SkillAge: Lifecycle-Aware Skill Aging and Safe Pruning for Self-Evolving Agents

Self-evolving Large Language Model (LLM) agents excel at accumulating procedural skills for complex tasks. Yet, unchecked skill accumulation inevitably triggers severe repository bloat, where redundant or obsolete routines inflate retrieval overhead and disrupt execution fidelity. To tackle this challenge, we propose SkillAge, an online lifecycle-aware framework for verifiable skill governance and safe pruning. SkillAge models procedural knowledge as dynamic assets transitioning across controllable operational states. From trajectory feedback, it extracts multidimensional evidence, including exposure-weighted performance evidence, semantic redundancy, and context cost, to parameterize an evidence-grounded retention score. This score guides a constrained Markov decision process (CMDP) reinforcement learning policy to optimize repository compactness without future-task annotations. Crucially, irreversible deletion is excluded from online actions; instead, candidates are staged in quarantine where a dual-gate replay protocol certifies permanent removal by bounding both cumulative and worst-case capability degradation. Evaluations across continuous lifelong benchmarks demonstrate that SkillAge achieves up to 89.5% repository compression and 92.0% token exposure reduction while reliably preserving critical future capabilities.