The Human Quality Layer for AI Agents [Testμ 2026]

At Testμ Conf 2026, Microsoft security assurance engineer Justin Roy argued that technical performance alone does not determine whether an AI agent is ready for deployment, because adoption depends on a “human quality layer” of visibility, understanding, influence, ownership, and appropriately calibrated trust. He described quiet rejection through behaviors such as duplicate checking, retaining old spreadsheets, private audit trails, and restricting agent use to low-risk tasks, which can make adoption dashboards appear successful while workflows become slower and less reliable. Roy proposed treating these human factors as testable engineering and operating requirements, including human-readable run records with evidence and uncertainty, verifiable explanations, meaningful controls for changing or stopping actions, clearly assigned decision and escalation roles, and approval processes tied to specific, time-limited actions. Using a hypothetical incident-response agent, he emphasized testing real-world conditions such as ambiguity, stale data, partial failure, conflicting sources, cancellation, and incorrect outputs rather than relying on successful demonstrations. He recommended beginning with one consequential workflow, involving people accountable for its outcomes, documenting workarounds as evidence of design gaps, and adjusting autonomy according to demonstrated capability, risk, reversibility, and users’ ability to supervise or challenge the system.

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