Tool-Using Vision-Language Agents

These agents use vision-language models to interpret images, video, documents, or live scenes, then call tools such as search, image viewers, code, specialist perception models, and robot actions. Common approaches structure work as plan–act–observe–reflect loops, with learned tool routing, retrieval and memory, and training through supervised trajectories or reinforcement learning. They are evaluated on visual question answering, charts and documents, long-video search, web navigation, and embodied tasks, where evidence traces and successful actions matter alongside answer accuracy. A central challenge is making tool use both useful and reliable: agents still struggle with visual grounding, choosing and recovering from calls, long-horizon coordination, and balancing accuracy against cost, latency, and safety.