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AI agents can act without asking you each time when you have granted them the necessary tools, data access, identity and permissions. They can search files, summarize documents, classify requests, read calendars, draft messages, write and test code, and coordinate multi-step work. They should pause for approval before actions that send, spend, commit, publish, delete, transfer sensitive data, change production systems or create legal, physical, regulatory or security consequences. Anything outside the approved scope—or any request to steal credentials, bypass controls or exfiltrate data—should be blocked outright.
The practical boundary is not whether a model is “smart enough.” It is the authority your design gives it and the controls around each tool call. This guide shows how to set that boundary, when to require a human decision, and how to make approvals auditable and safe.
What “acting without approval” actually means
An AI agent is a model that directs its own process and tool use while pursuing a goal. Anthropic describes the pattern as planning, acting, observing results, adjusting, and repeating until the task is complete or human input is needed. That differs from a fixed script: the agent chooses which available tool to call, in what order, and with which arguments.
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The four controls that set the boundary
- Tools: Search, database, email, payment, deployment, browser, file and other tools define what the agent can attempt.
- Data: The files, records, messages and web content in its context determine what it can see and infer.
- Identity: The account, service principal or delegated user identity determines whose authority is being exercised.
- Permissions: Roles, scopes, policy checks and approval gates determine which calls are allowed, paused or denied.
Remove a tool and the corresponding action is impossible. Limit its role to read-only and it can inspect but not alter the system. Require an approval interruption before execution and the agent can prepare an action but cannot complete the side effect on its own.
A useful autonomy ladder
| Level | What the agent may do | Typical examples | Human involvement |
|---|---|---|---|
| Observe | Read approved sources and return information | Search a knowledge base, inspect a calendar, summarize a document | No approval for each read; access is pre-authorized |
| Prepare | Create a proposed result without applying it | Draft an email, build a pull request, prepare a purchase cart | Review is required before the result is sent or applied |
| Reversible write | Make a low-impact change that can be undone | Add a private task, label a message, update a test record | May be pre-authorized within narrow limits |
| Consequential action | Trigger an external, financial, legal, production or security effect | Send a message, buy an item, deploy code, accept terms | Explicit approval immediately before execution |
| Prohibited | Attempt an unauthorized, unsafe or out-of-scope action | Steal credentials, bypass policy, exfiltrate data, establish persistence | Block and log; do not offer a weaker workaround |
The same operation can move between levels depending on scope. Reading a public calendar is low risk; editing a shared executive calendar is not. Writing to a development database may be reversible; writing to a production database may require a change window, two-person review and a rollback plan.
Tasks that are usually safe to pre-authorize
Pre-authorization means the agent can complete the action without interrupting you, provided every policy condition is met. Keep the scope specific: named systems, data classes, time windows, rate limits and maximum object counts are safer than a blanket “allow all tools” setting.
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Read-only search and retrieval
Agents can search approved drives, ticket systems, wikis, code repositories and public sources, then quote or summarize what they found. A read-only role prevents accidental edits while allowing useful investigation. Restrict indexing to the repositories and folders the agent actually needs, and mark confidential, regulated or personal-data locations separately.
Summarization and transformation
Summarizing a meeting transcript, extracting fields from invoices, converting notes into a checklist, translating a document or classifying support tickets normally has no external side effect. The main controls are data minimization, retention limits and an instruction that the output is a draft when the source is ambiguous.
Calendar and task inspection
Reading availability, finding scheduling conflicts and proposing time slots are generally safe. Keep event descriptions and attendee lists protected if they contain sensitive information. Let the agent create a private draft event only if it cannot notify attendees or reserve a room until approval.
Drafting communications
An agent may prepare an email, chat reply, support response or invitation for review. Drafting should not silently send, add recipients, upload attachments or create a public link. Show the final recipient list, attachments, quoted content and any generated claims in the approval view.
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Code analysis and test work
An agent can inspect a repository, explain a bug, write a patch on a branch, run unit tests in a sandbox and report failures. Use an isolated workspace, synthetic credentials and a network policy that prevents unintended production access. Opening a pull request can be treated as a preparation step; merging, releasing or changing infrastructure requires a separate gate.
Classification and routing
Routing a ticket to a queue, tagging a document or prioritizing a work item is often reversible. Define confidence thresholds and an escalation path. If a classification could affect benefits, employment, credit, healthcare or another regulated outcome, treat it as a consequential decision and require qualified human review.
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Actions that should require explicit approval
Approval is appropriate when an action sends information, commits resources, changes an authoritative record, affects another person, or is difficult to undo. Google Cloud recommends human oversight and explicit approval before critical or consequential decisions or actions, particularly where legal, economic or physical impact is possible.
Sending messages, invitations and publications
Require approval before sending email, direct messages, calendar invitations, customer notices, public posts or press material. The approval record should show the exact content, recipients, sender identity, attachments, links and scheduled time. Re-check recipients at execution time so a changed contact or poisoned document cannot redirect the message.
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An agent may research options or prepare a cart, but a person should approve the merchant, item, quantity, total amount, currency, shipping address and payment instrument immediately before purchase. Set a hard spending ceiling and block split transactions that could evade it. Refunds, transfers, trading, payroll and changes to billing details deserve the same or stronger controls.
Legal and contractual commitments
Do not let an agent accept terms, sign an agreement, renew a contract, make a warranty representation or submit a binding filing on its own. It can compare clauses and flag changes, but an authorized person must confirm the exact document, parties, obligations, dates and jurisdiction.
Regulated or high-impact decisions
Decisions affecting employment, lending, insurance, healthcare, education, housing, public benefits or safety need domain-appropriate human review. The reviewer must be able to inspect the evidence, challenge the recommendation and record the reason for the final decision. An approval button is not meaningful if the person cannot see what the agent used.
Production and infrastructure changes
Require approval before merging to a protected branch, deploying, changing access-control rules, rotating a live secret, modifying schemas, deleting data, changing DNS or scaling a production service. Tie approval to a named change ticket, maintenance window, test results, expected blast radius and rollback procedure. A deployment agent should fail closed if the ticket is missing, expired or outside scope.
Data transfers and sharing
Moving data to another system, customer, geographic region or external service can be irreversible even when the source remains intact. Show the fields, destination, retention period, encryption status and legal basis. Require approval for uploads, exports, public links and invitations to external collaborators. Redact unnecessary personal or confidential fields before the approval request.
Credential use and privilege elevation
Never let an agent discover, copy or reveal passwords, API keys, private tokens or recovery codes. If a task needs privileged access, use a short-lived, purpose-bound credential issued after approval. The policy should name the target, allowed operation, duration and maximum scope; revoke it automatically when the task ends.
High-risk cybersecurity actions
Scanning, exploitation testing, firewall changes, account recovery, malware analysis and incident response can affect systems beyond the intended target. Require an authorized engagement, a defined target list, allowed techniques, time window, rate limits and an emergency stop. The agent must not expand scope because a discovered host or prompt suggests it.
Actions that should be blocked by policy
Blocking is stronger than asking for approval. Use it when the action is unauthorized, inherently unsafe, or impossible to evaluate reliably.
- Out-of-scope hosts or accounts: Deny targets not listed in the task’s approved scope, even if credentials technically work.
- Credential theft or secret exposure: Block requests to extract tokens, scrape passwords, defeat multifactor authentication or reveal private keys.
- Persistence and stealth: Deny backdoors, hidden scheduled tasks, undeclared browser extensions, startup changes and mechanisms intended to survive review.
- Data exfiltration: Block attempts to package or transmit information to an unapproved destination, including via encoded text, public paste services or hidden channels.
- Policy bypass: Reject instructions to disable logging, ignore an approval gate, alter safety rules, impersonate a user or conceal an action from the owner.
- Unauthorized access: Do not use guessed, reused or intercepted credentials, or exploit a system without documented authorization.
- Unsafe ambiguity: Stop when the target, identity, requested outcome or consequences cannot be established. Escalate for clarification rather than choosing the broadest interpretation.
A block should return a clear reason to the calling application, record the event and avoid exposing sensitive policy details that would help an attacker probe for a bypass.
Why human approval is necessary
Intent can be misunderstood
Natural-language goals are incomplete. “Clean up the old customer files” could mean archive records, remove duplicates or delete data subject to retention rules. An agent can execute a coherent plan that is still contrary to the user’s intent. A review screen gives the user a chance to correct assumptions before the side effect.
Untrusted content can change the plan
Prompt injection occurs when instructions hidden in a webpage, document, email or repository attempt to redirect the agent. Treat retrieved content as data, not authority. Keep system policy and user intent in a higher-trust channel, label external text, restrict tool arguments and require approval for any action influenced by untrusted material. Anthropic identifies prompt injection and misread intent as major practical risks.
Consequences compound across a long run
An agent that can work for hours may perform dozens of individually plausible steps. A small error in the first step can multiply through later searches, edits and messages. Set timeouts, step budgets, spending limits and maximum affected objects. Re-evaluate authorization at each consequential boundary rather than granting a blanket session-wide permission.
Safeguards reduce risk but do not guarantee behavior
Anthropic’s guidance emphasizes careful choices about tools, data, permissions and operating environments, while warning that safeguards are not a guarantee. Approval is one layer alongside isolation, least privilege, validation, monitoring and recovery.
How to design a permission model
1. Inventory every tool and side effect
List each tool the agent can call and describe its maximum effect: read, create, modify, delete, send, spend, execute or publish. Include indirect effects such as triggering a workflow, notifying a person or changing a cache that another system treats as authoritative.
2. Separate read, prepare and execute operations
Do not expose one broad “project” tool when three narrow tools will do. Create distinct read-only, draft and commit operations. A draft tool should return a proposed payload; an execute tool should accept only a validated payload and an approval token issued for that exact action.
3. Give the agent its own identity
Use a distinct service identity or delegated role instead of a person’s unrestricted account. Apply least privilege to repositories, folders, APIs, databases and networks. Record the human who initiated a run separately from the agent identity that made each call, so responsibility and investigation remain clear.
4. Validate at the tool boundary
Validation belongs where the side effect is created, not only in the model prompt. Check the target, operation, arguments, identity, scope, time window, rate limit and data classification in the tool gateway. Reject malformed or extra arguments, expired approvals and mismatched identities. Never rely on the model to enforce its own permission rules.
5. Define approval triggers precisely
Write policies in terms a gateway can evaluate: “send only to internal recipients,” “spend no more than a stated amount per transaction,” “deploy only the approved commit during the named window,” or “export only these fields to this destination.” Include thresholds for object count, data volume, risk score and geographic location.
6. Make the approval interruption resumable
OpenAI’s approval lifecycle records an interruption before tool execution, lets the application approve or reject the pending item, and resumes the same run from saved state. Preserve the exact tool name, arguments, policy decision, identity, expiration and reviewer. If any of those change, create a new approval rather than silently reusing the old one.
7. Fail closed when review is unavailable
If the reviewer times out, the approval service is down, the identity cannot be verified or the policy engine returns an error, do not execute the action. Save the proposed operation for later review and tell the agent it is waiting. A graceful failure is safer than an automatic fallback to unrestricted execution.
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Capture the user goal, model and policy version, tools considered, exact arguments, source identity, approval request, reviewer, decision time, execution result and rollback reference. Protect logs from alteration and limit their contents so secrets and unnecessary personal data are not copied into the audit trail.
9. Provide isolation and recovery
Run untrusted browsing and code in separate filesystem and network boundaries. Use disposable workspaces, resource quotas and egress allowlists. For every write operation, define an undo path, backup or compensating transaction before enabling autonomous execution. Test the stop button and credential revocation, not just the success path.
Technical checks for sensitive workflows
For a high-risk cyber or infrastructure task, evaluate all of the following before execution:
- Target: Is the hostname, account, repository, environment or record explicitly in scope?
- Action: Is the operation listed as allowed, and is its risk level compatible with the current approval?
- Arguments: Are paths, selectors, quantities, commands and destinations validated and bounded?
- Identity: Is the agent using the intended service identity, with no unexpected privilege elevation?
- Engagement window: Is the approval still valid for this time, region and maintenance window?
- Environment: Are filesystem, network, browser and secret stores isolated as designed?
- Audit: Will the action and result be recorded independently of the agent?
- Failure behavior: Does any unavailable check stop execution rather than permit it?
Google’s guidance for Model Context Protocol deployments calls per-action human approval “Human-in-the-Middle” mode and also recommends a distinct agent identity and least-privilege access.
What this means for common requests
| Request | Allow without a new approval? | Safer implementation |
|---|---|---|
| “Find my next free afternoon.” | Usually yes, with calendar read access | Return availability without exposing unrelated event details |
| “Book a meeting with the client.” | No | Draft time, attendees, agenda and location; approve before sending invitations |
| “Reply to these support tickets.” | Drafts may be allowed; sending requires approval unless a tightly bounded auto-reply policy exists | Show recipients, attachments and claims; enforce templates and escalation rules |
| “Buy the cheapest compatible part.” | No | Prepare a cart and require approval of seller, total, delivery address and payment |
| “Fix the failing tests.” | Often yes in an isolated branch and sandbox | Run tests with no production credentials; require approval to merge or deploy |
| “Deploy the approved release.” | No for production | Verify commit, ticket, tests, window, approver and rollback before execution |
| “Export all customer records for analysis.” | No | Minimize fields, verify destination and retention, then obtain explicit data-transfer approval |
| “Scan this company’s network.” | Only under documented authorization | Check target list, techniques, rate limits, window and emergency contact before any probe |
How to compare agent products or deployment designs
Feature lists rarely reveal whether an agent is safe to operate. Evaluate the control plane as well as the model:
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- Read versus write capability: Can each connector be limited to observation, or does a “read” permission hide mutation?
- Reversibility: Is there an undo, backup or compensating transaction, and how quickly can it be used?
- Permission granularity: Can policy distinguish individual tools, actions, arguments, objects, destinations and thresholds?
- Identity and least privilege: Does every run use a distinct, attributable identity with short-lived credentials?
- Approval interruption and resumability: Can a reviewer inspect the exact pending call and resume the same run without re-planning?
- Auditability: Are prompts, tool calls, approvals, policy versions and outcomes recorded in tamper-resistant logs?
- Untrusted-content handling: Are external documents, websites and messages isolated from policy instructions and tool authority?
- Behavior when approval is unavailable: Does the system pause and fail closed, or silently continue with a broader permission?
Test these claims with a harmless staging workflow. Ask the agent to perform an allowed read, prepare a draft, request a blocked action, and continue after an approval timeout. Inspect the logs and verify that revoking the identity stops an in-progress run.
Operational checklist before enabling autonomy
- Define the business goal and the systems genuinely required to achieve it.
- Create a separate agent identity and least-privilege roles for each environment.
- Expose narrow tools with typed arguments instead of unrestricted shell or browser access.
- Classify every operation as observe, prepare, reversible write, consequential or prohibited.
- Set approval rules for sending, spending, publishing, deleting, transferring, elevating privilege and changing production.
- Require target, scope, time-window and identity checks at the tool boundary.
- Isolate browsing, code execution, filesystems, networks and secrets.
- Set step, time, rate, object-count and financial limits.
- Show reviewers the exact payload, recipients, destination, evidence and predicted impact.
- Make approval records single-use, expiring and bound to the exact action.
- Fail closed on policy, identity, logging or approval-service errors.
- Log decisions and provide a tested stop, revoke and rollback procedure.
- Review policies after incidents, near misses, connector changes and model updates.
FAQ
Can an AI agent send an email without asking me?
Technically yes if the email tool and sender identity allow it, but sending should normally be an approval-gated action. A narrowly scoped exception can be safe for low-risk, templated notifications when recipients, content, rate and data types are fixed and the policy is documented.
Can an agent buy something on my behalf?
It can research products and prepare a cart without approval. Require a human decision immediately before payment, showing the seller, items, total, currency, shipping address and payment method.
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Can an agent change production code?
It can analyze code, create a branch and run tests in an isolated environment. Production merges, deployments, schema changes and access-control edits should require a validated approval tied to a change record and rollback plan.
What if the agent says approval is unnecessary?
The agent is not the authority that decides its own permissions. The external policy engine and tool gateway must enforce the rule. Treat any attempt to bypass or disable that control as a blocked action and log it.
Is a human approval button enough?
No. The reviewer needs the exact action, arguments, identity, target, evidence, impact and expiration, and must be able to reject or modify the proposal. Approval should be enforced at execution time, logged independently and fail closed when the review service is unavailable.
Frequently Asked Questions
Can an AI agent send an email without asking me?
Technically yes if the email tool and sender identity allow it, but sending should normally be approval-gated. Narrow, templated notifications can be pre-authorized only with fixed recipients, content, rate and data limits.
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It can research products and prepare a cart without approval. Require human confirmation immediately before payment, showing seller, items, total, currency, shipping address and payment method.
Can an agent change production code?
It can analyze code, create a branch and run tests in isolation. Production merges, deployments and infrastructure changes should require approval tied to a change record and rollback plan.
What if the agent says approval is unnecessary?
The agent cannot decide its own permissions. An external policy engine and tool gateway must enforce the rule; attempts to bypass them should be blocked and logged.
Is a human approval button enough?
No. The reviewer needs the exact action, arguments, identity, target, evidence, impact and expiration, with independent logging and fail-closed behavior if review is unavailable.
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