Agentic Reasoning
Overview
Agentic reasoning examines how AI systems carry out goals through sequences of decisions, tool use, and interaction with an environment, rather than producing a single answer. Common approaches include ReAct-style reasoning and acting, task decomposition, retrieval and memory, multi-agent coordination, reinforcement learning, and verification with external tools or formal solvers. Work evaluates these methods on coding, multi-hop question answering, web research, and interactive environments, increasingly measuring process quality and resource use as well as final accuracy. A central tension is that planning, more tool use, and longer reasoning can improve difficult tasks but also add cost and failure points, so agents must learn when to gather evidence, verify, or stop.
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