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To make human approval a real control, stop the agent at the tool boundary before the side effect occurs. Save the exact operation and parameters as a pending request, authenticate and authorize a reviewer, then resume the same paused workflow only after that request is approved. A prompt telling an agent to ask first is not enforcement: the component that can write data, edit files, deploy, spend money, or transmit information must refuse to execute while approval is missing.
This guide presents a practical approval architecture for developers, product teams, and system owners. It covers action classification, deterministic policy, reviewer interfaces, trusted state, revalidation, timeout behavior, audit records, and the additional limits approval cannot replace.
The approval flow you are building
A safe human-in-the-loop design is a controlled state transition, not a conversational suggestion. The agent may propose an action, but a separate enforcement point decides whether that action can execute.
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- Prepare the exact operation. Capture the tool name, target resource, arguments, identity context, and a concise explanation of expected consequences.
- Intercept before execution. The tool wrapper or execution service checks policy. If approval is required and absent, it creates a pending request instead of invoking the side-effecting API.
- Pause the workflow. Persist the pending request and run state so the process can wait for a reviewer without losing its place.
- Authenticate and authorize the reviewer. Verify who is responding and whether that person or role may approve this particular operation.
- Apply the decision once. Approve, reject, or escalate the still-pending request. Reject stale, already-consumed, or altered requests.
- Revalidate and execute. Check policy, arguments, resource state, and authorization again immediately before the side effect, then record the result.
OpenAI documentation describes this as an interruption followed by resumption of the same run. AWS Bedrock confirmation and return-of-control patterns use the same basic boundary: the application receives a proposed function call before the function runs. Other workflow systems express the pause as an approval event or checkpoint, but the security property is identical: no approval, no side effect.
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Inventory every action that can change state
Start with an exhaustive map of tools and APIs, not with the language model. Include direct tools, helper services, plugins, background jobs, and multi-agent handoffs. A seemingly harmless helper can still alter state if it writes a file, sends a message, changes a permission, or triggers a deployment.
Record the operation in application terms
For each operation, document the target, arguments, reversibility, affected data, and worst credible consequence. “Call update_record” is too vague for a policy. “Change the billing status of customer 4821 from pending to paid” gives a reviewer and a classifier something concrete to evaluate.
| Field | What to capture | Why it matters |
|---|---|---|
| Operation | Create, update, delete, publish, deploy, spend, export, or transmit | Determines the initial risk category |
| Target | Database row, file path, repository, environment, account, or external endpoint | Shows scope and ownership |
| Parameters | The exact values that will be sent to the tool | Prevents approval of a vague intention followed by a different call |
| Reversibility | Whether the change can be rolled back and how | Distinguishes recoverable edits from permanent destruction |
| Impact | Users, records, money, availability, privacy, and compliance consequences | Supports a defensible approval tier |
| Authority | Service identity, tenant, project, and resource permissions | Ensures the agent cannot reach unrelated data |
Use request-time signals as well as static labels
Give each tool a baseline classification, then evaluate facts known only when the request arrives: number of records, production versus test environment, unusual destination, financial amount, sensitivity of the data, and whether the request falls outside a normal change window. A bulk update that is usually low risk may require review when it targets a production tenant or thousands of records.
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There is no universal risk threshold. Your tiers should reflect the data, users, regulations, and recovery capabilities of your system. A common design has three levels:
| Tier | Typical treatment | Example |
|---|---|---|
| Autonomous | Runs automatically when static and request-time policy checks pass | Writing a draft to an isolated workspace or updating a disposable test record |
| Single reviewer | One authorized person must approve the exact pending operation | Editing a customer record, publishing content, or changing a production configuration |
| Multiple reviewers | Two or more independent approvals are required, often from different roles | Deleting a dataset, deploying to production, changing access controls, or authorizing a high-value transaction |
Implement the classifier as deterministic logic. Amazon Web Services states the principle directly in its Agentic AI Lens, guidance AGENTSEC04-BP02: “Define a deterministic risk classifier: Map agent operation types to approval tier requirements (autonomous, single-reviewer, multi-reviewer), combine static classification with dynamic request-time signals, and implement the classifier as deterministic logic rather than an LLM.”
An LLM can extract parameters or explain an action, but it should not decide that its own action is safe enough to bypass review. Keep any persistent trust grant narrowly scoped by tool, resource, operation, tenant, and time; make it visible, auditable, and revocable.
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Enforce the rule at the tool boundary
Place the gate in the code path that actually invokes the side-effecting operation. An agent-level instruction can be bypassed by another agent, a retry path, a newly added tool, or a direct API call. Input and output guardrails also have workflow boundaries; a check that covers one model turn may not cover every custom function in a multi-agent system.
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async function invokeTool(call, context) {
const policy = classify(call, context); // deterministic application logic
if (policy.requiresApproval) {
const pending = await createPendingRequest({
call: freeze(call),
policy,
workflowId: context.workflowId,
expiresAt: policy.expiry
});
return interruptWorkflow(pending.id);
}
await validateImmediatelyBeforeExecution(call, context);
return execute(call, context);
}The wrapper must be unavoidable. Route all production calls through it, including retries, scheduled continuations, tool calls emitted by sub-agents, and administrative replay utilities. If a service can call the database directly, approval around only the model-facing route is incomplete.
Create a reviewable pending request
When policy requires approval, store a durable request before pausing. The request should contain the operation as it will be executed, not merely the model’s natural-language intention.
Minimum request contents
- A unique request identifier and workflow or run identifier.
- Tool name, operation type, target resource, and complete argument values.
- Agent identity, tenant, project, and the credentials or delegation context that would be used.
- Policy result, required tier, expiry time, and current state.
- A short impact explanation plus links or references to controlled supporting data.
- A cryptographic digest or equivalent integrity marker for the frozen operation.
- Creation timestamp, retry count, and correlation identifiers for downstream logs.
Keep full decision context in controlled, durable storage and expose it through an authenticated reviewer interface. The interface should show the tool or operation, target, actual parameters, expected impact, and relevant before-and-after values. A model-written summary can help orientation, but approval should not depend solely on that summary; reviewers need access to the underlying records and policy context.
Pause without losing the run
Represent the workflow as an explicit state machine such as READY, PENDING_APPROVAL, APPROVED, REJECTED, EXPIRED, EXECUTING, SUCCEEDED, and FAILED. Transition to PENDING_APPROVAL transactionally with creation of the request. The worker can then release compute resources and resume later from the saved state.
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| State | Allowed transition | Required guard |
|---|---|---|
| PENDING_APPROVAL | Approved, rejected, or expired | Request is still open and has not changed |
| APPROVED | Executing | Reviewer is authorized; approval is single-use; policy is still satisfied |
| REJECTED | Workflow continuation or termination | Tool invocation is never performed |
| EXPIRED | Escalation or new request | No automatic execution from an old decision |
| EXECUTING | Succeeded or failed | Idempotency and final validation checks pass |
Authenticate the reviewer and authorize the decision
The person who clicks Approve must be authenticated independently of values supplied by the agent or the approval request. Use your organization’s identity provider, multifactor authentication where appropriate, and server-side session validation. Do not treat an email address, display name, or role string included in agent output as proof of identity.
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Authentication answers “who is this?” Authorization answers “may this identity approve this operation?” Check both at decision time. Bind the reviewer to the tenant, project, environment, and resource scope of the pending request. For sensitive actions, require separation of duties so the requester, deployer, and approver are not the same principal.
NIST’s February 2026 NCCoE concept paper on software and AI agent identity and authorization identifies binding agent and human identity, least privilege, delegation, and verifiable audit as unresolved areas for industry work. It is a concept paper seeking feedback, not a finalized implementation standard, so treat these points as design concerns rather than mandatory conformance requirements.
Bind the decision to the exact pending operation
Approval is meaningful only if it applies to the operation the reviewer saw. Store the pending call and workflow state in a trusted server-side system or equivalent application-controlled store. Do not trust a serialized state blob merely because it came back from a client or a model. Such state may contain pending calls and approval decisions, but it is not proof of identity or authorization.
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- Verify the reviewer session and authorization for the request’s tenant, resource, and approval tier.
- Check that the request is still
PENDING_APPROVAL, unexpired, and unconsumed. - Compare the stored operation digest with the operation presented to the reviewer.
- Record the decision in an atomic transaction that marks the request consumed or approved exactly once.
- Resume the original workflow through its supported state mechanism, not by asking the model to generate a replacement call.
Use optimistic concurrency or a database conditional update so two clicks cannot produce two executions. Protect state at rest and in transit, restrict administrative access, and retain an integrity-protected history of state changes. Include replay protection such as a one-time approval nonce, expiration, and a consumed timestamp.
Revalidate immediately before execution
Approval can wait minutes or hours while the world changes. The record may be edited, the user’s permissions may be revoked, the destination may become unsafe, or the model’s context may be refreshed. Run validation again after approval and immediately before the tool call.
- Recheck reviewer authorization and the agent’s delegated permissions.
- Confirm the target still exists and belongs to the expected tenant and environment.
- Re-evaluate dynamic risk signals such as amount, record count, destination, and maintenance window.
- Compare current values with the values shown during review when concurrency matters.
- Validate argument types, ranges, schemas, allowed paths, and network destinations.
- Verify that the request has not expired, been revoked, or already executed.
OpenAI’s JavaScript Agents SDK documentation describes optional validation before approval and a second guardrail run after approval, specifically to catch changes while a call waited. If any material input changes, invalidate the old decision and create a new pending request rather than silently carrying approval forward.
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Execute the frozen, approved arguments. Do not substitute a fresh model-generated call after the reviewer has approved. For operations that may be retried, use an idempotency key tied to the pending request so a timeout or worker restart cannot duplicate the side effect.
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Define rejection, timeout, and escalation behavior
Rejection
A rejection leaves the side-effecting tool unexecuted. Return a structured result to the workflow, such as approval_rejected, with a reviewer-provided reason when policy permits. The agent may explain the rejection or propose a safer alternative, but any materially different operation must pass classification again.
Timeout
Set an explicit expiry for every request. On expiry, transition it to EXPIRED, prevent later approval from reviving it, and notify the owner or queue that needs to act. For critical changes, use a short expiry and escalation; for routine edits, a longer service-level window may be appropriate. Do not default to execution because nobody responded. AWS guidance recommends a safe fallback that usually blocks the operation.
Changed context
If the target, parameters, permissions, policy tier, or relevant data changes while waiting, cancel the old request and require a new review. Show the reviewer what changed so the second decision is informed rather than a rubber stamp.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Log both the decision and the result
Audit records should allow an independent investigator to reconstruct what was requested, who decided, what actually ran, and what happened afterward. Capture at least:
| Event data | Examples |
|---|---|
| Identity | Agent identity, delegated principal, reviewer identity, tenant, and roles at decision time |
| Timing | Request creation, notification, viewing, decision, expiry, execution start, and completion timestamps |
| Operation | Tool, target, complete arguments or a protected reference to them, policy tier, and operation digest |
| Decision | Approved, rejected, escalated, expired, or revoked, including reason and required reviewer count |
| Execution | Validation result, idempotency key, downstream response, affected resource identifiers, and error details |
| Integrity | State transitions, actor for each transition, correlation ID, and tamper-evident storage metadata |
AWS recommends recording reviewer identity, notification and response timestamps, operation, decision, and escalation events. Retention, access, redaction, and deletion schedules should follow your organization’s security and privacy requirements; do not place secrets or unnecessary personal data in a reviewer screen or log.
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Layer approval with least privilege and sandboxing
Approval is one control, not a substitute for limiting what the agent can reach. Give each tool the smallest credential and resource scope needed for its task. Separate read and write credentials, restrict database schemas and filesystem roots, constrain network destinations, and isolate development, staging, and production environments.
Use resource-level policies to prevent cross-tenant access even when a call is approved. Add independent filesystem, network, identity, and project boundaries for sensitive tools. Visual Studio Code documentation illustrates this distinction: permission settings govern whether a tool requires confirmation, while sandboxing independently limits terminal filesystem and network access. A reviewer should never be able to approve an operation that the execution identity can use to escape its intended boundary.
Framework patterns and their trade-offs
The following systems demonstrate the same pause-review-resume model in different places. Exact API names and settings are version-sensitive, so verify them against the documentation for the release you deploy.
| Environment | Documented pattern | What your application still owns |
|---|---|---|
| OpenAI API and Agents SDK | A per-tool approval rule interrupts a run; the application approves or rejects the pending item and resumes from the same state. Input guardrails can run before approval and again before execution. | Reviewer identity, durable state protection, authorization, approval UI, expiry, and audit storage. |
| AWS Bedrock and workflows | User confirmation pauses before a function and returns its name and parameter values. Return of control lets the application validate, modify, present, or reject the call; Step Functions callback patterns support asynchronous review. | Decision context, deterministic risk classifier, timeout and fallback policy, and the authenticated approval experience. |
| Microsoft Agent Framework | Approval-required tools pause a workflow and emit a request/response event; pending requests can be preserved in checkpoints. | Checkpoint protection, reviewer authorization, stale-state handling, and execution-side validation. |
| Visual Studio Code agent controls | Permission levels govern approval behavior, while sandboxing separately restricts terminal filesystem and network access. Tool and URL approval are separately configurable. | Product-specific settings are not a generic API; deployment owners must still define scope, identity, and audit requirements. |
Compare implementations by enforcement location, approving role, asynchronous waiting support, state storage and resumption, identity and authorization hooks, parameter validation or modification, timeout and escalation behavior, auditability, and operational burden. The available documentation does not establish a controlled, like-for-like benchmark for reliability, latency, or cost, so those dimensions require testing in your own deployment.
An end-to-end implementation plan
The following sequence turns the design into work items that can be assigned across application, security, and operations teams.
- Build the action registry. List every state-changing tool and API, its resource scope, reversibility, baseline tier, credential, and owner.
- Write policy as code. Implement deterministic rules for operation type, environment, tenant, data sensitivity, volume, amount, destination, and time window. Version the policy and include its version in each pending request.
- Wrap every execution path. Route model calls, sub-agent calls, retries, scheduled jobs, and administrative replays through the same pre-execution gate.
- Freeze and persist calls. Store canonical arguments, a digest, run state, expiry, and correlation IDs before interrupting the workflow.
- Expose a reviewer view. Present actual parameters, target, impact, relevant context, and policy tier through an authenticated interface. Provide approve, reject, and escalate actions according to role.
- Consume decisions atomically. Bind the decision to the request ID and digest, enforce one-time use, and prevent approval after expiry or cancellation.
- Resume the same workflow. Continue from the saved interruption rather than reconstructing the call from a new model response.
- Run final checks. Re-authorize, reclassify, validate current resource state, enforce idempotency, and execute only the frozen approved arguments.
- Emit complete events. Write decision, transition, execution, and downstream result records to protected audit storage.
- Exercise failure paths. Test rejection, duplicate clicks, expired requests, changed parameters, revoked permissions, worker crashes, downstream timeouts, and partial failures.
Testing scenarios that expose broken gates
- Attempt a direct call to the underlying API while the agent-facing request is pending; it must be denied by independent authorization.
- Change one argument after approval; the digest or final validation must detect the change and require a new decision.
- Submit two approvals concurrently; exactly one should transition the request to executable state.
- Replay an old approval token after execution or expiry; it must have no effect.
- Remove the reviewer’s permission between display and approval; the decision must be rejected.
- Restart the worker after interruption; the workflow must resume from trusted state without duplicating the call.
- Make the downstream service return a timeout after accepting the request; idempotency handling must prevent an unsafe retry.
- Have a sub-agent invoke a tool that the parent agent did not call; the same tool-boundary policy must still apply.
- Fill the reviewer context with unusually large or sensitive data; redaction, size limits, and access controls must remain effective.
The result you want is observable: every state-changing call has a policy decision, every approval is bound to a specific frozen operation, no rejected or expired request executes, and every execution can be traced back to an authenticated decision or an explicitly permitted autonomous tier.
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