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Keep an AI agent from acting on its own by separating read-only tools from write tools, granting the minimum permissions, validating every argument, and forcing a human approval interruption immediately before any side effect. Treat messages, purchases, form submissions, record changes, shell commands, and production edits as approval-required operations. A risk score or automatic policy decision is not the same as a person reviewing the action.
This design still works when an agent reads email, browses websites, operates a browser, or delegates to another agent. The enforcement point is the tool boundary: the action must pause after the agent has prepared exact arguments but before the tool can execute them.
What counts as an action that should require approval?
Require approval whenever an operation communicates externally, changes state, spends money, exposes sensitive information, or is difficult to reverse. Classify tools by consequence rather than by whether they are labeled “automation” or “AI.”
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| Tool or operation | Typical access | Recommended policy | Why |
|---|---|---|---|
| Search, retrieval, calendar lookup, reporting | Read-only | Allow automatically within scope | No external state changes when results are only returned to the agent or user. |
| Draft an email, message, ticket, or calendar event without sending it | Write to a private draft area | Usually allow, then require approval to send or publish | Drafting is reversible; delivery creates an external commitment. |
| Send email, chat, SMS, notification, or social post | External write | Always ask | The recipient, wording, timing, and disclosure cannot be reliably undone. |
| Submit a form, place an order, or make a purchase | External write or financial | Always ask | Submission can create legal, financial, or operational obligations. |
| Create, edit, or delete records | System mutation | Needs approval; stricter for bulk or destructive changes | Data loss and downstream automations may be difficult to reverse. |
| Run shell commands, scripts, or infrastructure changes | Privileged execution | Always ask, with a narrow allowlist | A command can affect secrets, hosts, data, or production availability. |
| Transfer money, change credentials, or alter production systems | High-impact privileged write | Always ask, often with two-person approval | These operations have high blast radius and may be regulated or irreversible. |
Microsoft’s Agent Framework documentation warns that, by default, tools supplied to an agent are invoked without user approval. Its guidance says side effects, sensitive data, irreversibility, and scope should determine which tools receive an approval gate. Google Cloud similarly recommends explicit human oversight for financial, legal, regulated, high-impact, or difficult-to-reverse operations, including production-data changes.
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How do I let an agent read but not change anything?
Expose separate read and mutation tools, then give the agent credentials that cannot call the mutation tools at all. A single “manage everything” tool makes it impossible to enforce a reliable read-only mode.
Use separate interfaces
- Create a calendar-read function that returns events but cannot create, update, or invite.
- Create a message-draft function that writes only to an isolated draft store.
- Expose a distinct message-send function whose policy is always ask.
- Use separate API keys, OAuth scopes, service accounts, or roles for retrieval and mutation.
- Keep deletion, bulk updates, credential changes, and production access in separate tools rather than optional flags on a general update function.
Enforce read-only access outside the model
Prompt instructions such as “do not make changes” are useful context but are not a security boundary. Enforce the restriction in the API gateway, identity provider, database role, or operating-system sandbox. If a read-only token is stolen or misused, it should still be unable to send, edit, delete, or execute.
Where should the approval checkpoint go?
Place the checkpoint after the agent has produced the final tool name and arguments, but immediately before the side-effecting tool executes. Approval earlier in a multi-step plan can become stale if the recipient, amount, text, or target changes.
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- A policy engine checks the tool, identity, scope, argument types, and risk rules.
- The system pauses the run and creates an interruption containing the exact proposed action.
- A person approves, rejects, or edits the request according to the product’s supported workflow.
- Only an approval tied to that exact request permits execution.
- The system records the decision and resumes or terminates the run.
OpenAI’s guardrails guidance describes human review that pauses a run before cancellations, edits, shell commands, or sensitive tool actions. Its Agents SDK represents pending approvals as interruptions that must be approved or rejected before the tool runs. Do not place a single approval at the start of a long workflow and assume it covers later changes.
What should an approval prompt show?
A useful approval request is a compact transaction record, not a vague “Continue?” dialog. The reviewer must be able to understand the consequence without opening hidden logs or reconstructing the agent’s reasoning.
- Tool and operation: for example,
send_message,delete_record, orrun_shell_command. - Identity and destination: the account acting, recipient addresses, workspace, database, host, or production environment.
- Complete arguments: message body, attachments, query filters, record IDs, command, amount, currency, and timing.
- Change summary: what will be created, modified, deleted, published, or disclosed.
- Triggering context: which user request and which prior step produced the proposal.
- Policy result: why approval is required and which rule matched.
- Expiration: how long the approval remains valid and what happens if the underlying arguments change.
When content is long, show a deterministic hash or version identifier alongside a readable preview. Bind the approval to the exact argument payload; changing even one recipient, amount, path, or message should invalidate it and create a new request.
How do I distinguish automatic risk checks from human approval?
An automatic policy can classify or allow a call without a person seeing it. If every call must be reviewed, configure an always-ask or needs-approval rule instead of an automatic risk mode.
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Anthropic’s permission documentation explicitly states that its “auto” policy is not a human checkpoint and recommends always_ask when a person must review every call. Automatic evaluation remains useful for low-risk retrieval, rate limits, and denying obviously disallowed operations, but it cannot substitute for a human decision on a consequential action.
A practical policy ladder
| Policy | Behavior | Appropriate use |
|---|---|---|
| Allow | Execute when authentication, scope, and validation pass | Read-only retrieval and narrowly bounded calculations. |
| Needs approval | Pause and request a human decision | Messages, purchases, edits, submissions, and privileged commands. |
| Block | Reject without offering execution | Disallowed destinations, destructive patterns, unsupported jurisdictions, or prohibited data access. |
| Auto-risk evaluation | Policy engine decides without necessarily showing a person | Low-risk routing, rate controls, or supplemental screening; not mandatory human review. |
How can I prevent prompt injection from making an agent click or send?
Assume that text from web pages, emails, screenshots, documents, and tool results is untrusted data. It may contain instructions aimed at the model, but it must not be able to grant permission, alter policy, or directly select a side-effecting action.
Layer the defenses
- Separate data from instructions: pass retrieved content in a clearly marked data field and tell the agent that embedded instructions have no authority.
- Use structured outputs: require a typed action object with enumerated operation names, bounded strings, numeric ranges, and explicit target fields.
- Validate at the tool boundary: reject unknown fields, malformed addresses, unapproved domains, unsafe paths, excessive amounts, and identifiers outside the caller’s scope.
- Apply allowlists: restrict recipients, domains, file paths, commands, database tables, environments, and spending limits.
- Parameterize queries: never concatenate model-produced text into SQL, shell commands, URLs, or template expressions.
- Isolate execution: use a sandbox, separate browser profile, scoped token, network egress policy, and disposable workspace for computer-use tasks.
- Keep approval independent: the reviewer, not the page or email, decides whether the exact action is acceptable.
Microsoft recommends treating model-supplied function arguments as untrusted and enforcing allowlists, type and range limits, path checks, and parameterized queries. OpenAI recommends structured outputs and isolating untrusted data so it cannot directly drive behavior. Anthropic’s computer-use guidance identifies human review for messages, purchases, form submissions, and data modification as the most effective mitigation for high-stakes computer-use actions, alongside scoped permissions and logging.
How specific should the agent’s instructions be?
Write an operating boundary that names the people, systems, data, and conditions in scope. Broad instructions such as “review my emails and take whatever action is needed” leave the model to invent recipients, urgency, and acceptable side effects.
Define an action contract
- Allowed communication channels and approved recipient groups.
- Permitted systems, projects, folders, environments, and geographic regions.
- Maximum amount, record count, file size, or command duration.
- Data classes the agent may read, quote, attach, or disclose.
- Conditions that require escalation, such as legal language, unusual payment details, or production access.
- Actions that are never permitted, even after a normal approval request.
OpenAI’s guidance recommends specifying who may be contacted, what may be sent, which systems are in scope, and the conditions for acting. Confirmation requests for purchases or email should be reviewed carefully rather than accepted as a routine continuation.
When is a reviewed plan better than approving every step?
Use a reviewed plan when many individually small actions combine into a meaningful outcome. The agent can outline intended steps, affected systems, and expected changes; a person can correct the plan before execution and still receive interruptions for high-risk steps.
Anthropic describes Plan Mode as a workflow in which the agent presents its intended plan for review before execution and permits intervention during the run. A plan is not blanket authorization: if the recipient, scope, amount, or operation changes, the system should pause again with the new exact arguments.
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What must an audit log contain?
Log enough information to reconstruct what the agent attempted, what policy decided, who approved it, and what actually happened. Store denied and failed calls as well as successful ones.
| Event field | Example content |
|---|---|
| Run and parent identifiers | Conversation, workflow, session, and nested-agent IDs. |
| Actor identity | User, service account, delegated identity, and tenant or workspace. |
| Tool and version | Exact tool name, policy version, connector version, and model run. |
| Arguments | Validated payload, with secrets redacted but destinations and change details retained. |
| Policy decision | Allow, needs approval, or block; matched rule and reason. |
| Approval record | Approver identity, timestamp, decision, expiration, and payload hash. |
| Execution result | Success, rejection, timeout, external identifier, error, and rollback status. |
| Browser or computer-use evidence | Relevant screenshots, URLs, and step sequence when needed to reconstruct the action. |
Anthropic recommends logging the full sequence of actions, and Microsoft documents reviewing computer-use activity. Protect logs because they may contain message bodies, personal data, credentials metadata, or sensitive destinations; apply retention and access rules appropriate to that information.
How should I test an approval system before trusting it?
Test the enforcement path with adversarial inputs and operational failures, not only with a successful “send message” demonstration.
- Bypass attempts: place instructions in an email, webpage, image, attachment, or tool result that ask the agent to ignore policy or send secrets.
- Argument mutation: approve a request, then change the recipient, amount, path, or message and verify that the approval is rejected.
- Scope violations: request an unlisted domain, table, environment, account, or file path.
- Nested execution: have one agent hand off to another and verify that the child cannot inherit unapproved write authority.
- Replay: reuse an old approval token and confirm that it is single-use or expires.
- Failure handling: disconnect the approver, time out the tool, or return a partial result; verify that the system fails closed and reports the state.
- Bulk operations: test large record sets and destructive filters with separate thresholds and previews.
- Audit completeness: compare the external system’s activity with your internal log to find missing or duplicated events.
Microsoft cautions that computer-use review is probabilistic: a model may fail to request review when a person would want it, or ask when it is unnecessary. Approval prompts therefore complement validation, least privilege, isolation, and monitoring; they do not guarantee perfect prevention.
Which actions need stricter controls?
Increase controls as impact, irreversibility, sensitivity, or blast radius increases. A read-only lookup may need authentication and rate limits; a production deletion may need a named approver, a second approver, a maintenance window, a backup, and a tested rollback.
- Financial: require amount and currency limits, beneficiary allowlists, and independent confirmation.
- Legal or regulated: require an authorized role and preserve the exact submitted content.
- Production systems: separate deploy and rollback permissions, restrict time windows, and require change records.
- Personal or confidential data: minimize fields, mask previews, and verify the recipient before disclosure.
- Irreversible deletion: prefer soft delete, preview affected records, and require a second confirmation for bulk scope.
Google Cloud’s guidance says agents should not be used to bypass human oversight or authorization. For these domains, an agent can prepare analysis or a change set, but the authorized human remains responsible for the final decision.
How do I compare agent platforms or governance products?
Evaluate enforcement capabilities rather than marketing labels such as “human in the loop.” Ask whether the product can prevent execution, not merely display a warning after the fact.
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| Evaluation question | What a strong implementation provides |
|---|---|
| Is approval mandatory or risk-scored? | An always-ask or needs-approval mode that cannot be silently downgraded. |
| Are policies per tool and account? | Different rules for each operation, identity, workspace, environment, and connector. |
| Can read and write scopes be separated? | Distinct tools and credentials, with mutation authority absent from read-only sessions. |
| Are arguments validated before execution? | Typed schemas, allowlists, range checks, path checks, and rejection of unknown fields. |
| Do approvals cover handoffs? | Child agents and delegated workflows cannot bypass the parent’s approval requirement. |
| Is the audit log complete? | Arguments, policy decisions, approver, timestamps, results, failures, and browser evidence. |
| How is untrusted content handled? | Isolation, structured data channels, prompt-injection defenses, and scoped credentials. |
| What rollback exists? | Previews, idempotency, cancellation, soft delete, backups, and documented recovery. |
What does a minimal implementation look like?
The following pattern keeps policy enforcement outside the model and binds approval to the validated payload. Names are illustrative; adapt them to your framework.
proposal = agent.plan_tool_call(user_request, context)validated = schema.validate(proposal.arguments)
policy = policy_engine.evaluate(
tool=proposal.tool,
identity=session.identity,
arguments=validated
)
if policy == "block":
log("blocked", proposal, policy)
stop_run()
if policy == "needs_approval":
request = approval_queue.create(
tool=proposal.tool,
arguments=validated,
payload_hash=hash(validated),
expires_at=short_deadline
)
decision = wait_for_human(request)
if decision != "approved" or hash(validated) != request.payload_hash:
log("rejected_or_changed", proposal, decision)
stop_run()
result = execute_tool(proposal.tool, validated)
log("executed", proposal, result)The execution service must re-check authorization and validation after the pause. Never let the model call the underlying connector directly, and never treat a natural-language “approved” message from untrusted content as an approval event.
What are the most common failure modes?
The agent sends before the dialog appears
The approval hook is probably attached to planning or display rather than execution. Move it into the connector or gateway that performs the side effect, and deny direct access to the underlying API.
The user approves one action and another executes
Bind approval to a canonical, hashed argument payload and invalidate it whenever any field changes. Include the tool version and target identity in the binding.
Read-only mode still changes data
Remove mutation scopes from the credential and separate the tools. Do not rely on a system prompt or a UI toggle that leaves write authority available to the same session.
Prompt injection changes the destination
Treat retrieved text as data, validate destinations against an allowlist, and display the final recipient and content in the approval request. A human must approve the resulting action, not the page’s instructions.
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Approvers click through everything
Reduce low-value prompts with precise allowlists and risk tiers, while making high-risk requests show complete arguments, consequences, and affected systems. Review approval and rejection patterns to find rules that are too broad or too noisy.
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How should I roll this out?
Start with observation, then enforce the highest-risk boundaries first.
- Inventory every tool and label its side effects, data sensitivity, reversibility, and blast radius.
- Split retrieval, drafting, mutation, deletion, privileged execution, and production operations into separate interfaces.
- Issue least-privilege identities and deny mutation authority to read-only workflows.
- Add schemas, allowlists, range limits, path checks, and parameterized queries.
- Configure always-ask approval immediately before consequential execution.
- Design a confirmation view that exposes exact arguments, target, identity, and timing.
- Instrument complete logs, including blocked, denied, failed, and approved calls.
- Run injection, replay, mutation, delegation, timeout, and rollback tests.
- Review logs and user feedback, then tune low-risk automatic rules without weakening high-risk gates.
Frequently Asked Questions
Can an approval request expire if nobody responds?
Yes. Use a short expiration and fail closed when the deadline passes. The agent should stop or return a draft, not retry the side effect with a new implicit approval.
What happens when the approver is unavailable?
Define an explicit fallback such as queueing the request, routing it to an authorized backup, or cancelling it. Never substitute a broader service account or automatic approval merely to keep the workflow moving.
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Avoid self-approval for high-impact operations because it removes independent oversight. Use a separately authenticated human or an independent control plane, and record the approver’s identity.
How do I handle an agent that needs to send many similar messages?
Approve a bounded batch only when every recipient, template version, data field, and rate limit is visible and fixed. If any item differs materially, split the batch or request a new approval.
Can I undo an action after approval?
Design reversibility before enabling the tool: drafts, delayed delivery, soft deletes, idempotency keys, backups, and provider cancellation APIs can reduce harm. An undo path does not remove the need for approval before the original side effect.
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