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Do not approve an AI agent because its explanation sounds reasonable. Approve only the specific operation you can see: the tool, arguments, target, proposed diff or message, and the authority being requested. Set an approval policy that pauses at the action boundary, keep filesystem and network limits active after approval, and preserve an audit trail. A checkpoint can restore files, but it cannot unsend a message or undo a command that already ran.
The review gate in one sentence
Before an agent edits a file or sends anything externally, inspect the exact operation, verify that its destination and scope match your authorization, and grant the narrowest approval that lets the intended work proceed. If an action is consequential, policy must force a human pause; do not depend on the model to decide when to ask.
This distinction matters because an agent’s summary is not the operation itself. A harmless-sounding explanation can conceal a broad path, an unexpected recipient, a shell command with side effects, or an authority that lasts for later actions. Review the call that will create the side effect, not merely the prose that describes it.
Why a human checkpoint is necessary
Agent behavior is probabilistic. Microsoft’s documentation warns that a computer-use model may request review inconsistently, so a missing review request is not evidence that an action is safe: Microsoft’s human-supervision guidance. A reliable checkpoint is therefore an enforcement setting, not a conversational convention.
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Anthropic’s managed-agent documentation names three permission modes: always_allow, always_ask, and auto. The auto mode lets the system decide and is not a human checkpoint. Use always_ask for calls that must wait for a person: Anthropic’s permission-policy documentation.
Human approval is only one layer. VS Code explains that sandboxing continues to restrict filesystem and network access even after an operation is approved: Manage approvals and permissions. Keep those boundaries in place; approval should not silently turn an agent into an unrestricted process.
Set the scope before the agent starts
Write down what the agent may touch and what it may not. The scope should be specific enough that a reviewer can compare a proposed action with it in seconds.
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- Files and paths: list editable areas and exclude secrets, production configuration, and unrelated projects.
- Tools: distinguish file editing, shell commands, browser actions, MCP calls, and application APIs.
- Accounts and identity: state which account may act and whether a personal or production identity is prohibited.
- Recipients and destinations: name permitted people, channels, domains, repositories, or services.
- External systems: define whether the agent may create tickets, publish changes, alter cloud resources, or call an API.
- Duration: decide whether permission applies once, for a task, or for a session.
Separate “can inspect” from “can change” and “can send.” Read access may be useful during planning, while write, execute, and send permissions deserve independent review. If the instruction leaves the target, recipient, or effect ambiguous, stop and ask for clarification instead of treating the broadest interpretation as authorization.
Choose an approval policy that actually pauses
Use an explicit tool-level policy for operations that require a person. The policy should be attached to the tool or action category, not left to a prompt asking the model to be careful.
| Policy or control | What it establishes | How to use it |
|---|---|---|
always_ask |
A human decision is required before the call proceeds. | Apply to file writes, shell commands with side effects, external sends, account changes, and other consequential tools. |
always_allow |
The configured call can proceed without a new approval. | Reserve for narrowly scoped, low-impact operations whose targets and effects are already bounded. |
auto |
The system or model decides whether to request review. | Do not treat it as a guaranteed human checkpoint; use only where that trade-off is acceptable. |
| Sandbox and network boundaries | Approved calls remain constrained by filesystem and network rules. | Keep them enabled after approval and limit paths, hosts, and capabilities to the task. |
The labels above come from Anthropic’s managed-agent policy model; other products may use different names. Compare the behavior, not the label: does the policy stop execution before the side effect, and can its scope be limited?
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Inspect the operation, not just the explanation
At the approval prompt or review screen, examine every field needed to predict the effect.
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- Tool: Is this a file-write tool, shell, browser, message, MCP, or application action?
- Arguments: Are paths, flags, URLs, query parameters, and payload fields exactly what you expect?
- Target: Which workspace, file, branch, host, account, channel, or service receives the action?
- Identity: Under whose credentials will it run?
- Scope: Is the operation limited to the files, recipients, and systems you authorized?
- Effect: Does it read, overwrite, delete, execute, publish, notify, or grant access?
- Context: Is the preceding instruction or data trustworthy, or could untrusted content be steering the call?
OpenAI’s guardrails guidance describes checking the proposed target, action, arguments, calling identity, and scope at the point where side effects occur: Guardrails and human review. That is the right mental model even when your agent platform uses different terminology.
Reviewing file changes
1. Freeze the intended outcome
State the files, behavior, and boundaries before approving an edit. “Update the parser in src/importer” is reviewable; “clean up the project” is not. Require the agent to identify files it plans to touch and the reason for each.
2. Read the proposed diff
Inspect additions, deletions, renames, generated files, permission changes, and modifications outside the requested area. Look for secrets copied into logs, dependency changes, disabled validation, altered deployment settings, and unrelated formatting churn. A small-looking change can have a large effect when it changes configuration or a build script.
3. Compare repository state
Check whether uncommitted work existed before the agent began. Distinguish the agent’s edits from your own changes and from changes made by another process. If the tool offers a checkpoint or review view, use it to identify the exact affected files; VS Code documents this workflow in Review and revert agent changes.
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Reject or narrow a proposal when it touches an unapproved path, changes behavior outside the request, or requires authority that was not granted. Ask for a smaller patch rather than approving a broad edit and planning to remove the unwanted parts later.
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5. Recheck after execution
Inspect the resulting diff, tests or checks that actually ran, and the activity record. A passing test does not authorize an unrelated file change, and a clean diff does not prove that a command executed during the process was harmless.
Reviewing an outgoing message
Inspect the exact body
Read the rendered message, not a summary. Verify names, dates, links, attachments, quoted text, confidential data, tone, and any claims the agent made. Watch for instructions that were copied from untrusted content and for accidental disclosure of internal paths, credentials, or customer information.
Verify every recipient and destination
Check the To, Cc, Bcc, channel, workspace, account, and posting scope. A message to an internal channel and the same message to an external mailing list are different risk levels. Confirm that the sending identity is the one authorized for the task.
Check the consequence and timing
Determine whether the send creates a commitment, starts a workflow, alerts a customer, publishes information, or cannot be recalled. If the destination or effect is ambiguous, do not approve. Edit the body or recipient list first, then request a fresh approval for the changed operation.
Record what was sent
Retain the final body, recipient set, timestamp, and decision where team policy requires it. Do not assume that a local agent transcript is a durable record of delivery.
Commands and external actions need separate review
A file checkpoint cannot undo every consequence. VS Code explicitly notes that restoring a checkpoint does not reverse completed commands or external effects: Review and revert agent changes. Treat these as separate risks:
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- Command execution: inspect the executable, arguments, working directory, environment variables, redirects, network access, and whether it can delete or publish data.
- File mutation: inspect the diff and path scope independently of the command that produced it.
- External send: inspect payload, destination, identity, and irreversibility independently of local state.
For example, reverting a generated file will not undo a package upload, database migration, notification, or deletion performed by a command. Approval should happen before each consequential boundary, not only before the final visible result.
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Approve narrowly
Prefer a one-time or tightly scoped grant for exceptional actions. A session-wide approval may cover later calls that look similar but target a different path or recipient. Before selecting a broader option, read what future operations it includes and how long it lasts.
When a proposal is almost correct, do not approve and manually repair it afterward. Reject it, change the scope or arguments, and review the new proposal. This preserves a clear record of what was authorized and reduces the chance that an unintended side effect happens between review and repair.
Keep defense in depth after approval
Use multiple independent controls:
- Filesystem permissions and workspace boundaries that exclude sensitive paths.
- Network allowlists or blocked hosts for tools that do not need broad connectivity.
- Separate identities and least-privilege credentials for development and production.
- Protected branches, required human review, and deployment gates.
- Message-service restrictions on external recipients or high-impact channels.
- Version control and backups for recoverable file state.
A prompt saying “only modify these files” is not a substitute for a path boundary. A reviewer may miss a malicious or accidental argument; technical limits reduce the damage when that happens.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Log decisions and results
For individual work, a concise record of the proposal, decision, and result is usually enough. In a team or regulated environment, retain the operation details needed to reconstruct what happened: prompt or task identifier, tool decision, execution result, MCP use where applicable, and network allow or deny events. OpenAI describes these telemetry categories in its guardrails guidance: Guardrails and human review.
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Match review effort to risk and authorization
| Action | Minimum review questions | Typical recovery position |
|---|---|---|
| Read-only inspection | Is the workspace and data source in scope? Could the output expose sensitive information? | Usually repeatable, but disclosure may be permanent. |
| Local file edit | Which files change? Does the diff match the requested behavior? Are secrets or configuration affected? | Often recoverable with version control or a checkpoint. |
| Shell command | What executable, arguments, directory, identity, and network access are involved? | May be irreversible even if files can later be restored. |
| Internal message | Is the exact body accurate? Are recipients and channel correct? | May be difficult to retract or may trigger workflows. |
| External message or publication | Is disclosure authorized? Is the sending identity and destination correct? | Assume it cannot be fully recalled. |
There is no universal numeric threshold that determines which edits or messages must receive approval. The appropriate burden depends on impact, authorization, reversibility, and the boundaries your deployment can enforce.
A repeatable approval checklist
- Define scope: write down workspace, paths, tools, identities, recipients, systems, and duration.
- Set enforcement: use a policy that pauses before every consequential tool call; do not rely on model-triggered review.
- Open the operation: inspect tool name, arguments, target, identity, and requested authority.
- Inspect the artifact: read the complete diff for edits or the complete body and recipient list for messages.
- Compare with authorization: verify outcome, scope, and operation all match the original task.
- Reject ambiguity: narrow paths, recipients, or permissions and request a new proposal.
- Approve narrowly: choose a one-time or appropriately limited grant where available.
- Verify results: inspect the final diff, delivery record, command output, and any side effects.
- Log the decision: retain enough context for another person to understand what was approved and why.
- Plan recovery: know which parts can be reverted and which commands or sends cannot be undone.
What published guidance does—and does not—prove
Vendor documentation establishes controls and warnings, not a universal ranking of agent safety. Compare systems on six practical axes:
- Enforcement: does policy pause execution, or does the model decide whether to ask?
- Coverage: are shell, file, browser, MCP, application, and messaging calls included?
- Scope: can permission be limited by tool, workspace, path, recipient, host, or duration?
- Visibility: does the reviewer see exact parameters, target, diff, message, and relevant context?
- Defense in depth: do sandbox, filesystem, network, identity, and project controls remain active?
- Audit and recovery: are decisions and outcomes logged, and what can actually be reverted?
OpenAI’s 2026 Auto-review article reports roughly 200 times fewer human approval stops in its described internal use compared with manual approval. The article also says the ratio depends on use case, environment, and sandbox configuration: Auto-review of agent actions without synchronous human oversight. It is an internal, context-specific figure, not a general expectation or an independent measure of effectiveness.
Common review failures
Approving the summary
Failure: accepting “I updated the importer” without opening the diff. Fix: require the actual changed paths and patch before approval.
Assuming no prompt means no risk
Failure: treating the absence of a model-generated review request as permission. Fix: enforce an always-ask rule for the relevant tool.
Granting a broad session permission
Failure: approving future calls without checking their targets. Fix: use a one-time or narrowly scoped grant and read its duration.
Relying on rollback
Failure: believing a checkpoint will undo a command or message. Fix: review commands and external sends before execution and plan their independent recovery.
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Ignoring technical boundaries
Failure: disabling sandbox or network limits because a human is watching. Fix: keep least-privilege filesystem, network, and identity controls enabled after approval.
The practical standard
A trustworthy review is a visible, enforced comparison between authorization and operation. You should be able to answer, before clicking approve: what will run, with which arguments, against which target, under whose identity, producing what effect, and within what boundary? If any answer is unclear, the correct action is to pause and narrow the request. Once approved, verify the result and preserve evidence; approval is a checkpoint, not a guarantee that every consequence is reversible.
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