// An Anatomy of CLI Coding Agent Trajectories // (bookmark it) When your coding agent fails a task, when did the run actually go wrong? Mos…

// An Anatomy of CLI Coding Agent Trajectories // (bookmark it) When your coding agent fails a task, when did the run actually go wrong? Most reliability studies use the final label to answer this. The run passed or it failed, and the trajectory in between stays a black box. That tells you the outcome but not the moment the agent lost the thread, and not whether an intervention a few steps earlier would have saved it. This is the first large-scale study of CLI coding-agent failure trajectories. It treats failure as a timeline with three parts, the onset where things first go wrong, the evolution where the error compounds, and the point where recovery becomes impossible. Why does it matter? If you build or supervise coding agents, a pass rate tells you how often to worry. A failure anatomy tells you where to intervene. Knowing where trajectories become unrecoverable is what turns constant babysitting into a checkpoint you can actually place. Paper: https://arxiv.org/abs/2607.09510 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-07-13

Related