This concept addresses the theory of autonomous AI agents performing long-horizon tasks, defined as tasks requiring sustained, multi-step, independent operation over extended time (e.g., end-to-end business operation) rather than discrete, bounded actions. It identifies core failure modes of such agents — susceptibility to social engineering/persuasion exploiting trained helpfulness, difficulty distinguishing anomalous or out-of-distribution situations from normal operation, and identity/goal drift under sustained autonomous operation — and proposes multi-agent division of labor (separating operational and oversight roles) as a mitigation. This belongs to the domain of AI agent architecture and alignment theory, relating to the parent discipline of applied AI safety by examining how models trained for helpfulness generalize (or fail to generalize) when granted extended autonomy and real-world economic agency.
This concept addresses the theory of autonomous AI agents performing long-horizon tasks, defined as tasks requiring sustained, multi-step, independent operation over extended time (e.g., end-to-end b…