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An AI can explain how to send an email and be wrong; the mistake is still advice for a person to assess. If an AI agent sends the email, it has changed the outside world. That difference adds execution risk—not because wrong answers are harmless, but because an action can take effect before anyone catches an error.
Why does acting add risk?
An answer usually informs a human decision. An agent can also use connected tools to browse, sign in, operate a computer, run code, or control a physical device. The more a tool can change, the more the agent’s mistake can become an immediate consequence rather than a recommendation someone can reject.
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NIST’s 2025 discussion of tool use in agent systems separates perception, reasoning, and actions that directly affect an environment. It also identifies factors that shape risk: the action’s criticality, possible harm severity, whether effects persist or compound, and whether they can be reversed. A search-only connection is not equivalent to one that can send messages, delete records, or make purchases.
That does not make answers risk-free. A false explanation can lead someone to make a damaging choice. The additional concern with an agent is that it may execute that choice itself, potentially without a person reviewing the result first.
What should you check before granting an agent access?
There is no universal risk score in the cited guidance. Instead, assess the specific task across these dimensions:
- Permission: Can the tool only read and search, or can it write, send, delete, buy, or otherwise change data?
- Input trust: Is the agent working from information you supplied and trust, or from untrusted websites, emails, and files that may contain malicious instructions?
- Reversibility: Can you readily undo the result, or could it persist, spread, or be difficult to recall?
- Consequence: What happens if the agent misunderstands the task or follows a malicious instruction?
- Timing of review: Will you review the proposed action before it happens, or only notice a problem afterward?
These factors work together. Read-only research from unfamiliar pages still has a risk of misleading you, but it cannot directly send money if the connection truly lacks payment permissions. A write-enabled agent handling sensitive information from untrusted sources presents a different exposure, especially if its actions are hard to undo.
How can untrusted content redirect an agent?
An agent may read material that contains instructions written to manipulate it, such as text hidden in a webpage, email, or file. NIST calls this kind of threat agent hijacking: malicious instructions embedded in data can try to redirect an agent away from the user’s intent. This is why an agent should not treat every instruction it encounters while browsing or processing documents as authoritative.
NIST’s January 2025 guidance on strengthening hijacking evaluations emphasizes that evaluations need to adapt as attacks change and should account for the consequences of the particular task. A prompt-injection defense that performs well in one test is not proof that every agent, tool, or workflow is protected.
Which actions deserve a confirmation or human review?
Review is most valuable before an action that is consequential, difficult to reverse, or based on untrusted inputs. For lower-impact tasks, a constrained tool or an after-the-fact log may be proportionate. For a sensitive message, purchase, deletion, or other consequential change, it is prudent to inspect the exact action and its target before execution.
OpenAI’s January 2025 Operator system card describes confirmation before certain state-changing actions, a watch mode, and proactive refusals for some higher-risk tasks. These are examples from one system, not controls guaranteed in every agent. Confirmation can catch a misunderstanding, but it cannot ensure a reviewer notices every problem or that the proposed action is safe.
In that system card, OpenAI reported that its post-mitigation Operator model requested confirmation with average recall of 92% on a 607-task evaluation spanning 20 risky-action policy categories. Recall here is the fraction of cases where confirmation was needed that triggered a request. The card also reported 94% recall for refusing selected high-risk tasks on a synthetically generated evaluation set. Both figures describe OpenAI’s bounded evaluations, not general agent performance.
The same card reported 99% recall and 90% precision for Operator’s prompt-injection monitor on 77 red-team-created attempts, while it flagged 46 of 13,704 benign screens. Those system-specific test results do not guarantee that a monitor will catch an attack in a different product or setting. Its March 2025 API update also reported 38.1% Computer-Using Agent performance on OSWorld and recommended human oversight in those scenarios; this is dated context for that system, not a current measure of agent reliability generally.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What safeguards make agent use easier to review?
- Limit permissions: Grant only the access needed for the task. Prefer read-only access when the agent does not need to change anything.
- Separate proposal from execution: Where possible, have the agent prepare a draft or proposed change for a person to inspect before it is sent or applied.
- Require approval selectively: Set review before actions with meaningful financial, privacy, security, or operational consequences, particularly when they are hard to reverse.
- Keep an evidence trail: Record what the agent did and which sources supported its decision so a person can investigate an unexpected result.
- Evaluate the actual workflow: Test with the tools, inputs, and consequences the agent will encounter; do not assume that a general capability label or a past benchmark establishes safety for a new task.
NIST’s 2026 work on evaluation probes for agentic AI describes comparing an agent’s claims with trusted documents and preserving an audit trail that connects decisions to supporting evidence. That makes review more concrete: rather than relying only on an agent’s assertion, a reviewer can see what it found and how the evidence relates to its conclusion.
OpenAI’s 2023 paper, Practices for Governing Agentic AI Systems, uses the phrase “AI systems that can pursue complex goals with limited direct supervision” for agentic systems. It is a useful description, not a universally settled definition. In practice, focus less on the label “agent” and more on the permissions, inputs, and real-world effects of the system in front of you.
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