Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteBefore sharing AI agent work, verify the evidence behind its important claims, check that the work stayed within the request, and inspect any consequential actions or outputs. A polished response is not proof of accuracy. Use a human reviewer for high-impact decisions and treat automated evaluation as a way to structure checks—not as a substitute for judgment.
Start by checking the task and its boundaries
Compare the result with the original request, not just with the agent’s summary of what it did. Check for missing requirements, unsupported additions, claims beyond the requested scope, and actions the requester did not authorize. This matters because an agent may plan, use tools, observe results, and revise its approach; a plausible final answer can conceal a mismatch between the task and the work performed. Anthropic describes this operating loop and discusses risks including misunderstood intent and prompt injection in Trustworthy agents in practice.
Verify the claims that matter
Do not try to fact-check every sentence with equal effort. Identify claims that are factual, current, consequential, or likely to be repeated, then trace each one to the evidence offered for it. NIST describes evaluation probes that compare agent claims with a human-curated reference corpus and can produce an audit trail; this is evaluation work in development, not a guarantee that a probe proves an answer correct. See NIST’s Building Evaluation Probes into Agentic AI.
Check whether a citation supports its claim
Open the cited source and read enough context to understand what it actually says. Confirm that it is authentic and relevant, and that the cited passage supports the specific claim rather than merely discussing the same subject. NIST distinguishes three useful dimensions:
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- Faithfulness: Does the source support the statement?
- Completeness: Does the statement preserve relevant qualifications and the source’s full message?
- Sufficiency: Is the source strong enough to carry the claim at the level of certainty or importance used?
A citation can look properly formatted and still fail any of these tests.
Keep qualifications attached to the claim
Look for details that change a statement’s meaning: dates, exceptions, geography, version, eligibility, and whether a result is conditional. A source that supports a narrower or qualified point does not justify a broader, unqualified version. If the evidence leaves an important point unsettled, narrow the wording or mark the uncertainty instead of presenting it as established fact.
Recheck facts that may have changed
Features, policies, prices, schedules, and other time-sensitive details should be checked against current authoritative sources before sharing. Use a source appropriate to the claim—such as current product documentation for a feature—and note the relevant date or version when it affects the reader’s decision. There is no universal freshness interval: how recently a fact needs checking depends on how quickly it can change and what happens if it is wrong.
Inspect what the agent produced or did
When the agent creates code, analysis, or an artifact, inspect the underlying output or test it where feasible; do not rely only on the agent’s account of success. For external actions, compare the relevant tool results or observable outcome with the request and authorization. OpenAI recommends giving reviewers access to information needed to verify outputs, and advises human review wherever possible, especially for high-stakes uses and code generation in its Safety best practices. OWASP likewise advises validating agent outputs before displaying or executing them in its AI Agent Security Cheat Sheet.
Raise the approval bar with the consequences
Use more scrutiny when a mistake could cause greater harm. In particular, require explicit human review before destructive, financial, administrative, or externally visible actions. A simple approval prompt is not always enough: OWASP recommends controls that bind approval to the exact action and independently validate its scope and authorization. OpenAI also highlights human review for high-stakes domains and generated code. The appropriate review process depends on the action and its impact; the cited guidance does not define one universal risk scale.
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For work that matters, record what was checked, which issues were corrected or left open, which sources support the final version, and who approved any consequential action. This makes the decision easier to audit or revisit. NIST identifies machine-readable audit trails as a goal for its evaluation probes; a reviewer’s own record is a practical process choice, not a feature guaranteed by every agent product.
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- DUAL INTERFACE SMART CARD: Contactless NFC ISO 14443 plus ISO 7816 contact reader support in an ISO 7810 ID-1 format that is passive and needs no battery
- SWISS ENGINEERED DESIGN: Built by Cryptnox as a single card for authentication and access control and backed by a 2 year warranty
A practical review checklist
- Restate the request: Compare the result and actions with the original scope and authorization.
- Prioritize claims: Identify current, consequential, or easily repeated factual statements.
- Trace evidence: Open the sources and test faithfulness, completeness, and sufficiency.
- Check freshness: Verify changeable details against current authoritative material.
- Inspect outputs and actions: Review artifacts, relevant tool results, or observable outcomes where feasible.
- Escalate high-impact work: Obtain explicit review and validate the exact authorized action.
- Record the decision: Note checks, unresolved issues, evidence, and approvals.
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