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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsDo not approve an AI agent because its explanation sounds reasonable. Pause at the action boundary, inspect the exact operation and arguments, verify the target and recipients, compare the effect with the authority you granted, and approve only the smallest safe scope. For consequential actions, enforce an approval policy that stops execution until a person responds. Keep sandbox, filesystem, network, identity, and audit controls active after approval: a human review is one layer, not a substitute for containment.
The review gate in one sentence
An AI agent should be allowed to act only when the visible operation matches the task you authorized. The reviewer needs to see what will happen, where it will happen, who or what will receive it, and which permissions the call will use.
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This applies equally to a proposed code edit, a shell command, a browser action, an MCP call, or an outgoing message. Review the operation itself rather than relying on the model’s summary. A description such as “I updated the configuration safely” is not evidence that the changed lines, command arguments, destination, or data exposure are safe.
Use this sequence:
- Define the workspace, files, tools, accounts, recipients, hosts, and external systems that are in scope.
- Require an explicit pause for actions that must not run without a person.
- Inspect the tool name, complete arguments, target, identity, requested authority, and proposed diff or message.
- Compare the proposal with the authorized outcome and stop when the target, scope, or effect is ambiguous.
- Grant the narrowest approval that permits the intended action.
- Inspect the result and retain an audit record, then plan recovery around effects that cannot be undone.
Why an agent’s explanation is not enough
Agents can summarize a plan accurately while still proposing an unsafe command or sending the right words to the wrong recipient. The meaningful evidence is the operation presented to the tool boundary: the files and lines in a diff, the command and its parameters, the message body, the recipient list, and the destination account or host.
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A review request generated by the model is also not a dependable safety net. Microsoft documents that computer-use review requests can be triggered inconsistently because the behavior is probabilistic; an agent may not ask for help before a consequential action. Treat policy-enforced approval as the checkpoint, not the model’s decision to request one.
Human review does not remove the need for technical boundaries. Visual Studio Code’s approval guidance explains that sandboxing can continue to restrict filesystem and network access even after a call is approved. A reviewer should therefore ask both “Is this action intended?” and “Can the tool reach anything beyond the intended boundary?”
Set authorization before the agent starts
Write a short action brief
Before starting a run, record the intended result in concrete terms. A useful brief names the project or workspace, permitted paths, tools, accounts, recipients, data classes, and stop conditions.
- Workspace: identify the repository or directory, and whether generated files, tests, or build artifacts may be touched.
- Allowed paths: list directories or individual files. Mark secrets, production configuration, credential stores, and unrelated projects as out of bounds.
- Allowed tools: distinguish read-only tools from editors, shells, browsers, message senders, deployment tools, and MCP servers.
- Identity: specify which account or service identity may act. A developer account and a production service account are not interchangeable.
- Recipients and destinations: name permitted people, channels, domains, repositories, hosts, or queues.
- Data limits: state whether personal data, credentials, customer records, or proprietary content may leave the workspace.
- Stop conditions: require a pause for a new file, a deletion, a permission change, an external send, a production target, or an unexpected recipient.
This scope is the reference for every later decision. If the instruction says “update the local test configuration,” a proposal that edits deployment credentials is outside scope even if it would make the tests pass.
Choose an enforced approval policy
Policies should be attached to the tool or action, not left to the model’s judgment. Anthropic’s managed-agent documentation distinguishes always_allow, always_ask, and auto policies. Use always_ask for calls that must wait for a person; auto is an automated decision mode, not a human checkpoint. See Anthropic’s permission-policy documentation for the current definitions.
Apply an always-ask rule to actions such as sending messages, deleting or overwriting files, changing access controls, running commands with external side effects, contacting an unapproved host, or using a production identity. Read-only inspection can usually have a lower-friction policy when the surrounding sandbox and identity restrictions are strong.
OpenAI’s guardrails guidance likewise recommends checking the proposed target, action, arguments, calling identity, and scope at the point where side effects occur. Its human-review material is at OpenAI Developers’ guardrails and human-review guide. Do not infer that a policy in one tool automatically covers another shell, browser, MCP server, or messaging integration.
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Review file changes before accepting them
Start with repository state
Establish what was already changed before the agent began. In a Git repository, inspect the status and diff:
git status --short
git diff --stat
git diff -- path/to/affected-fileThese commands are examples of a review method, not a substitute for the agent’s interface. Check untracked files as well as modified files; a malicious or accidental addition may be invisible in a narrow diff. If the workspace contains unrelated human changes, identify them before approving anything so the agent’s work is not confused with prior edits.
Read the complete diff, not only the summary
For every changed file, verify:
- The path is inside the authorized workspace and was expected.
- The number and type of files match the task. A request to change one parser should not create a new credential file or modify deployment scripts without explanation.
- Imports, dependencies, permissions, environment variables, and network endpoints have not changed silently.
- Tests, logging, and error handling still reflect the intended behavior.
- Generated or minified output is accompanied by the source change that justifies it.
- Secrets, tokens, private keys, customer data, or copied production content are absent from the patch.
- Deletes, renames, file-mode changes, and symlink changes are intentional.
Ask the agent to explain a suspicious line if useful, but evaluate the line yourself. Explanations are context; the diff is the proposed effect.
Separate commands from edits
A file checkpoint or version-control revert can restore content, but it cannot necessarily undo a command that already ran. A script may have uploaded data, altered a database, changed permissions, or contacted an external service before the file was restored. Review shell commands independently, especially those containing redirects, recursive deletes, package installation, credential use, network requests, or deployment operations.
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- The agent cannot identify why a file changed.
- The diff includes paths, hosts, or identities outside the action brief.
- A command must run before you can understand its effect.
- The patch adds a broad permission or disables a security control to solve a local problem.
- The requested result can be achieved with a smaller, reversible change.
Inspect an outgoing message before it is sent
Verify the exact content
Read the final rendered body, not a template or summary. Check subject, formatting, links, attachments, quoted history, signatures, and any hidden or generated content. Confirm that dates, amounts, names, claims, and promised actions are correct. If the agent inserted data from a file or database, verify that the data belongs in this message and that disclosure is authorized.
Verify destination and identity
Review every recipient, including carbon-copy and blind-copy fields, channel members, mailing lists, webhook URLs, and reply-to addresses. Compare domains and account identities character by character where misdelivery would matter. A message to an internal test channel is not equivalent to one sent to a customer, regulator, public forum, or production notification list.
Check timing and irreversibility
Confirm whether the action sends immediately, schedules delivery, creates a ticket, posts publicly, or triggers another workflow. If the integration supports drafts, use a draft as the review artifact and require a separate approval for send. A sent message may be copied, forwarded, indexed, or acted upon even if you later delete it.
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Inspect every tool call at the boundary
Use a consistent inspection card for shell, browser, file, database, MCP, and messaging calls:
| Field | What to check | Typical stop condition |
|---|---|---|
| Tool | Exact tool or connector name and version, if shown | The call uses a more powerful connector than the task requires |
| Arguments | Complete parameters, flags, paths, payloads, filters, and headers | A wildcard, recursive flag, hidden default, or omitted filter broadens scope |
| Target | Workspace, file, account, host, channel, repository, or database | The target is ambiguous, production, or outside the brief |
| Identity | User, service account, token, or delegated authority | The identity has more privilege than the operation needs |
| Effect | Read, create, modify, delete, publish, send, or execute | The effect is external, destructive, or difficult to reverse |
| Scope | Paths, recipients, records, duration, and permission lifetime | A one-time task requests persistent or organization-wide access |
OpenAI’s guidance recommends this kind of boundary check and says ambiguous or high-risk actions should pause for explicit human approval. See the documented approval guidance. Do not approve a call merely because a previous call from the same run was safe; each side-effecting operation deserves its own scope check.
Match the approval burden to risk
There is no universal numeric rule that says every edit or message must receive the same review. Use the impact, authorization, and reversibility of the proposed action.
| Risk level | Examples | Suggested control |
|---|---|---|
| Low | Read-only search in an isolated workspace; formatting a disposable local file | Automatic execution may be acceptable with sandbox and path limits; retain an activity record |
| Moderate | Editing application code, adding a dependency, opening a pull request draft | Review the diff and tool arguments; require approval before merge, publish, or external access |
| High | Deleting data, changing permissions, running a migration, contacting an external host | Always-ask policy, narrowly scoped identity, explicit target and rollback plan |
| Critical | Sending confidential information, production deployment, financial or legal communication | Named human approver, exact payload and recipients, independent verification, and an auditable decision |
Risk is contextual. A small edit to an authentication policy may be more consequential than a large change to documentation. If you cannot explain the effect or the rollback, increase the review level or stop.
Approve narrowly
When the proposal is correct, grant only the permission needed for that operation. Prefer a one-time approval or a tool-, path-, recipient-, host-, and duration-limited grant over a broad session approval. A session-level permission can silently cover later calls that you have not inspected yet; read the interface wording to understand whether your choice applies once, to a tool, to a workspace, or to the remainder of the run.
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Do not bundle unrelated actions into one approval. Approving a code diff should not also authorize a package download, a production deployment, and an email send. If an agent presents several operations together, separate them or reject the bundle and request a narrower plan.
Maintain the sandbox and network restrictions after approval. Visual Studio Code documents approval and permission controls at Manage approvals and permissions; its documented model is a useful reminder that approval does not mean unrestricted access. Review the actual settings in your environment because labels and supported features can change.
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Verify the result and keep evidence
For files
After execution, inspect repository status and the resulting diff again. Run the smallest relevant tests in the approved environment, check generated artifacts, and confirm that no unexpected file or permission change appeared. If the agent claims a test passed, verify the command, working directory, and exit result in the activity record.
For messages
Confirm the final delivery event, recipient set, channel, timestamp, and account used. If the system reports a failure or retry, check whether a partial send occurred before attempting again. Save the approved body and the actual delivery record when the communication has legal, financial, security, or customer impact.
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Record who approved the action, what they saw, the policy in force, the identity used, the result, and any follow-up. OpenAI describes telemetry covering prompts, tool decisions, execution results, MCP use, and network allow/deny events in its alignment discussion at Auto-review of agent actions without synchronous human oversight. Use equivalent logs available in your platform; do not assume a chat transcript alone captures the decisive arguments or result.
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Use version control, snapshots, and checkpoints for file recovery, but understand their limits. Visual Studio Code’s guidance on reviewing and reverting changes explains that restoring a checkpoint does not reverse completed commands or external effects. Its instructions are at Review and revert agent changes.
Before approving a high-impact call, identify the actual rollback:
- For files, define the commit, patch, or snapshot that can restore the prior state.
- For databases, identify a tested transaction or migration reversal; a source-code revert is not a database rollback.
- For messages, prepare a correction process, but assume the original may already have been read or copied.
- For access changes, know which administrator can revoke the grant and how long propagation takes.
- For uploads or API calls, determine whether the remote system supports deletion, cancellation, or key rotation.
If no practical rollback exists, require a higher approval level and an independent check of the payload and target.
Use a repeatable playbook
Coding-agent playbook
- Freeze the starting state with a status check or checkpoint.
- Give the agent a path-limited task and prohibit unrelated files, secrets, and production systems.
- Require approval before shell commands with network, deletion, installation, deployment, or credential effects.
- Read the complete diff, including new and deleted files.
- Run approved tests in the sandbox and inspect their commands and results.
- Approve a commit or merge separately from approving the edit.
Messaging-agent playbook
- Provide the allowed recipients, sender identity, channel, and data classification.
- Have the agent produce a draft rather than send directly.
- Inspect body, subject, links, attachments, quoted text, recipients, and reply-to address.
- Check for accidental personal, confidential, or regulated information.
- Use an always-ask send policy and approve only the exact reviewed payload.
- Verify delivery and retain the approval and delivery records.
Mixed workflows
When an agent edits a file and then sends a message, treat them as two approvals. The message may contain a diff, test result, or generated report that changed after the first review. Reinspect the final attachment and text immediately before sending, and do not let approval of the file imply approval of disclosure.
How to compare approval systems
When evaluating an IDE, managed-agent platform, or custom orchestration layer, compare controls on these axes rather than relying on a feature name:
| Axis | Questions to ask |
|---|---|
| Enforcement | Does policy pause execution, or does the model decide whether to request review? |
| Coverage | Are shell commands, file operations, browser actions, MCP calls, app tools, and message sends all covered? |
| Scope | Can permissions be limited by tool, workspace, path, recipient, host, identity, or duration? |
| Visibility | Does the reviewer see exact parameters, target, diff, message, and enough recent context? |
| Defense in depth | Do sandbox, filesystem, network, identity, and project boundaries remain active after approval? |
| Audit and recovery | Are decisions and outcomes logged, and what can actually be reverted? |
This comparison describes control dimensions, not a cross-vendor effectiveness benchmark. The official materials do not establish a universal threshold for when every file edit or message must require approval.
What the available measurement does—and does not—show
OpenAI’s April 30, 2026 article reports roughly 200 times fewer human approval stops for its described internal use when compared with manual approval. The same article says the ratio depends on the use case, environment, and sandbox configuration. Treat that figure as a report about that deployment, not as an expected improvement or an independent safety result for every agent.
The other cited documentation primarily describes controls and limitations rather than measured outcomes. Approval quality still depends on the reviewer seeing the real operation, having enough context, and being able to enforce a narrow scope.
Common failure modes and fixes
“The agent said it was safe.”
Failure: accepting a narrative instead of inspecting the operation. Fix: open the diff, payload, arguments, target, and identity; ask for clarification only after examining them.
“It did not ask me first.”
Failure: treating a model-generated review request as the checkpoint. Fix: configure an always-ask rule at the tool boundary for the action categories that require human approval.
“I restored the checkpoint, so the incident is over.”
Failure: forgetting that commands, uploads, database writes, and sends can persist. Fix: investigate external effects separately and execute their specific rollback or containment plan.
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“I approved the session once.”
Failure: overlooking the scope of a broad permission. Fix: inspect whether approval applies to one call, a tool, a workspace, or the entire session; revoke or narrow it before later actions.
“The diff is small, so the risk is small.”
Failure: judging by line count rather than effect. Fix: treat authentication, permissions, deployment settings, data exports, and external sends as high impact even when the patch is one line.
Operational checklist
- Scope and authorization are written before execution.
- Required actions use an enforced human checkpoint.
- The reviewer can see exact tool, arguments, target, identity, and scope.
- File reviews include untracked files, deletes, renames, permissions, and secrets.
- Message reviews include final body, recipients, destination, attachments, and timing.
- Sandbox, filesystem, network, and identity limits remain active after approval.
- Approvals are narrow and not silently reused for unrelated operations.
- Post-action results and decisions are logged.
- Recovery distinguishes file restoration from commands and external effects.
The Bottom Line
Approve an AI agent only after the exact operation, target, authority, and scope are visible and consistent with your authorization. Enforce that pause in policy, keep technical boundaries active, and treat every external effect as separately reviewable and recoverable.
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