The ReAct paper by Yao et al. proposed a paradigm where language models alternate between thinking (reasoning traces) and acting (tool calls or API interactions). This Thought-Action-Observation loop became the foundation for modern AI agents. Understanding ReAct is essential for designing agent-style prompts that combine planning, execution, and self-correction in a single conversation flow.
