What are ReAct agents and how do they work?

The ReAct framework is an innovative approach designed to enhance the functionality of large language models (LLMs) by integrating reasoning and action into their workflows, addressing the common issue of making decisions based on incomplete or inaccurate assumptions. Unlike standard LLMs that generate responses solely based on internal data, ReAct agents engage in a continuous cycle of thought, action, and observation, enabling them to interact with external tools, gather real-world data, and refine their conclusions based on actual findings. This iterative process not only improves accuracy by reducing hallucinations but also allows agents to tackle complex, multi-step tasks autonomously, adapting to new information as it becomes available. The framework's tool-agnostic design makes it versatile for various applications, from customer support and research to workflow automation. As a result, ReAct has become a widely adopted pattern in AI agent development, with platforms like Zapier offering accessible solutions for implementing agentic workflows without requiring extensive coding expertise.