Out of Step with Evidence: Task Progress State Mismatch in Long-Horizon Tool-Use Agents

Long-horizon tool-use agents must carry forward results from earlier work: later steps need to determine what has been completed, what remains open, and what must be revised. Three judgments can diverge: whether local work is correct, whether task progress state is updated appropriately, and whether the task is ultimately completed. Prior analyses characterize final outcomes, erroneous steps, stage-wise progress, or process dynamics, but at units of analysis that do not capture how intermediate results change task progress state. We analyze natural trajectories from six long-horizon task environments to examine whether and how task progress state mismatches arise, persist, and are repaired. At each handoff where local work begins to shape subsequent execution, we record what the preceding step accomplished, which state updates the evidence permitted or required, which updates the agent enacted, and the downstream fate of the resulting state. Across 2,622 trajectories, only six of thirty-four cataloged phenomena recur in all six environments, each with distinct signatures; both candidate mismatch relations—updates exceeding what the evidence permits and required updates left unenacted—are locatable everywhere yet diverge in composition; models share the main problem types but differ in environment-local ways; and within single executions, activity is not enactment, operation success is not correct state, and a receipt is not recovery. Task progress state analysis provides the missing intermediate layer between local work and final outcomes, identifying which states should be retracted, re-verified, reopened, or re-bound before mismatches compound. Reliable long-horizon agency requires more than correct steps: it requires progress that stays in step with the evidence.