The dangerous failure in an agent workflow is not only that something gets missed. It is that a summary signal gets mistaken for evidence that noth...
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Two reviewers can still leave the same blind spot if they share the same source assumptions. I like treating AI security review as triage, not closure: map the finding, attach evidence, and keep a human-owned list of what remains unresolved.
Exactly right. Shared source assumptions give you correlated blind spots, so two reviewers agreeing is not independence; it is the same bet placed twice. That is why the control I trust is not "a second opinion"; it is "read the primary evidence": the raw findings, the code, the recomputable binding, not another reviewer's conclusion. Triage-not-closure is the right frame. The moment "reviewed" collapses into "clean," the human is ratifying a summary, not the artifact.
That primary-evidence rule is the part that makes the review defensible. A second model can be useful for coverage, but the closure step still needs artifacts: code paths, configs, threat model assumptions, and the exact reason a finding is accepted or dismissed.
That matches what I found when I went looking for exactly that artifact in my own work.
I audited a benchmark I had written and tried to reconstruct why each case was marked the way it was. I could not. The field recorded a judgment per case, but its intended definition was never written down, so what any given assignment was meant to assert could not be recovered. The reason was simply never recorded at the time, and a reason you did not record is not retrievable later by reading the code.
So now the reason is a field. Each case carries its rationale, and the verdict plus the specific property that failed are produced by executing the check rather than by asserting an expected value. A reviewer can now inspect and challenge a concrete artifact instead of an unfalsifiable label.
I still wrote the checks and the cases, so the correlated blind spot you identified remains. This change makes the judgments inspectable; it does not make them independent.
But of the artifacts you listed, the exact reason turned out to be the one that decays fastest. Code paths and configs are still there months later. The reason lives in someone's head unless you make it a field.
That is the uncomfortable part of AI-assisted review: the workflow can look polished while the unresolved evidence is still sitting there. I trust these systems more when the output separates claims, evidence, and open checks instead of flattening everything into confidence.
That separation should probably be more than a presentation preference. It should be part of the review contract.
Claims state what the system concluded. Evidence shows what supports those claims. Open checks identify what the workflow has not established.
The third category is the easiest to omit, because it weakens an otherwise polished conclusion. So if an open checks section is absent, I would treat completeness as unresolved, rather than assume there was nothing left to check.
That would make "reviewed" mean the evidence and the uncertainty were both exposed, not that the artifact was declared clean.