Attribution-Guided Skill Evolution for Online Continual Agent Learning

LLM agents increasingly face streams of tasks where experience from earlier tasks should be reused while adapting to new ones. We study this setting as online continual agent learning, where a shared external skill library is updated sequentially from completed tasks. Existing methods leverage agent experience to refine reusable skills through reflection, feedback, or reinforcement learning, but task-level outcomes provide limited insight into individual skill contributions. Failures may stem from missing activation, non-adherence, or incorrect application, while task success does not guarantee that every invoked skill is useful, making direct feedback-driven updates prone to inappropriate modifications. We propose AGSE (Attribution-Guided Skill Evolution), a plug-and-play framework that attributes skill–trajectory interactions along five dimensions: activation, adherence, correctness, coverage, and precision. These attributions provide structured, skill-specific evidence for guarded skill repair, expansion, and scope refinement, without model-weight updates or policy training. Across three heterogeneous CLI-agent frameworks, AGSE achieves exact accuracy of 72.9%, 59.4%, and 48.9% on OfficeQA Pro, and completion rates of 59.64%, 49.40%, and 48.19% on SkillFlow. In continual learning evaluation, AGSE achieves an FS of 67.7, FWT of +0.0227, and forgetting rate of 13.95%. Ablations demonstrate the importance of both multidimensional attribution and continual skill evolution.