Interleaving thinking and acting produces more capable agents than either alone — ReAct became the foundational pattern for LLM-based autonomous agents.
Proposes interleaving reasoning traces and actions in language models, allowing them to create plans, execute tool calls, and update their reasoning based on observations. Outperforms both reasoning-only and acting-only approaches.