Tool-Using Agents

Tool-using agents are language-model-based systems that choose and invoke external tools—such as search, code, databases, or graphical interfaces—and use the results to pursue goals over multiple steps. Research develops tool selection and retrieval, planning and decomposition, memory and reusable skills, and training with supervised trajectories or reinforcement learning; runtime harnesses add state tracking, verification, permissions, and recovery. Evaluation is shifting from isolated function calls and final answers toward stateful workflows, measuring task completion alongside cost, policy adherence, robustness to errors, and intermediate actions across web, terminal, office, and domain-specific tasks. A central tension is that broader autonomy and longer tool chains expand capability but also amplify grounding errors, stale state, unsafe side effects, and hidden tool failures, so reliable execution and calibrated stopping remain open problems.