Feedback Loops in AI Systems

Feedback loops in AI systems are the mechanisms by which user actions, corrections, and preferences flow back to improve agent behaviour. This includes explicit feedback (thumbs up/down, corrections, ratings), implicit feedback (which suggestions users accept vs ignore), and structural feedback (users adjusting agent permissions or boundaries). Well-designed feedback loops create a virtuous cycle where the agent gets better with use. Poorly designed ones create frustration or, worse, reinforce bad behaviour.