The landscape of autonomous AI agents is shifting from basic conversational interfaces to sophisticated software capable of complex tasks. This evolution necessitates careful consideration of when to intervene.
Runtime safety layers have emerged as a critical component in managing these agents, yet their effectiveness in timing interventions remains a topic of concern.
This research underscores the shortcomings of relying on affect-based triggers and large language model judges, suggesting that current strategies may not adequately address the nuances of intervention timing.