The paper introduces query-conditioned agent action attribution, a task that recovers the source and ordered intermediate evidence for a query-specified aspect of an action taken by an LLM agent. The proposed method uses small open-weight models as attribution proposers and achieves better source and evidence rankings with lower inference cost.