Design and Experimental Evaluation of A ReAct Agent Framework Integrating Retrieval-Augmented Generation

Abstract

With the capability enhancement of Large Language Models (LLMs), LLM-based agent systems have demonstrated extraordinary potential in complex task reasoning. However, challenges such as context limitations and knowledge hallucinations persist in practical engineering applications. This paper focuses on the “Design of ReAct Agent Framework Fused with RAG” and proposes a reusable engineering framework. The proposed framework utilizes the ReAct structure as the core for reasoning execution, employs strictly constrained prompts to achieve reliable tool calls (Prompt as Code), integrates Retrieval-Augmented Generation (RAG) as a domain knowledge tool, and enhances system stability through multi-turn context management and error feedback self-correction mechanisms. Based on a general-purpose ReAct project and a specific school policy QA project, this paper establishes a unified experimental setup and conducts main experiments, ablation studies, and case analyses using an author-constructed QA set based on publicly available university policy documents from a specific university. Results on 50 sampled questions show a 100% tool-call success rate for the tool-enabled variants. The ablation results further indicate that context-window management and early stopping reduce the average number of reasoning steps. However, accuracy gains remain inconclusive under the current EM/F1 evaluation and require further validation. This paper provides an engineering pathway reference for designing LLM Agent systems oriented toward real-world business scenarios.