A model upgrade can change what an AI agent does with existing permissions. Requalify its action boundaries, compare tool behavior, test independent controls, and expand production autonomy only when the evidence supports it.
Understand how rewards and feedback shape AI behavior. Separate reinforcement learning, human preferences, and persistent adaptation, then evaluate whether rewarded behavior actually achieves the intended outcome.
Control which feedback becomes a persistent change and who may approve it. Use promotion manifests, scoped evaluation, traceable release decisions, and tested withdrawal paths.
Evaluate what an agent attempted, what executed, and what happened to the service. Keep control compliance, appropriate behavior, and verified outcomes separate in the scoring and release decision.
Define AI rewards around verified service outcomes. Separate permissions, success, escalation, and efficiency so an improved metric cannot conceal missing evidence or poor operational behavior.
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