Enhancing Tool Orchestration and Planning for Agentic RAG Systems: A Case Study

Abstract

Traditional RAG faces bottlenecks in industrial environments. To address these limitations, this paper proposes an Agentic RAG architecture that decouples high-level strategic orchestration from technical execution through an LLM Supervisor and domain-specialized sub-agents. Using a proprietary dataset, we evaluate the efficacy of System 1 and System 2 reasoning paradigms alongside tool integration. Our experimental results demonstrate that ReWOO combined with Few-Shot execution guidance achieves a maximum success rate of 93.33%. Furthermore, we introduce an evaluation methodology which reveals that the ToolVerifier module is essential for complex planning. This study establishes that modular planning and robust verification are critical for reliability in knowledge-intensive industrial applications.