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An AI agent should not be able to turn a suggestion into an irreversible action just because it can call a tool. Build a brake pedal into the system: limit the agent’s authority outside the model, validate proposed tool calls at the boundary where they can cause side effects, preview meaningful changes, and require explicit review before high-consequence actions run.
A confirmation dialog alone is not enough. It works only if execution is actually paused and the reviewer can see what will happen, with which arguments, to which resource. Approval is one layer of control—not a guarantee against prompt injection or unsafe behavior.
What an approval gate should do
An approval gate pauses a run before a sensitive tool call causes a side effect. The reviewer sees the proposed action and relevant arguments, then makes an explicit approve-or-reject decision. The application enforces that decision: approval lets the operation proceed; rejection prevents it.
OpenAI’s API documentation describes human review as pausing a run so a person or policy can approve or reject a sensitive action. In its Agents SDK workflow, an application resolves the interruption and resumes the same run state. That distinction matters: a message telling the model to “ask first” is not a gate if the tool can still execute without an externally enforced decision. OpenAI: Guardrails
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Keep automatic checks and human review conceptually separate. Guardrails can check inputs or outputs against rules; human review supplies a decision about a particular action. Use both where warranted, and apply checks at the tool boundary rather than assuming a top-level agent check will cover every call in a multi-agent workflow.
Constrain authority before adding approvals
The safest approval prompt is one the agent cannot bypass—and the strongest fallback is limiting what it can do even if a request is approved or the model is manipulated. Define the agent’s identity, task, resources, tools, and permitted operations outside its own instructions. Avoid giving it broad access to a user account or static credentials that reach beyond the task.
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- Scope access narrowly: grant only the tools, resources, and operations needed for the task. Prefer resource- and operation-specific permissions over a general account credential.
- Isolate execution: where feasible, run agent-generated code in a sandbox or separate environment, with filesystem and network access restricted to what the task requires.
- Broker credentials: keep application and long-lived credentials out of agent-generated code. Use a trusted application or proxy to mediate access to services.
- Enforce boundaries independently: separate identity, project, filesystem, and network limits so that a mistaken or manipulated agent cannot simply expand its own authority.
NIST discusses the risks of broad local-user access and static credentials in agent deployments, while OpenAI’s sandbox guidance emphasizes isolation, network restrictions, and keeping application credentials separate from agent-generated code. These controls reduce the potential impact of a bad action; they do not establish that an agent is safe in every environment. NIST: AI Agent Identity and Authorization OpenAI: Safety in building agents
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Choose the right control for each action
Not every action needs the same kind of review. Match the control to the consequence, scope, and ambiguity of the operation. A read-only lookup may need no per-call prompt; a broad or irreversible change may need a preview and an enforced approval before execution.
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| Control pattern | When it acts | What it is good for | Important limitation |
|---|---|---|---|
| Scoped permissions and isolation | Before and throughout the task, at identity and environment boundaries | Limiting reachable tools, resources, operations, and network paths | Does not determine whether an allowed action matches the user’s intent |
| Tool-boundary validation | When a proposed call reaches the tool that can cause a side effect | Checking arguments and, where relevant, results against policy | Rules must cover the relevant tool and operation; agent-level checks alone may miss calls in multi-agent workflows |
| Plan or change preview | Before a multi-step execution begins, with opportunities to intervene during execution | Reviewing a coherent set of proposed steps or changes without prompting for every small operation | A plan is useful only if execution remains tied to the reviewed intent and can be stopped when it diverges |
| Per-action approval | Immediately before a sensitive call runs | Making a high-consequence action contingent on an explicit decision | Frequent or context-poor prompts can lead to reflexive approval |
| After-the-fact review | After execution | Auditing or detecting what happened | Cannot prevent a side effect that has already occurred |
The strongest practical design combines these controls: narrow what the agent can reach, validate calls at the point of use, and reserve human approval for actions where a mistaken execution would matter. A review process should also define what happens when intent or arguments are unclear, when the reviewer is unavailable, or when the request times out. For sensitive actions, fail closed: do not execute unless an authorized decision is received. OpenAI’s guidance for authorized cybersecurity workflows recommends failing closed when review times out or becomes unavailable, alongside independent filesystem, network, identity, and project boundaries. OpenAI: Guardrails
Make review concrete with previews and dry runs
A reviewer cannot meaningfully approve an action they cannot understand. Before execution, show the operation, target resource, important arguments, and expected changes in terms that make consequences clear. For a multi-step task, show a plan or proposed changes as a unit when the environment supports it, then preserve a way to intervene if execution departs from that plan.
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Anthropic describes Plan Mode as a way to show a plan for review and editing before actions occur, with the ability to intervene during execution. This is the useful idea behind a dry run: present what the agent proposes to do without applying those changes, then allow execution only after the appropriate checks. A preview is not a dry run if it already modifies the target, sends a message, or exposes data. Anthropic: Trustworthy agents in practice
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For each preview, make it possible to answer:
- Which tool or operation will run, and against which account, project, file, or record?
- What are the meaningful arguments, including recipients, scope, amount, or content where applicable?
- What will change, and is the action reversible?
- Does the preview match the action that will actually execute?
- Can the reviewer reject it or stop later steps without the operation happening anyway?
If the target or arguments change after review, treat that as a new proposal and revalidate it. Otherwise, approval can become detached from the actual action. A plan-level review can reduce repetitive prompts, but it should not silently authorize materially different actions introduced later.
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Prevent approval fatigue without weakening the gate
Prompts lose value when they appear too often or ask for decisions without useful context. NIST warns that frequent requests can habituate users into approving reflexively; its article states, “When users become habituated to thoughtlessly approving access requests, HITL undermines the non-repudiation and accountability it was meant to provide.” NIST: AI Agent Identity and Authorization
Reduce unnecessary interruptions by separating low-risk, bounded work from sensitive actions and using plan-level review for coherent multi-step changes where appropriate. Do not address fatigue by making the approval button implicit, preselected, or detached from the proposed action. Keep consequential decisions explicit and provide enough context to make them deliberate.
OpenAI reported roughly 200 times fewer human-approval stops in Auto-review than in manual approval mode in a Codex-session comparison. In an illustrative internal snapshot, 720 out-of-sandbox actions would have interrupted users under manual approval; Auto-review reviewed them, rejected seven, and those seven split into four that continued by a safer path and three that stopped for user input. OpenAI also reported that around 99% of the small fraction requiring review were approved. These are figures from the described internal deployment, not general benchmarks or a safety rate; OpenAI notes that ratios depend on use case, environment, and sandbox configuration. OpenAI: Introducing Codex
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Approval, previews, sandboxing, and scoped permissions address different failure modes, but none makes prompt injection impossible. An agent can still propose an unsafe action within its allowed boundary; an execution environment can expose data; and a reviewer can approve the wrong thing if the prompt is unclear or the action changes after review. Anthropic cautions that safeguards are not a guarantee and points to risks across tools, harnesses, environments, and prompt injection. OpenAI’s SDK documentation likewise places review and enforcement responsibilities in the application’s own harness. Anthropic: Trustworthy agents in practice OpenAI: Guardrails
Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.” That autonomy is why a brake pedal must be architectural: instructions can guide behavior, but permissions, validation, and approval enforcement must operate outside the model. Anthropic: Trustworthy agents in practice
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