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Put approval checks at the point where an AI agent is about to change something—not in a chat prompt or an after-the-fact audit. A reliable system defines which actions are allowed, which need a human decision, and which are blocked; shows reviewers the exact proposed action; and records whether the action actually ran.
Why approval must happen before an agent acts
An agent can call tools that send messages, spend money, change access, delete or export data, or update many records at once. If the action has already reached the downstream system, a log can help explain what happened, but it cannot prevent the side effect.
Enforce authorization in the application or orchestrator at the tool boundary, immediately before execution. OpenAI’s Guardrails and human review guidance puts it plainly: “Put validation next to the tool that creates the side effect.” This matters in chained workflows: a general input or output check may not inspect every custom tool call. Validate the target, operation, arguments, acting identity, and task scope where the consequential tool runs.
A model asking “Should I proceed?” can be a useful interaction feature, but it is not the same as an authorization control. The application must be able to stop execution even if the model fails to ask, asks at the wrong time, or produces a misleading request.
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Classify actions into allow, approve, and deny
Inventory each tool and the downstream operations it can trigger. Assess consequence, reversibility, scope, data sensitivity, and privilege. A read-only lookup or draft may be safe to automate within a narrow scope; sending that draft, changing permissions, deleting records, exporting sensitive data, or applying bulk updates may warrant approval or a hard block. These are starting categories, not universal risk ratings: map them to your systems and obligations.
Write the rules as deterministic policy rather than leaving the model to decide whether its own proposed action is safe. Microsoft recommends meaningful oversight and system-level controls that apply regardless of model output. Its guidance says to require approval for high-risk or irreversible actions.
- Allow: narrowly defined, routine operations that can run without interruption within an approved scope.
- Approve: actions that are ambiguous, high-impact, privileged, bulk, destructive, or difficult to reverse.
- Deny: operations that are never permitted for this agent, task, identity, or environment.
Start with no permissions and grant only what the task needs. Microsoft’s least-privilege guidance for agents describes scoped roles, allowlisted actions, approval for bulk changes, and stronger controls for high-impact steps. Treat these as adaptable design guidance, not a universal configuration that fits every architecture.
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Build an approval workflow that can stop execution
1. Check the proposed action at runtime
Before a tool call can cause a side effect, evaluate the operation, exact arguments, target resource, calling identity, and task or engagement scope against policy. If the action requires review, suspend the run before execution and route the proposal to an authorized reviewer. A reviewer’s approval should authorize only the proposal shown, not an open-ended class of future actions.
2. Show a decision-ready approval request
Make clear what will happen, where, under which identity, and which records or data are affected. Include the arguments and enough context to judge whether the request fits the approved task and scope. Offer explicit approve and reject choices, plus a way to correct or amend a proposal when the workflow supports it. Do not ask reviewers to supply passwords, PINs, payment-card details, or other secrets in the review response; Microsoft’s computer-use supervision guidance specifically warns against entering sensitive information in a review request.
3. Make rejection, timeout, and interruption safe
A rejection must leave the action unexecuted. If the reviewer is unavailable or the decision times out, keep a high-risk action paused or fail closed; do not silently continue. Preserve the pending run so an authorized decision can resume that same work without replaying earlier steps or duplicating side effects. OpenAI documents an approval-interruption pattern in which an application handles pending items and resumes saved state. The application still has to implement its own policy and enforcement.
Give operators a separate way to pause or stop autonomous behavior. Test that they can disable the agent and revoke its authority by invalidating tokens, rotating credentials, or removing stale permissions. Microsoft’s agent risk guidance calls for reliable system-level pause or stop mechanisms, while its least-privilege guidance describes revocation and permission review.
Keep authority narrow and revocable
Give agents distinct identities instead of allowing them to inherit broad human credentials. Grant task-scoped access, allowlist the tools and operations needed for the job, and review those permissions periodically. Make the effective identity and scope visible to the policy check and reviewer; otherwise, an approval can look reasonable while the agent has broader authority than the person expects.
Revocation should be operationally tested, not assumed. Confirm that disabling an agent, invalidating its tokens, rotating credentials, and removing permissions actually prevent subsequent calls to the downstream systems. Microsoft’s least-privilege guidance discusses these controls as part of managing agent identity and access.
Record the decision and the outcome
Use a stable correlation identifier to connect the original task, proposal, policy evaluation, human decision, tool call, and downstream result. Capture enough to reconstruct the event without relying on the agent’s own summary:
- Agent identity, role, effective scope, and represented user when applicable.
- Target resource, proposed action, and arguments.
- Policy or rule that applied and its result.
- Whether approval was requested, granted, rejected, or timed out, and who decided.
- Tool execution status and what the downstream system actually changed.
Microsoft’s least-privilege guidance identifies agent identity, role, scope, action, resource, and correlation ID as useful audit details. Its agent risk guidance also recommends logging plans, tool calls, decisions, and outcomes. Protect the logs with appropriate access controls and retention rules: auditability should not create a new route to sensitive information.
NIST’s Building Evaluation Probes into Agentic AI describes an emerging approach that checks agent claims against curated documents and links decisions to evidence in machine-readable trails. The page presents development work, not a finalized standard or a measured guarantee for approval systems.
Do not mistake a prompt or button for effective oversight
A visible approval button proves little by itself. The reviewer needs relevant context and real authority to reject, and the action that executes must match the approved proposal. Verify that every path to the consequential operation passes through the same enforceable control, including nested and chained tool calls.
Microsoft warns that 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. Its documentation says not to treat these requests as a fail-safe or guarantee. Use model-generated check-ins to help users interact with a workflow, not as the only gate for high-consequence actions.
Content an agent reads from webpages, files, or screenshots may be adversarial. Microsoft’s agent risk guidance discusses indirect prompt injection, while its computer-use supervision guidance advises trusted, isolated environments and validation. Do not let instructions found in untrusted content override system policy or the approval boundary.
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Compare approval implementations by their controls
When assessing an SDK, orchestration framework, or enterprise agent-control product, compare the enforcement design rather than relying on a feature label:
- Enforcement point: Does the check run before each relevant side effect, including nested tool calls?
- Determinism: Can model output bypass the rule, or does application or orchestrator policy block execution?
- Review context: Can a reviewer see the exact action, arguments, target, identity, and scope?
- Decision handling: Are rejection, edits, timeout, reviewer unavailability, and escalation defined?
- Resumption: Can a paused run continue from saved state without repeating completed work?
- Identity and permissions: Are effective scope and revocation visible and manageable?
- Auditability: Can records connect the proposal, policy result, reviewer response, tool call, and actual downstream change?
- Operational burden: What reviewer load, latency, integration effort, log retention, and ongoing access reviews will the design require?
Official product documentation can explain available patterns and features, but it does not establish independent, apples-to-apples performance across products.
Examples of documented implementation patterns
OpenAI Agents SDK and API
OpenAI’s Guardrails and human review guide describes approval interruptions that return details and resumable state; an application can approve or reject pending items and resume the run. The application must add its own review and enforcement—this is not automatic inheritance of Codex Auto-review.
Microsoft Entra Agent ID guidance
Microsoft’s least-privilege pattern covers agent identities, scoped roles, allowlisted actions, approval for bulk updates, stronger checks for high-impact steps, audit logging, and revocation. Teams should adapt it to their architecture and requirements.
Microsoft Copilot Studio computer-use supervision
The computer-use supervision documentation describes review requests delivered through email or an activity panel, with the workflow paused until a response or timeout. Because requests are probabilistic rather than a guaranteed authorization gate, this feature should not replace deterministic checks. Model support changes over time; the page listed Azure OpenAI Computer-Using Agent (CUA) and Anthropic Claude Sonnet 4.5 as active when reviewed on October 4, 2026, and marked OpenAI Computer Using Agent V1 retired. Check the live documentation before depending on a specific model.
NIST evaluation probes
NIST’s evaluation-probes project page describes work on testing factual grounding against curated documents and creating evidence-linked audit trails. It is development work, not a completed standard or proof that a particular approval control is effective.
Anthropic’s user-facing check-in example
Anthropic’s trustworthy-agents article describes an expense-submission agent that may ask whether it should retrieve an expense policy when a hotel charge exceeds a stated cap. That illustrates a helpful user check-in, not a guarantee that every risky action will be stopped for approval.
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