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Start by checking the task and its scope
Read the original request alongside the agent’s result. Check for missing requirements, unsupported additions, and statements or actions that go beyond what was requested or authorized. A polished answer can still be incomplete or out of scope.
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This matters especially when an agent can use tools or act on its own. Anthropic describes an agent as a system that can plan, act, observe results, adjust, and repeat; misunderstood intent and prompt injection are among the risks discussed in its April 9, 2026 article on trustworthy agents in practice. Review both the answer and, where relevant, what the agent did to produce it.
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Do not try to fact-check every sentence with equal effort. First identify claims that are factual, time-sensitive, consequential, or likely to be repeated by others. For each, identify the evidence offered and check whether it actually carries the claim.
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Check faithfulness, completeness, and sufficiency
- Faithfulness: Does the cited source support the statement being made?
- Completeness: Does the statement preserve relevant qualifications, exceptions, and the source’s broader meaning?
- Sufficiency: Is the source strong enough to justify the claim’s level of certainty and importance?
NIST’s description of evaluation probes for agentic AI treats these as distinct dimensions of citation quality. A citation that looks properly formatted does not, by itself, pass any of these checks.
Open the source and read it in context
Confirm that a source is authentic and relevant, then read enough surrounding material to catch its date, scope, qualifications, and exceptions. A source mentioning the same topic is not necessarily evidence for the particular claim attached to it. For current product features, policies, prices, schedules, and similar volatile facts, check an up-to-date authoritative source before sharing; there is no universal freshness interval that applies to every claim.
NIST describes probes that compare agent claims with a human-curated reference corpus and produce an audit trail. That is a way to structure evaluation, not a guarantee that an automated check proves a claim correct.
Inspect the agent’s work, not just its explanation
When an agent produces code, analysis, or an external action, inspect the underlying artifact, relevant tool outputs, or observable result where feasible. A confident summary of a tool call is not a substitute for checking what the tool returned or what actually changed.
- For code, inspect the proposed changes and relevant output before relying on or running them.
- For research or analysis, compare important conclusions with the source material and any underlying data available to you.
- For actions in another system, verify the target, scope, and result against what was authorized.
OpenAI’s Safety best practices recommend giving reviewers access to information needed to verify outputs and advise human review where possible, especially for high-stakes uses and code generation. OWASP’s AI Agent Security Cheat Sheet likewise recommends validating outputs before display or execution.
Set the approval bar according to the consequences
There is no single review scale that fits every agent task. As a practical rule, increase scrutiny when a mistake could cause serious harm, be hard to reverse, or affect people outside the review team. In particular, treat destructive, financial, administrative, and externally visible actions as reasons for explicit human review.
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For consequential actions, a simple approval prompt may not be enough. OWASP recommends stronger controls, including binding approval to the exact action and independently validating its scope and authorization. Reviewers should be able to tell what will happen and to what target before approving it.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Record what was checked and decided
For work that needs review or approval, leave a concise record of what you checked, which issues you corrected or left open, who approved any consequential action, and what sources support the version being shared. This makes the decision easier to inspect later. NIST describes machine-readable audit trails as a goal for evaluation probes; that does not mean every agent product provides one, so record gaps in the review process where needed.
Quick Recap
A practical review sequence
- Restate the task. Keep the original request at hand and compare it with the result for omissions, additions, and unauthorized actions.
- List material claims. Prioritize factual, current, consequential, or shareable claims and identify the evidence attached to each.
- Open and assess sources. Check authenticity, context, faithfulness, completeness, and sufficiency.
- Refresh volatile facts. Verify details such as current features or policies against authoritative, up-to-date sources.
- Inspect artifacts and actions. Review relevant code, tool outputs, or observable effects where feasible.
- Escalate high-impact decisions. Require meaningful human review and confirm exact scope and authorization before approval.
- Record the decision. Note checks, unresolved issues, evidence, and approvals needed to understand the final version.
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