return details of pending items and resumable state. An application can approve or reject pending items and then resume the same run. The application still needs to provide its own review and enforcement; do not assume it automatically inherits Codex Auto-review or a complete authorization policy. The guide also recommends putting validation next to tools that create side effects. See OpenAI’s guardrails and human review guide.
Microsoft Entra Agent ID guidance
Microsoft’s least-privilege pattern is an enterprise example for agent identity, scoped roles, allowed actions, approval for certain higher-risk operations, audit logging, and revocation. It is guidance to adapt—not a claim that every architecture or deployment has identical capabilities. See Microsoft’s agent identity security best practices.
Microsoft Copilot Studio computer-use supervision
Microsoft describes review requests routed by email or an activity panel, with the workflow paused until a response or configured timeout. The feature’s requests are probabilistic rather than a guaranteed authorization gate. Model support can change: Microsoft’s page listed Azure OpenAI Computer-Using Agent (CUA) and Anthropic Claude Sonnet 4.5 as active when reviewed on 2026-10-04, and marked OpenAI Computer Using Agent V1 retired. Check the current support list before relying on it. See Microsoft’s computer-use supervision documentation.
Anthropic’s expense-agent example
Anthropic describes an expense-submission agent that may pause to ask whether it should retrieve an expense policy when a hotel charge exceeds a stated cap. This illustrates a user-facing check-in during a multistep task; it does not establish that every risky action will be gated. See Anthropic’s Claude for Financial Services overview.
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NIST evaluation probes
NIST describes probes intended to assess whether agent claims are grounded in a curated set of documents and to create trails linking decisions to evidence. The project is presented as work in development, not a completed approval standard or proven commercial control. See NIST’s Building Evaluation Probes into Agentic AI.
How to compare approval implementations
Vendor and standards documentation describes design guidance and features, not an independent apples-to-apples product benchmark. Compare implementations against the same operational questions:
Quick Recap
| Evaluation area | What to verify |
|---|---|
| Enforcement point | Is approval checked before every relevant side effect, including nested or chained tool calls? |
| Determinism | Can model output bypass the rule, or does application or orchestrator policy block execution? |
| Review context | Can the reviewer see the exact action, arguments, identity, target, and scope? |
| Decision handling | Are rejection, editing, timeout, unavailable reviewers, and escalation handled explicitly? |
| Resumption | Can a paused run continue from saved state without duplicating prior work? |
| Identity and permissions | Are agent identity and effective scope visible, narrow, and revocable? |
| Auditability | Can records connect the proposal, policy decision, human response, tool call, and actual downstream result? |
| Operational burden | What latency, reviewer load, integration work, log retention, and ongoing access review will the design require? |
A practical release checklist
- List every side-effecting tool and its downstream operations.
- Define allow, approval-required, and deny rules in deterministic policy.
- Check action, target, arguments, identity, and task scope immediately before execution.
- Make approval requests specific, reviewable, and easy to reject without disclosing secrets.
- Test rejection, reviewer unavailability, timeout, pause/resume, and duplicate-execution handling.
- Test operator stop controls and credential or permission revocation.
- Record the proposal, policy outcome, reviewer decision, execution, and downstream result under a shared correlation ID.
- Review who can access audit records and how long they are retained.
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