EvoToolAgent: Co-Evolving Autonomous Tool Agents and Interaction for Robust LLM Tool Use

Large language model (LLM) agents increasingly rely on external tools to perform complex tasks. However, tool interfaces, output formats, and availability may continuously change during deployment, making it difficult for agents designed for static tool environments to adapt reliably. We argue that the key to dynamic tool adaptation lies not only in recovering from failed tool calls, but also in identifying the sources of failure and directing failure feedback to the appropriate system components. To this end, we propose EvoToolAgent, an attribution-driven framework for agentic system evolution in dynamic tool environments. EvoToolAgent performs component-level failure attribution to localize errors arising during task execution to specific system components, thereby providing a unified mechanism for both within-task local recovery and cross-task system evolution. In addition, we introduce EvoToolBench-D, a benchmark designed to systematically evaluate the adaptability of agents to tool changes and failures. Experimental results demonstrate that EvoToolAgent consistently outperforms baselines across different backbone models and dynamic tool conditions, highlighting the importance of explicit failure diagnosis and structured recovery for tool-using agents in dynamic environments.