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

Long-horizon, tool-using agents must maintain an understanding of task goals, evidence, and current state across many actions. Yet we observe that when an early, plausible but incorrect interpretation becomes the task state on which later decisions are based, agents may continue to plan, verify, use tools, and produce deliverables around that error even as counterevidence accumulates. We call this trajectory-level failure Narrative Lock-in (NLI). We study how these interpretations shape subsequent execution and whether correcting them produces behavioral recovery. To characterize this phenomenon, we analyze 6,035 trajectories from four benchmarks and five models using a five-dimension rubric that spans narrative formation and behavioral propagation. Within the same task and model, NLI trajectories score lower in 34 of 42 matched groups, with mean completion of 0.207 compared with 0.623 for non-NLI trajectories. We then evaluate six correction strategies on a complete matched cohort of 728 selected cases. Strict recovery ranges from 10.7% to 36.4%, while verbal acknowledgment substantially overestimates behavioral recovery. This gap suggests that asking an agent to reconsider often leaves the task state governing its later actions unchanged. Motivated by this finding, we evaluate Nexus, a unified external state-supervision loop that uses preserved evidence to identify conflicts and guide task-state revision. On 75 ClawMark cases, Nexus records 44 wins, 15 ties, and 16 losses against the original trajectories. Together, these results characterize NLI as a measurable failure of long-horizon agent execution and show that evidence-grounded state supervision offers a practical response.