What is the ReAct prompting pattern and why is it used for agents?

ReAct (Reason + Act) interleaves reasoning traces with actions, so the model alternates Thought, Action, and Observation steps. The Thought plans the next move, the Action calls a tool or API (e.g. search, calculator), and the Observation feeds the tool result back into context before the next Thought. This lets the model ground its reasoning in external data instead of relying only on parametric memory, which reduces hallucination and enables multi-step tasks. It underpins many tool-using agents: the loop repeats until the model emits a final answer. Compared with plain chain-of-thought, ReAct can verify facts and recover from errors mid-task, but it needs a controller to parse actions, execute tools, and enforce step limits to avoid loops.