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Make human approval a hard stop immediately before any agent tool call that can create, update, delete, publish, spend, deploy, or transmit data. The reliable pattern is: classify the action, freeze the exact operation and parameters, pause the run, authenticate and authorize a reviewer, apply a one-time decision to that pending operation, revalidate it at execution, and record the complete outcome. A prompt telling an agent to “ask first” is not an enforcement mechanism; the tool or workflow boundary must refuse execution until an approved decision exists.
What an approval step must control
An approval control sits between an agent’s proposed operation and the side effect. For example, an agent may propose UPDATE customers SET credit_limit = 50000 WHERE id = 1842, deleting a file, publishing a release, or sending a payment. The system should construct that call, show it to an authorized reviewer, and prevent the underlying tool from running while the request is pending.
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Reviewing an execution result is useful for monitoring, but it is not approval. Once a database write, deployment, or transmission has happened, a reviewer can only attempt remediation. The enforcement point therefore belongs at the tool boundary or at an application callback that controls the tool invocation.
The pause-and-resume lifecycle documented in OpenAI API and Agents SDK guidance records an interruption in place of tool execution. After an application approves or rejects the interruption, it resumes the same run from its stored state. AWS Bedrock confirmation and return-of-control patterns use the same architectural idea: the application receives the proposed function name and parameter values, decides what to do, and only then allows execution. Exact API names and event schemas are version-sensitive, so treat those products as illustrations of the control flow rather than as a universal interface.
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The end-to-end approval flow
- Inventory: enumerate every tool and API that can change state or cause an external consequence.
- Classify: apply deterministic policy to the operation, target, parameters, actor, and current context.
- Prepare: create an immutable pending request containing the exact call, not a vague description.
- Pause: save workflow state and block the side-effecting tool until a decision is available.
- Review: authenticate the reviewer independently and display the operation, target, parameters, impact, and relevant context.
- Decide: authorize, reject, escalate, or let the request expire; bind the response to that specific pending request.
- Revalidate: check identity, policy, freshness, resource state, and parameters again immediately before execution.
- Execute and log: consume the decision once, perform the call, and record both the decision and the result.
If any required condition is missing, the safe outcome is no execution. The reviewer interface can be asynchronous; the tool boundary cannot be bypassed while the review is pending.
Step 1: Inventory every state-changing action
Start with an inventory rather than with prompts. List built-in and custom tools, direct API clients, background jobs, webhooks, and multi-agent handoffs. Include actions that do not look like database writes but still create a durable consequence.
- Create, update, merge, or delete records.
- Write, move, rename, or delete files and objects.
- Publish content, deploy code, change configuration, or rotate credentials.
- Send email, messages, payments, orders, or data to an external service.
- Grant permissions, change subscriptions, or consume a budget.
Describe each operation in application terms. Record its target resource, parameters, data sensitivity, reversibility, blast radius, and whether a transaction or compensating action exists. An operation that is reversible in theory may still require approval if the rollback is expensive or incomplete.
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Do not assume an agent-level check covers every path. OpenAI’s guardrail guidance notes that input and output guardrails have workflow boundaries. If every custom call must be reviewed, put the check next to the side-effecting tool so another agent, route, or callback cannot skip it.
Step 2: Define deterministic approval tiers
Use a policy engine or ordinary application logic to decide whether review is required. Do not ask the same language model that proposed an operation to decide whether its own operation is risky. AWS’s Agentic AI Lens states: 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.
A practical three-tier design is a starting point, not a universal standard. Adjust thresholds, roles, and separation-of-duties rules to your data and consequences.
| Tier | Typical treatment | Examples of policy signals |
|---|---|---|
| Autonomous | Runs without an interactive reviewer when all fixed conditions hold. | Read-only calls, test records, a narrow tenant, low-value reversible updates, or a pre-authorized batch below a stated limit. |
| Single reviewer | One authenticated person approves the exact pending call. | Production record edits, customer-visible publication, external messages, or changes above a monetary or volume threshold. |
| Multiple reviewers | Two or more authorized people, or separate roles, approve before execution. | Irreversible deletion, privilege changes, high-value transfers, production security configuration, or cross-tenant operations. |
Combine static classification (the operation type and resource) with request-time signals such as amount, affected rows, environment, data sensitivity, time of day, and the requesting identity. A persistent trust grant should be narrowly scoped, time-limited where possible, auditable, and revocable. Never let a broad “approved tool” flag silently authorize new targets or parameters.
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Step 3: Intercept the call before execution
At the execution boundary, evaluate the policy against the concrete call. If approval is required and no valid decision is attached, create a pending request and interrupt the run. The side-effecting function must not receive control while the request is pending.
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A framework-neutral sketch looks like this:
proposal = agent.propose_tool_call()
policy = classify(proposal, current_context)
if policy.requires_approval:
pending = save_pending_request(
run_id=run.id,
tool=proposal.tool,
arguments=canonicalize(proposal.arguments),
policy_version=policy.version,
expires_at=policy.expiry
)
pause(run, pending.id)
else:
execute_after_revalidation(proposal, decision=None)The pending record should contain a canonical representation of the tool name and arguments, a unique request identifier, the workflow or run identifier, the policy version, creation and expiry times, and a hash or equivalent integrity check. Store it in an application-controlled system, not in an untrusted client field.
Step 4: Build a review request a person can actually judge
Show the reviewer the operation, target, actual parameters, expected effect, and consequences. “The agent wants to update data” is not sufficient. A reviewer should be able to answer what will change, where, for whom, and how much.
Minimum visible details
- The tool or API operation and the environment, such as production or test.
- The resource identifiers, tenant or account scope, and affected record count when known.
- The exact parameter values, with secrets redacted but not replaced by misleading summaries.
- A before-and-after diff, query preview, or transaction plan when the system can produce one safely.
- The agent’s reason and relevant user request, clearly labeled as context rather than authorization.
- Warnings about irreversibility, external recipients, cost, privacy, or policy exceptions.
- Expiry time, required reviewer role, and the available actions: approve, reject, or escalate.
A model-written explanation can orient a reviewer, but approval must not depend only on that summary. Keep detailed decision context durably in controlled storage and expose it through an authenticated review interface. AWS recommends this arrangement, and Visual Studio Code guidance likewise tells reviewers to inspect the tool name and input parameters, particularly for file changes and external services.
Authenticate and authorize the reviewer
Authenticate the human separately from values supplied by the agent or by the approval request. Then authorize that identity for the specific run, operation, resource, and tier. A person who may edit a staging record may not be allowed to approve a production deletion. Record the reviewer’s identity and role at decision time.
NIST’s February 2026 NCCoE concept paper on software and AI agent identity and authorization frames binding agent and human identity, least privilege, delegation, and verifiable audit as unresolved implementation issues. It solicits feedback and is not a finalized standard or mandatory rule; use it to identify design questions rather than to claim compliance.
Step 5: Bind the decision to trusted workflow state
When the reviewer responds, verify all of the following before changing the pending request:
- The reviewer’s authenticated identity and role are still valid.
- The reviewer is authorized for this run, operation, resource, and approval tier.
- The request identifier refers to a still-pending, unexpired record.
- The operation and parameters have not changed since presentation.
- The decision has not already been consumed or superseded.
Consume an approval once. Use a transaction, compare-and-set update, or equivalent mechanism so two workers cannot execute the same approved call. Protect serialized run state with ownership and integrity checks. OpenAI’s guidance warns that serialized state containing approval decisions and pending calls is not proof of identity or authorization and recommends trusted storage and replay protection.
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Step 6: Revalidate immediately before execution
Approval can become stale while a request waits. Re-run input validation immediately before the tool executes. The OpenAI JavaScript SDK documentation describes optional pre-approval validation and a second guardrail pass after approval so changes during the wait are detected.
Check that the target still exists, the caller still has permission, the environment is unchanged, limits have not been exceeded, and optimistic-lock or version conditions still match. If the affected rows, amount, recipient, or query plan differs from what the reviewer saw, invalidate the decision and create a new pending request.
Use database transactions, idempotency keys, and conditional writes where appropriate. These mechanisms do not replace human review; they prevent retries, races, and duplicate side effects after a valid decision.
Step 7: Handle rejection, timeout, escalation, and changed context
Rejection
A rejection leaves the tool unexecuted. Return a structured result to the agent or user that states the operation was not performed and, where policy permits, includes the reviewer’s reason. Do not let the agent automatically alter the request and execute the variant without a fresh policy decision.
Timeout
Set an explicit expiry on every pending request. If no response arrives, block the operation by default, notify the escalation channel, and preserve the expired record for audit. AWS recommends timeout policies and safe fallbacks, typically blocking the operation rather than assuming consent.
Escalation and multi-review
For a multi-review tier, track each required approval separately and require all of them before execution. An escalation should change the authorized reviewer or tier in trusted state, not merely add a note to the user interface.
Changed context
If policy inputs, resource state, parameters, affected records, or identity change while waiting, mark the old decision stale. Present the new exact operation for review. Carrying approval forward across a material change defeats the control point.
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Record enough immutable or tamper-evident information to reconstruct the lifecycle:
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- Run, request, and workflow identifiers.
- Tool name, canonical parameters, target resource, policy version, and risk tier.
- When the request was created and presented.
- Reviewer identity, role, authentication event, decision time, and decision.
- Escalation, timeout, rejection reason, or stale-context event.
- Execution start and end times, result, affected-resource identifiers, and error details.
- Integrity metadata linking the decision to the exact pending operation.
AWS specifically recommends reviewer identity, notification and response timestamps, the operation, decision, and escalation events. Make logs access-controlled and retain them according to your legal, security, and operational requirements. A log that says “approved” without the operation and parameters cannot prove what was authorized.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Layer approval with least privilege and sandboxing
Approval is one barrier, not the whole security model. Give the agent only the credentials, tables, filesystem paths, network destinations, and actions it needs. Use resource-level policies, separate identities for environments, read-only database roles where possible, and transaction limits.
Keep independent filesystem and network restrictions. Visual Studio Code documentation treats permission approvals and sandboxing as distinct layers; OpenAI API guidance likewise recommends independent filesystem, network, identity, and project boundaries for sensitive tools. If an agent is tricked or a reviewer makes a mistake, those boundaries reduce the reachable blast radius.
Framework patterns and their trade-offs
The following documented patterns show different places to implement the same pause-review-resume control. They are not a controlled comparison, and the source material provides no like-for-like reliability, latency, or cost benchmark.
| 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 the same state. Input guardrails can run before approval and again before execution. | Reviewer authentication, trusted state, authorization, replay protection, UI, expiry policy, 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, reviewer roles, deterministic risk classifier, timeout and fallback behavior, and logs. |
| Microsoft Agent Framework | Approval-required tools pause a workflow and emit a request/response event; pending requests can be preserved in checkpoints. | Identity, policy, reviewer experience, stale-state checks, and execution safeguards. Distinguish tool approval from an interactive user handoff. |
| Visual Studio Code agent controls | Permission levels govern approval behavior, while sandboxing independently restricts terminal filesystem and network access. Tool and URL approval are separate settings. | Deployment-specific configuration and policy. These are product controls, not a generic agent API for every application. |
When choosing an implementation, compare where enforcement occurs (tool boundary, orchestrator, or application callback), who approves, whether work can wait asynchronously, how state is resumed, what identity checks exist, whether parameters can be modified, how timeouts escalate, and how much operational burden the deployment adds.
A concrete implementation checklist
- Inventory every side-effecting tool and indirect path to it.
- Assign deterministic approval tiers and document thresholds.
- Place the gate beside each side-effecting tool, including custom and multi-agent calls.
- Canonicalize and persist the exact pending operation before pausing.
- Authenticate reviewers independently and authorize them for the specific action.
- Display target, parameters, impact, context, warnings, expiry, and required roles.
- Use one-time decisions, integrity checks, ownership checks, and replay protection.
- Revalidate policy, identity, resource version, and parameters immediately before execution.
- Block on rejection, timeout, missing approval, stale context, or failed validation.
- Log proposal, presentation, reviewer, decision, escalation, execution, and result.
- Constrain credentials, filesystem, network, and resource scope independently.
Failure modes to test before production
The agent executes while the screen says “pending”
Test the tool endpoint directly, bypassing the normal chat UI. If it accepts a call without a valid decision, the enforcement is too high in the stack. Move the check into the tool adapter or application callback.
A reviewer approves one operation but another runs
Compare a cryptographic hash or canonical serialization of the presented and executing arguments. Reject any mismatch and require a new review.
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Race two workers against the same request identifier. A transactional consume step should allow one execution and make the second attempt fail as already consumed.
A forged approval reaches the workflow
Attempt to submit a decision with a different user, run identifier, or request identifier. The server should authenticate the reviewer, check authorization, and verify ownership and integrity of the pending state.
Approval survives a dangerous change
Change the resource version, affected-row count, amount, recipient, policy tier, or environment while the request is waiting. The pre-execution validation must mark the decision stale and stop the call.
No one responds
Use a short test expiry and verify that the operation is blocked, escalation is emitted, and the timeout appears in the audit record. Never treat a missing response as approval unless a separately justified policy explicitly says so.
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Inspect requests with large payloads, nested arguments, redactions, and external URLs. Provide a safe diff or preview, preserve necessary context in controlled storage, and make the exact values available without exposing secrets.
Operational rollout
Begin in a non-production environment with representative tools and failure injection. Measure queue age, approval latency, rejection and timeout rates, stale-context frequency, duplicate-execution attempts, and policy overrides. These are operational signals for tuning tiers and reviewer staffing, not universal benchmarks.
Roll out by operation class or environment, keeping a kill switch that disables autonomous execution and forces review. Version the policy and record that version with each request so later investigations can explain why a call was or was not gated. Review granted trust exceptions regularly and revoke those no longer needed.
Train reviewers to inspect parameters and consequences rather than simply clicking the first available button. Keep the approval interface separate from agent-generated text, protect it with normal account security controls, and make the decision auditable from presentation through execution.
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