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Governance patternstructural

Put a verifier on the agent

Attach an independent checking step to the least-supervised operator — in PAN runs, the single highest-leverage move.

What it changes

dampenedFailures adopted by people or agentsIllustrative PAN-run result: ≈45.8% steady-state harm removed in a published PAN run (vs ≈6.3% for a model upgrade alone).

Who can pull it

Deploying organizationHarness builderDeveloper

What it looks like institutionally

Autonomous and lightly-supervised operators are where adopted errors concentrate: nothing stands between the model's output and action. Attaching a verifier — a human check, a second independent system, a mandatory ground-truth lookup — inserts correction exactly where correction was absent.

The general principle: rank operators by how unsupervised they are, and spend verification there first. A verifier on an already-well-supervised workflow buys little; the same verifier on the automated pathway can dominate every other option.

Illustrative PAN-run comparisons make the point sharply — see the ledgered scenario result below — but the qualitative logic stands on its own: correction capacity matters most where it is currently zero.

Ledgered PAN-run results used above

In a published PAN model run over a supervised-plus-agent scenario, adding a verifier to the autonomous agent removed roughly 45.8% of steady-state harm and a coordinated governance package roughly 43.0%, while upgrading the model alone removed roughly 6.3%.[†]

Addresses: Unsupervised error adoption · Automation without correction. Test a version of this lever in the PAN Lab.

Documented cases behind this pattern

Michigan MiDAS

Automated unemployment-fraud adjudication with no human review — the canonical high-error, zero-correction structure.

Deciding whether this lever fits your deployment?

Which patterns matter — and in what order — depends on your system's actual shape. Ranking your options on evidence, with what can backfire stated, is engagement work.

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Sources & Evidence

Claims made on this page and what supports them. The full registry lives in Evidence.

ScenarioIn a published PAN model run over a supervised-plus-agent scenario, adding a verifier to the autonomous agent …

In a published PAN model run over a supervised-plus-agent scenario, adding a verifier to the autonomous agent removed roughly 45.8% of steady-state harm and a coordinated governance package roughly 43.0%, while upgrading the model alone removed roughly 6.3%.

Illustrative PAN-run result: PAN Social Work User Guide (v6.2.11.6), lever-ranking walkthrough (fig03). Direction-and-shape only — not a calibrated prediction for any specific deployment.