Tool Use

Tool use in AI studies how language and multimodal agents choose tools such as APIs, code interpreters, search systems, and robotic actions, then use their results to complete tasks. Common approaches train agents on synthetic or verified interaction traces, use reinforcement learning to improve multi-step planning and tool selection, and add runtime mechanisms for state tracking, verification, and recovery. Benchmarks test function calling, long workflows, multimodal and embodied tasks, and performance when tools or environments are unreliable. A central challenge is making tool use both selective and dependable: agents must know when a tool helps, execute calls correctly, and recover from failures without unnecessary calls or harmful side effects.