Let It Go: Narrative Lock-in in Open-ended Real-Life AI Agents

LLM-based agents show growing proficiency in structured tasks, yet in open-ended, multi-source, long-horizon settings they remain susceptible to a distinct failure mode: Narrative Lock-in. We define it as the agent's tendency to mistake a fluent, self-consistent, causally closed explanation, in short, a narrative, for reliable task ground truth. Once formed, this erroneous claim consolidates into operational task state and persistently shapes downstream evidence interpretation, planning, and action selection. User corrections, critic modules, and reflection mechanisms often fail to break this state because they may inherit the same flawed premise. The fundamental issue is not reasoning incapability, but the agent's inability to let go of a fluent, self-consistent narrative once formed. To empirically characterize this pattern, we design controlled trigger experiments across eight task domains and four models, testing whether Narrative Lock-in can be reliably induced and quantified. We further propose Nexus, a lightweight external oversight framework that decouples execution from supervision: an Observer logs behavioral facts, a WorldMap maintains structured external state, and a Mentor detects claim–evidence mismatches and issues targeted interventions when lock-in risk is identified. Preliminary results show that Narrative Lock-in is a reproducible, quantifiable failure pattern, and that Nexus can reduce error-path persistence and improve recovery from erroneous task states.