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AI agents should receive only the tools, data and authority required for their assigned task, with deterministic checks at the tool-execution boundary and human approval before sensitive, externally visible, destructive, irreversible or ambiguous actions. An agent’s plan or request is not consent. Your application must decide whether a proposed call may run, must pause for a person, or must be denied. Good approval prompts state exactly what will happen, which account and target are affected, what has already run, and what will happen if the user declines.
How should I set permissions for an AI agent?
Start with least privilege, then enforce every proposed action in the application and execution environment. Model instructions can describe boundaries, but they cannot be the authorization mechanism: a prompt-injected webpage, file, email or tool response may tell the agent to do something that the user never approved.
Put authorization at the side-effect boundary
Check a call immediately before the helper that changes the outside world. For example, a send_email helper should verify the initiating user, allowed recipients, message classification and approval state before connecting to the mail service. A text-only model output check is too early and too easy to bypass if generated code or a custom tool can make the call directly.
Bind the decision to four things: the identity that started the run, the exact action, the target resource and the time or validity window. A permission to update one ticket should not silently become permission to delete a project, and approval for one invoice should not authorize a batch of invoices discovered later.
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Use narrow scopes and separate credentials
- Expose only the tools needed for the task; do not give a support agent unrestricted shell, database and billing access.
- Use read-only credentials for investigation and separate write credentials for approved changes.
- Constrain targets, domains, file paths, records and network destinations with allowlists where practical.
- Set short-lived tokens and an expiry for approvals so a stale confirmation cannot be replayed.
- Record the human, service account or workflow identity that initiated and approved each call.
For generated code, enforce these restrictions inside the runtime and exposed helpers. A policy applied only to the conversational agent does not govern code that runs outside that agent.
When should an AI agent ask for approval?
Require a fresh human decision when an action can cause material harm, disclose information, create an external commitment or become difficult to undo. Low-impact, reversible work can often run automatically when it is inside a documented scope.
| Action class | Typical treatment | Examples |
|---|---|---|
| Low impact and reversible | Allow automatically within a narrow scope; log the call | Search an internal knowledge base, draft a response, calculate a report |
| Externally visible | Pause for confirmation unless an explicit standing rule covers it | Send an email, publish a post, open a customer-facing ticket |
| Financial or contractual | Require fresh approval with amount, payee and terms shown | Purchase, refund, transfer funds, accept a contract |
| Destructive or hard to reverse | Require confirmation, safeguards and often a human takeover | Delete records, revoke access, deploy to production |
| Ambiguous or outside policy | Deny or pause; ask for clarification rather than guessing | Unclear recipient, conflicting instructions, unexpected domain |
Impact and reversibility matter more than the label “AI.” A human-authored automation that deletes data needs the same control as an agent. Conversely, a read-only agent may not need an interruption for every search if its data scope and logging are sound.
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An effective prompt is a transaction preview, not a vague “Allow?” dialog. It should let the reviewer understand the action without opening another system, and it should stop before the first operation that requires consent.
Include the decision-critical fields
- Action: the verb and operation, such as “send,” “delete,” “publish” or “change.”
- Target: account, resource, recipients, destination domain or record identifiers.
- Payload: amount, file names, message text, permissions or other consequential values.
- Reason and origin: what task triggered the proposal and which untrusted content, if any, supplied instructions.
- Scope and duration: whether approval covers one call, a named set of calls or a time-limited session.
- Consequences: what becomes visible, charged, deleted or difficult to reverse.
- Prior activity: what has already run and what has not.
- Choices: approve once, deny, edit, request a safer alternative or take over manually.
Example of a specific prompt
Proposed action: Send the attached incident report to security@example.org from the company security mailbox. The report contains three customer email addresses and is 2.4 MB. No message has been sent. Approve this single send, edit the recipients, or deny?
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This wording identifies the side effect, recipient, sensitive content, size and one-time scope. It does not imply that a webpage instruction or the model’s own explanation is permission.
How do I enforce permissions at the tool boundary?
Use a deterministic authorization path that runs for every side-effecting call, including calls made by custom tools or generated code. A practical sequence is:
- Classify the tool. Mark whether it reads, writes, publishes, spends money, changes identity or destroys data. Keep the classification with the implementation, not only in a prompt.
- Resolve identity. Attach the authenticated user, tenant, service account and run identifier to the request. Reject calls with missing or mismatched identity.
- Normalize the target and arguments. Canonicalize URLs, file paths, record IDs, recipients and amounts before policy evaluation so alternate spellings cannot evade restrictions.
- Evaluate scope. Check tool, action, target, data sensitivity, user role, token expiry and any tenant or domain allowlist.
- Calculate impact. Decide whether the call is allowed, requires approval, must be denied or requires human takeover. Do not let the model choose the category.
- Pause before execution. For approval-required calls, display the complete preview and hold the operation. Do not perform a partial send, upload or deletion while waiting.
- Bind the approval. Store a hash or equivalent binding of the approved action, target and payload. If any material field changes, request approval again.
- Execute with constrained credentials. Use the narrow token and environment selected for that call, with network and filesystem isolation where possible.
- Log the result. Record the policy decision, reviewer, timestamp, arguments after redaction, outcome and error. Make logs tamper-evident and searchable.
Input and output guardrails can catch unsafe requests or results, but they do not replace a check next to the tool that creates the side effect. This is especially important in manager-style workflows where one agent delegates work to custom application tools.
How do major agent platforms handle approval policies?
Platforms differ in who makes the decision, when execution pauses and whether application-owned tools are covered. Treat these as implementation patterns, not proof that one vendor’s defaults are universally safest.
| Platform example | Documented behavior | Important scope limit |
|---|---|---|
| OpenAI computer-use integration | The application and isolated execution environment implement consent rules. Guidance calls for limited domains and actions, with human control for purchases, authenticated flows, destructive operations and hard-to-reverse activity. | On-screen instructions and other third-party content are not authorization; the application must decide. |
| OpenAI Agents SDK | Input and output guardrails validate requests or results, while human review pauses a run for approval. Checks should sit beside the side-effecting tool. | Custom application tools still need their own authorization logic. |
| Anthropic managed agents | Permission modes include always_allow, always_ask and auto. Under auto, the server may run, deny or pause a call; policy can be set at toolset or individual-tool level. |
Custom tools run by your application are not governed by these server permission policies. |
| Microsoft enterprise guidance | Recommendations include minimum necessary tools, data and operations; approval for high-risk or irreversible actions; meaningful oversight; and reliable pause or stop controls. | These are governance recommendations, so your implementation must define exact enforcement and failure behavior. |
| ChatGPT agent | Product help describes confirmations for high-impact actions and advises enabling only needed apps, avoiding vague instructions and reviewing app permissions. | Product behavior should not be generalized to a different agent runtime or an application-owned integration. |
Before deployment, verify the current defaults for the exact edition and integration you use. Confirm whether custom tools are included, which identity is evaluated, how policy updates propagate and what happens if the approval service cannot be reached.
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How do I give users control to pause or stop an agent?
Provide both planned checkpoints and an emergency stop that does not depend on the model cooperating. A reviewer should be able to see the pending action, cancel the run and revoke credentials without waiting for another model response.
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- Expose a visible pause and cancel control for every long-running run.
- Revoke or disable the run’s short-lived tokens when cancellation is requested.
- Make queued side effects re-check cancellation immediately before execution.
- Separate cancellation from rollback; stopping future calls cannot undo an email, payment or deletion that already succeeded.
- Show a clear status: completed, partially completed, awaiting approval, denied, cancelled or failed.
- Provide an operator takeover path for authenticated flows, captchas, ambiguous targets and other cases the agent cannot safely resolve.
For browser-based agents, isolate the session, restrict domains and keep sensitive authentication steps under human control. Never accept a “confirm” instruction displayed by a page as proof that the user consented.
What happens if an approval reviewer or policy service is unavailable?
There is no universal fail-open or fail-closed rule established across platforms, so define the behavior for each action class. High-impact, destructive and externally visible operations should normally stop when the required decision cannot be obtained. A low-risk read may be allowed to continue if its policy explicitly permits that fallback.
Document the failure contract
- Which tools pause, deny or continue when the policy service times out?
- How long does a pending request remain valid?
- Can a second reviewer take over, and how is that handoff recorded?
- Are retries idempotent, or could a timeout cause a duplicate payment or message?
- What notification reaches the user when a run stops unexpectedly?
Test outages deliberately. A system that appears safe while the reviewer is online may silently execute with broader defaults during a dependency failure.
How should I test and monitor agent permissions?
Test authorization as a security boundary, not only as a user-interface feature. Include normal, adversarial and recovery cases before granting production access.
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Minimum test set
- Attempt a tool outside the user’s role, tenant or target allowlist.
- Change one approved argument, recipient or amount after the confirmation is issued.
- Inject instructions through a webpage, document, email or tool result that conflict with the user’s task.
- Expire the approval token and replay the same request.
- Disconnect the policy or reviewer service during a destructive call.
- Cancel while a call is queued, during authentication and immediately before execution.
- Run generated code that tries to call a helper not exposed in the agent’s tool set.
- Check that logs redact secrets while retaining enough context for investigation.
Monitor approval rate, denial rate, policy overrides, repeated retries, unusual targets, token expiry failures and actions taken after a user cancelled. Alert on privilege escalation, sudden increases in externally visible actions and any call that bypasses the normal decision path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do the published Auto-review figures actually show?
OpenAI Alignment reported in an April 30, 2026 article that Codex sessions in its internal deployment stopped for human approval roughly 200 times less often than in manual approval mode. The same report said Auto-review approved around 99% of the small fraction of actions that reached review.
Those are vendor-reported results from one internal deployment, not an independent cross-platform benchmark, a safety guarantee or a general accuracy rate. Workload, policy configuration, reviewer population and action mix can all change the outcome. Use such figures as context for evaluating a specific deployment, not as a reason to remove approval from high-impact operations.
How can I create a permission policy that remains understandable?
Write policies in terms of actions and consequences that both engineers and reviewers can audit. A concise policy should identify the permitted tools, data classes, targets, maximum impact and required approval freshness.
Policy template
- Purpose: the business task and responsible owner.
- Allowed reads: systems, fields, tenants and retention limits.
- Allowed writes: exact operations, destinations and rate limits.
- Approval triggers: spending, disclosure, publication, deletion, access changes and ambiguity.
- Reviewer: eligible identity, separation-of-duties rule and escalation path.
- Validity: one call, one batch or a stated time window.
- Revocation: how operators disable tools, tokens and queued work.
- Evidence: logs, approval binding, policy version and resulting system state.
Review the policy whenever a tool, data source, model workflow or external integration changes. A new custom tool can create a new side effect even if the conversational instructions have not changed.
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How do I stop an AI agent from taking actions without permission?
Remove ungoverned capabilities, enforce authorization immediately before execution, require explicit approval for high-impact actions and make cancellation independent of the model. Then test prompt injection, argument tampering, service outages and replay so that an agent cannot turn untrusted instructions or stale consent into authority.
Frequently Asked Questions
Can I let an agent remember my approval for future runs?
Only for a narrowly defined, low-impact scope with an expiry and an easy revocation path. Bind the standing approval to specific tools, targets and limits; require a new decision whenever the payload, recipient, amount or risk classification changes.
Should read-only agent actions ever require approval?
Yes, when the data is highly sensitive, access is unusual, or disclosure itself creates risk. A read-only call can expose personal, financial or security information, so classify data sensitivity separately from whether the tool writes.
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What should I do when an agent has already made an unapproved change?
Stop queued work, revoke the run’s credentials and preserve the audit record before attempting remediation. Notify the affected owner, determine exactly what completed, and use a separately authorized human process for rollback or disclosure.
Who should be allowed to approve an agent action?
Use an authenticated reviewer whose role covers the target and impact, with separation of duties for payments, access changes and other high-risk operations. Record the reviewer identity and policy version so an approval can be investigated later.
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