ReAct Pattern
Definition
ReAct (Reasoning + Acting) is an agent design pattern where the LLM alternates between reasoning about what to do next and taking actions, creating an explicit thought-action-observation loop.
Why It Matters
ReAct solves a fundamental problem in AI agents: making their reasoning transparent and controllable. By explicitly separating “thinking” from “acting,” ReAct creates agents whose decision-making process you can observe, debug, and improve. This makes agents more reliable and trustworthy than black-box approaches.
How It Works
A ReAct agent follows a loop: (1) Thought - the agent reasons about the current situation and what action to take, (2) Action - the agent executes a tool or API call, (3) Observation - the agent receives the result of the action. This cycle repeats until the task is complete. The explicit “Thought” step provides chain-of-thought reasoning that improves decision quality.
When to Use It
ReAct is ideal for: (1) tasks requiring multiple tool calls with reasoning between them, (2) situations where you need to debug agent behavior, (3) complex tasks where intermediate reasoning improves outcomes, or (4) building trustworthy agents where transparency matters. For simple single-tool tasks, the overhead of explicit reasoning may not be worth it.
Source
ReAct enables LLMs to generate reasoning traces and task-specific actions in an interleaved manner, leading to improved performance on multi-step tasks.
https://arxiv.org/abs/2210.03629