Can Tool-Using Agents Keep Adapting as Tool Environments Change?

Large language model (LLM) agents increasingly rely on external tools, yet existing tool-learning methods typically assume relatively stable tool environments. In practice, tools and their interfaces continuously evolve. We study dynamic tool learning, where an agent must retain unaffected tool-use capabilities while efficiently adapting behaviors invalidated by environmental changes. Our key observation is that multi-step tool-use behaviors naturally exhibit ordered and compositional structures. Inspired by natural selection, we propose CATE (Continual Adaptation to Tool Environments), which organizes tool-use experience as an evolving population of skills. CATE represents accumulated tool-use experience as reusable Skills that capture not only individual tool usage but also the ordering and dependencies among tools. Starting from single-tool Skills, it progressively develops compositional multi-step behaviors through interaction, recombination, and failure-driven refinement. When the environment changes, CATE localizes affected skills and adapts them through experience inheritance, preserving still-valid knowledge instead of relearning the entire tool space. To systematically evaluate continual adaptation, we introduce DynaToolBench, a controlled benchmark built on a stable MirrorAPI execution backend. DynaToolBench models tool replacement over three successive timesteps while ensuring that evaluation tasks remain solvable as the environment evolves. Experiments across evolving tool environments show that CATE maintains strong performance over time, effectively recovering capabilities affected by tool changes while largely preserving previously acquired behaviors.