Development of an Agentic Retrieval-Augmented Generation System with Hybrid Retrieval for Budget Planning and Validation
Abstract
Abstract Budget planning and validation require users to examine cost references, compare proposed budget items with relevant guidelines and historical records, and provide traceable evidence for each decision. This process becomes challenging when the required information is scattered across formal cost standards, budget plans, project realization records, and internal narrative documents. This study develops a budget validation system based on Agentic Retrieval-Augmented Generation (RAG) with hybrid retrieval. Small and medium enterprises are used as the application domain, while the main contribution lies in combining structured retrieval and semantic retrieval to support evidence-grounded budget validation. Structured information, including cost guideline items, budget components, and historical realization data, is stored in a relational database. Meanwhile, narrative documents are converted into Markdown chunks and indexed in a vector database. Three workflows are implemented and compared: Single-Agent RAG, Routing-Based Agentic RAG, and Orchestrator-Workers Agentic RAG. The evaluation is conducted using 30 test cases and four RAG-based metrics: context precision, context recall, faithfulness, and answer relevancy. Latency is also measured as an additional indicator of response time. The results show that Routing-Based Agentic RAG achieves the highest context recall score of 0.873 and the highest faithfulness score of 0.878, while sharing the highest context precision…
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