Tests, observations and decision records that show how an agent behaves under relevant conditions.
Agent systems change after release, so evidence must keep moving. Automation can collect and test signals; people remain responsible for judgement and action.
Give decision owners a balanced view of outcomes, quality, human work, cost, risk and change—with every measure tied to an action.
Connect spending to accepted workflow outcomes by counting retries, review, correction and failures alongside model and platform charges.
A technical review can trace data and test controls, but it cannot alone determine lawful basis, resolve policy choices or certify compliance.
A dramatic exploit is a discovery, not a decision rule. Translate it into a threat condition, required control, repeatable regression and release consequence.
Confidence is not a universal percentage. It is a judgement about evidence quality, uncertainty, consequence and the reversibility of the proposed release.
A useful assurance partner improves the quality of a specific AI decision while leaving ownership, evidence and operational accountability with the organisation.
Client control means owning the decision standard, representative cases, evidence access and release authority—not forcing every test to be built or run internally.
Keep workflow cases, graders and decision rules separate from the platform so alternatives can be compared without rebuilding the standard of proof.
Grade findings by consequence, reach, reproducibility and control strength, then keep that technical judgement separate from the release decision.
Age is only one signal. A dataset becomes stale when its cases, labels or conditions no longer support the decision the evaluation is meant to inform.
A useful release pack connects the intended workflow, evaluated behaviour, unresolved risk and operating controls to one accountable decision.
KEEP EXPLORING
Read the method, assumptions and questions to ask.