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Set an AI agent to ask by default for any action that can send, edit, delete, execute code, disclose sensitive data, or spend money. Allow automatic execution only for low-risk, reversible work. A safe design combines four controls: a capability inventory, input/output/tool validation, a human approval interruption before side effects, and an independent sandbox that limits what an approved action can reach.
The approval request should show the reviewer the agent, exact tool, arguments, target, and expected effect. Your server must authenticate and authorize the reviewer, consume the pending request atomically, record the decision, and resume the same paused run after approval. A rejection, timeout, policy failure, or unavailable reviewer should stop high-risk work rather than silently downgrade protection.
What does permission management for an AI agent control?
Permissions determine which capabilities an agent can invoke, with which arguments, against which resources, and under what review conditions. They are broader than a login permission: an agent may be authenticated correctly yet still be allowed to delete the wrong file, call an unsafe endpoint, or send a message without a human seeing the final payload.
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Actions that normally need review
- Sending email, messages, tickets, invitations, or public posts.
- Editing or deleting records, files, code, infrastructure, or production configuration.
- Running shell commands, installing packages, or invoking tools with network access.
- Calling sensitive MCP actions or connected-app operations that expose personal, financial, legal, or confidential data.
- Purchasing, transferring funds, accepting contracts, changing billing, or making decisions with physical, economic, legal, or safety consequences.
Actions that can often run automatically
Low-risk work is usually read-only, bounded, reversible, and easy to inspect: formatting a draft in memory, classifying a document without exporting it, calculating a value from supplied inputs, or preparing (but not sending) a proposed change. Confirm that the operation really is reversible; an apparently harmless API call may trigger notifications, billing, or downstream automation.
How do I inventory an agent before granting permissions?
Start with an explicit inventory, then remove everything the agent does not need. Do not infer permissions from a natural-language system prompt; enforce them in the tool registry, policy layer, or provider configuration.
- List every callable function. Record the name, purpose, argument schema, result type, side effects, and whether it can call another tool.
- Map connected services. Include MCP servers, SaaS apps, databases, queues, identity providers, data sources, and browser or URL destinations.
- Define resource boundaries. Specify filesystem paths, repositories, tables, tenants, accounts, network hosts, ports, and environments. Prefer an allowlist over a broad wildcard.
- Classify data. Mark personal, financial, health, legal, customer, credential, and proprietary data so policies can require stronger review or prohibit export.
- Describe side effects. State exactly what changes outside the agent: a file write, API mutation, email delivery, payment, deployment, or physical command.
- Disable unused capabilities. A tool that is not needed is an unnecessary attack and approval surface.
Store this inventory with an owner and policy version. Review it whenever a tool schema, connected account, model, or workflow changes; a newly added argument can create a new side effect without changing the tool name.
Which permission mode should an agent use?
Use an ask-by-default policy for consequential operations, and make automatic execution an explicit exception for low-risk work. A policy engine should evaluate the tool, arguments, target, session context, and current risk rather than applying one global switch.
| Mode | Behavior | Appropriate use | Main risk |
|---|---|---|---|
| Always allow | Runs every matching call without a reviewer | Read-only or demonstrably reversible operations with narrow scope | A tool may gain a side effect later, making the old exception unsafe |
| Always ask | Pauses before every matching call | Sending, deleting, editing, spending, shell commands, and sensitive data access | Reviewer fatigue can lead to rubber-stamping |
| Auto or policy-evaluate | Evaluates each call and allows, denies, or pauses | Mixed workflows where risk depends on arguments, amount, destination, or session | Policy gaps can silently allow an unsafe combination |
| Deny | Blocks the capability regardless of a user request | Unused tools, prohibited data flows, and actions outside the agent’s role | Users may try to work around the control with another tool |
Anthropic documents equivalent concepts named always_allow, always_ask, and auto. In connected-app controls, labels can include “Always ask,” “Allow read actions,” “Allow low-risk actions,” and, where offered by the app, “Allow all actions.” Availability depends on the app, account, provider, and workspace policy, so expose the effective setting to the reviewer rather than assuming a label means the same thing everywhere.
Where should validation and guardrails run?
Put checks at the boundary where the relevant mistake can be stopped. Input, output, and tool guardrails solve different problems and should be layered rather than substituted for one another.
Input guardrails
Validate the user request before planning begins. Reject disallowed objectives, attempts to bypass approval, requests for secrets, and operations outside the user’s authorization. Normalize identifiers and destinations so a policy cannot be evaded with alternate spellings or encoded values.
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Output guardrails
Inspect generated text and structured results before they reach a user or another system. Redact secrets, enforce a response schema, detect unsupported claims, and prevent an agent from presenting an unapproved draft as completed work. Output checks do not stop a side effect that already occurred, so they cannot replace tool checks.
Tool guardrails
Inspect arguments immediately before invocation and results immediately after it. Confirm the tool is permitted for this agent, the target is in scope, quantities and destinations satisfy policy, and the result does not contain a prohibited disclosure. Re-check after a tool returns because a seemingly read-only call may report a redirect, a new URL, or a follow-on action.
Use automatic guardrails for deterministic policy checks and human review for decisions that require judgment. As OpenAI’s guidance puts it, “Use guardrails for automatic checks and human review for approval decisions.”
How do I design an approval gate before a side effect?
An approval gate should pause the run before the tool executes, present a complete and understandable request, and create a resumable decision record. It should not ask for vague consent such as “continue?” when the reviewer cannot see what will happen.
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- Build the proposed call. Resolve the exact tool version, arguments, target, account, environment, and expected side effect without executing it.
- Re-evaluate policy. Check user authorization, data classification, amount limits, destination allowlists, time windows, and any separation-of-duties rule.
- Create a pending request. Assign a server-generated request ID, bind it to the run and call, store a hash or immutable representation of the arguments, and set an expiry.
- Show a review summary. Display agent identity, initiating user, tool name, arguments, target, expected changes, data that will leave the boundary, policy reason, and any warnings.
- Pause the run. Return an interruption containing only the information needed to render the review UI. Persist the run state; do not start a new conversational turn.
- Accept approve or reject. The reviewer submits the request ID and decision through an authenticated endpoint. “Reject” must be a first-class outcome with a reason field that can be optional for low-risk work and required for high-risk work.
- Resume or terminate. On approval, resume the original run with the server-owned decision. On rejection, timeout, policy change, or unavailable review, terminate or route to a safe recovery path.
What the reviewer should see
- The human-readable purpose and the exact machine-call payload.
- Which identity initiated the run and which identity will execute the tool.
- Resources affected, including account, tenant, path, record IDs, host, and environment.
- Expected side effects, reversibility, cost or quantity, and downstream notifications.
- Why approval is required, which policy version was evaluated, and when the request expires.
- Any unresolved uncertainty, missing data, or tool result that could change the decision.
What is the difference between an approval gate and a sandbox?
An approval gate answers “Should this action run?” A sandbox answers “What can it reach even if it runs?” They are independent controls and should be configured together.
| Control | Stops or limits | Example |
|---|---|---|
| Approval | Human or policy decision before execution | Require a reviewer before deleting a customer record |
| Sandbox | Filesystem, network, process, or device reach after execution starts | Permit a command only in a temporary directory with no outbound network |
| Guardrail | Invalid inputs, arguments, or results at a defined boundary | Reject a payment amount above the policy limit |
| Provider policy | Platform-level restrictions that may override an app setting | A workspace disallows a connected-app action even when a user selected a permissive mode |
Microsoft’s VS Code documentation makes the distinction explicit: approvals decide whether an action runs, while sandboxing restricts filesystem and network resources after approval. Configure terminal, URL, and tool approvals with a separate sandbox; an approved command should still be unable to access secrets or production hosts outside its declared scope.
How do I secure the reviewer endpoint?
Treat the approval endpoint as a security-sensitive operation, not as a form that trusts fields in the browser. The reviewer must be authenticated independently, authorized for the specific run and call, and prevented from replaying a decision.
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- Authenticate the reviewer. Use your application’s session, identity provider, or service-to-service credentials. Never accept a reviewer identity supplied only in the request body.
- Authorize the relationship. Check that the authenticated person may approve this run, tool, tenant, data class, and amount. Enforce separation of duties when the requester cannot approve their own action.
- Resolve server-owned state. Look up the pending interruption by its server-generated ID. Compare the stored run ID, tool, argument hash, policy version, and expiry with the submitted decision.
- Consume atomically. In one transaction, verify the request is pending, mark it approved or rejected, and prevent a second concurrent submission from changing it.
- Resume the original snapshot. Pass the stored decision to the paused run; do not reconstruct the tool call from untrusted form fields or create a fresh user turn.
- Record an audit event. Save reviewer identity, decision, reason, timestamps, policy version, request and run IDs, execution result, and any error.
The OpenAI SDK guidance requires these protections: authenticate with application middleware, authorize against the stored run and selected calls, validate decision IDs against server-owned pending requests, and atomically consume the request so replayed or concurrent submissions cannot resume one snapshot twice. Apply approval or rejection only to server-owned interruption objects.
How should an approved run resume?
Resume the exact paused run with its preserved context, tool call, and policy state. Starting a new turn can cause the model to plan again, alter arguments, lose the reviewer’s decision, or execute a different call.
- Load the pending request and verify it is still unexpired and unconsumed.
- Verify the policy version and tool schema have not changed. If they have, invalidate the old approval and request a new review.
- Attach the signed, server-owned decision to the interruption object.
- Resume the original run. Re-run non-mutating checks immediately before the side effect.
- Execute once, capture the provider response, and mark the request completed or failed.
- If execution fails, report the failure without automatically retrying a mutating call. A retry requires an idempotency key or a new approval when the side effect could have happened.
For high-risk actions, fail closed when the reviewer times out or the review service is unavailable. A safe recovery message can offer a draft, export, or manual procedure instead of silently allowing the operation.
How do common agent platforms model permissions?
Platforms differ in who owns orchestration, state, tools, and approvals. Compare the effective behavior rather than relying on product names: default mode, scope, enforcement boundary, and recovery or audit features are the important axes.
| Implementation | Default behavior | Scope | Enforcement boundary | Recovery and audit questions |
|---|---|---|---|---|
| OpenAI Agents API, Agents SDK, or Responses API | Configured by the application; can combine guardrails and human-review interruptions | Tool, run, session, or application policy depending on the layer | Input/output/tool guardrails plus an approval interruption | Which layer owns state and orchestration? Is the interruption persisted, authenticated, atomically consumed, and logged? |
| Anthropic permission modes | always_allow, always_ask, or auto evaluates a call |
Tool and session context | Provider or application permission decision | Does the application retain the evaluated arguments and reviewer decision, and can it resume the same session? |
| Connected-app controls | Labels such as “Always ask,” “Allow read actions,” “Allow low-risk actions,” and sometimes “Allow all actions” | App, account, provider, or workspace | App controls plus provider and workspace policy | Which policy has precedence, and where are reviewer identity and action history recorded? |
| VS Code agent workflows | Approval settings for terminal, URLs, and tools | Workspace, tool, terminal, or URL | Approval plus independent filesystem and network sandbox | Can an approved command still reach resources outside the sandbox, and are command results retained? |
| Google Workflow Builder | Explicit request-info or approval step pauses downstream work | Workflow checkpoint | Workflow-level pause before later steps | Is the checkpoint state durable, who may respond, and what happens when it expires? |
Provider, account, and workspace policies can override a local setting. Document the effective policy shown at runtime and test it with the identity and tenant that will operate the agent.
How should I classify risk and escalation?
Use a small, explicit risk matrix so every tool has a predictable default. Escalate based on impact, not on whether the model sounds confident.
| Risk class | Typical characteristics | Default | Extra controls |
|---|---|---|---|
| Low | Read-only, reversible, no sensitive export, narrow resource scope | Automatic after argument and result checks | Sandbox, rate limit, audit log |
| Moderate | Internal edits, notifications, sensitive reads, or actions affecting several records | Ask or policy-evaluate | Reviewer summary, scope and quantity limits, idempotency |
| High | Deletion, external communication, code execution, production changes, financial or contractual effects | Always ask; fail closed on timeout | Strong reviewer authentication, separation of duties, dual approval where required, immutable audit |
| Prohibited | Outside the user’s authority, disallowed data flow, or an unsafe capability | Deny | Alert, preserve evidence, and offer a safe alternative |
Google recommends human oversight and explicit approval before agents perform critical or consequential actions, particularly decisions with financial, legal, contractual, economic, or physical effects. Apply that principle even when a tool technically supports unattended execution.
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When should I use workflow-level checkpoints?
Use a workflow checkpoint when the approval concerns a business process rather than one isolated tool call. A request-info step can collect missing fields; an approval step can pause the workflow before downstream actions execute.
For example, an expense workflow can let the agent gather receipts and calculate totals automatically, request missing tax information, then pause before submission or payment. Keep the checkpoint tied to the exact downstream steps: changing the amount, recipient, or policy version should invalidate the prior approval.
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What should I log and test?
Logs must let an investigator reconstruct what the agent proposed, what the reviewer saw, what was decided, and what actually happened. Avoid logging secrets or full sensitive payloads when a redacted representation and hash will prove integrity.
- Agent, model, user, tenant, run, tool, and policy version.
- Argument schema version, normalized target, side-effect classification, and payload hash.
- Approval creation, display, decision, timeout, rejection reason, reviewer identity, and timestamps.
- Execution result, provider request ID, idempotency key, error, retry, and final state.
- Sandbox profile, network destinations, filesystem paths, and policy overrides.
Test the failure paths
- Submit an approval with a different tool argument or target; it must be rejected.
- Send the same approval twice concurrently; only one may resume the run.
- Attempt approval after expiry, policy change, logout, or reviewer-role removal.
- Crash after approval but before execution; recovery must not lose the pending state or duplicate a side effect.
- Return a malicious redirect, oversized result, secret, or unexpected tool output; output and tool guardrails must stop or quarantine it.
- Try to reach a forbidden path or host from an approved command; the sandbox must block it.
What are the most common permission mistakes?
Using the prompt as the permission system
Instructions such as “ask before deleting” are useful context but are not enforcement. Put the rule in code or provider policy and test the tool boundary.
Approving a category instead of a concrete call
“Approve email” is too broad. Bind approval to the exact recipient, subject, body or attachment hash, account, and send operation; require a new decision when any material field changes.
Confusing read access with safe access
A read can expose credentials, personal data, or a URL that triggers another action. Classify the data and inspect returned results, not just the HTTP verb or tool name.
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For consequential operations, an unavailable reviewer is not approval. Expire the request and provide a manual or draft-only recovery path.
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Resuming by creating a new turn
A new turn can change the plan and bypass the approved snapshot. Persist and resume the original interruption, then verify the tool arguments again.
Skipping idempotency
Network retries can duplicate a payment, message, or deletion after the provider accepted the first request. Use provider-supported idempotency keys and reconcile uncertain outcomes before retrying.
Can I express an approval policy as configuration?
A declarative policy makes review rules reviewable and testable. The exact syntax depends on your framework, but the structure should identify the tool, scope, conditions, and decision.
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{
"tool": "send_message",
"scope": {"destinations": ["approved-domain.example"]},
"conditions": {"attachments": "review_required", "external": "review_required"},
"mode": "always_ask",
"on_timeout": "deny",
"requires": ["authenticated_reviewer", "original_run_resume", "atomic_decision"],
"audit": ["reviewer", "policy_version", "argument_hash", "execution_result"]
}Treat this as an example data model, not a universal standard. Validate configuration at deployment time, reject unknown tools or fields, and include automated tests that prove each rule is enforced at the actual side-effect boundary.
How can I roll out permissions safely?
Deploy in stages so policy mistakes are visible before they affect production.
- Observe. Run the agent with tools disabled or simulated and record proposed calls, data classes, and estimated risk.
- Constrain. Enable only the smallest read-only scope and add sandbox limits.
- Review. Turn on approval for every mutation, then tune low-risk exceptions using evidence from real calls.
- Exercise recovery. Test rejection, timeout, replay, concurrent decisions, provider errors, and policy changes.
- Expand deliberately. Add one tool or resource at a time, with an owner, expiry, and rollback plan.
- Reassess continuously. Revisit permissions after model upgrades, tool-schema changes, new connected accounts, incidents, or changes in law and business policy.
A useful operating rule is: the agent may prepare broadly, but it may commit narrowly. Keep drafting, analysis, and simulation separate from the final action that changes an external system.
Frequently Asked Questions
Can two people approve the same AI-agent action?
Yes, if the policy requires dual control, create separate pending decisions tied to the same immutable call and consume each exactly once. The tool should execute only after all required approvals are present, and the audit record should identify both reviewers and the order of decisions.
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Expire it rather than silently reusing it. Changes to the user, target, amount, tool schema, policy version, or relevant data should create a new request with a fresh summary, even if the original reviewer had approved.
How can reviewers avoid approving unsafe model-generated content?
Show rendered content and the underlying structured payload side by side, including links, attachments, recipients, and hidden metadata. Provide a way to edit or return the proposal for correction without granting permission to execute the edited version automatically.
Should approval records contain the full sensitive payload?
Not necessarily. Store the minimum needed for review, redact secrets and unnecessary personal data, and retain a cryptographic hash or immutable reference so the executed payload can be proven to match what was approved.
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