ReAct Pattern: Interleaving Reasoning and Action for LLM Agents › Summary

ReAct (Reasoning + Acting) interleaves chain-of-thought reasoning with tool execution in an iterative loop. The pattern consists of three phases per iteration: Thought, which contains explicit natural language reasoning about the current state and next steps; Action, which is a structured tool invocation with specific parameters; and Observation, which is environmental feedback that grounds reasoning in facts from external systems. The complete sequence of thought-action-observation triples forms the agent's trajectory, which is its working memory and audit trail.

The formal trajectory $\tau = (t_1, a_1, o_1, t_2, a_2, o_2, \ldots)$ captures the agent's full reasoning history. At each step, the model conditions on this trajectory to generate the next thought and action. The key insight is that observations provide factual grounding that corrects the model's internal assumptions, reducing hallucination rates substantially compared to pure chain-of-thought prompting.

Key implementation considerations include:

• Few-shot prompting to teach the expected format through concrete examples. Example quality and diversity matter more than count.
• Careful action space design to match the tools available to the class of problems the agent will face.
• Reliable action parsing with helpful error messages that guide the model toward format compliance.
• Stop sequences when using real LLMs to prevent the model from hallucinating observations.
• Loop detection and graceful termination when the agent is not making progress.
• Context management strategies (truncation, summarization, selective retention) for tasks requiring many iterations.

The limitations are real: linear token growth, error propagation in multi-hop chains, action space rigidity, and retrieval quality dependence all require thoughtful mitigation in production systems. But the pattern is simple and interpretable while remaining effective, which has made it the dominant approach to building tool-using LLM agents. It turns a static language model into an interactive agent able to multi-step reasoning, dynamic information gathering, and structured problem-solving. This provides the reasoning foundation for modern autonomous AI systems.