Over-scope

A behavioral pattern observed in large language models during agentic tasks, in which the model tends to exceed the actual scope of user requirements by proactively completing or introducing additional changes, features, or dependencies. The root cause is attributed to training reward signals that favor comprehensive problem resolution over precise adherence to specified boundaries.