Design agent feedback around goals, observations, bounded actions, verification, and correction. Use control-theory concepts to examine stability, observability, and the limits of automation.
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.
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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