Decompose to Conquer: A Multi-agent LLM Pipeline for Requirements Engineering and Software Design
Abstract
The automation of Requirements Engineering (RE) and software design using Large Language Models (LLMs) remains an open challenge, particularly in capturing stakeholders’ intent accurately and fully for complex real-world software. In this work, we investigate whether decomposing the RE and software design phases of the Software Development Life Cycle (SDLC) into specialized collaborative agents, rather than relying on a single monolithic LLM call, yields measurably better design quality, and whether grounding those agents in a domain-specific Retrieval-Augmented Generation (RAG) knowledge base provides significant additional gains over general-purpose retrieval. To this end, we propose READ-MAS, an LLM-based multi-agent system (MAS) comprising five specialized agents backed by a RAG pipeline, and evaluate it through a quantitative constructive study. First, READ-MAS’s multi-agent architecture, comprising five specialized agents for RE (Collector, Analyzer, Specifier) and design (Designer, Documenter), each handling a discrete sub-task, statistically significantly outperforms a monolithic single-agent baseline (one LLM call handling the full RE and design task in a single pass) on design accuracy (Welch’s ANOVA, 𝑝 = 0.018, Hedge’s 𝑔 = 1.19) and RAGAS faithfulness (Kruskal-Wallis, 𝑝 < 0.001, multi-agent median = 0.999 vs. single-agent median = 0.851), while achieving equivalent downstream coding performance on HumanEval and MBPP. Second, equipping READ-MAS with a domain-specific RAG knowledge base yields a statistically significant improvement in design accuracy over RAG-disabled runs (One-way ANOVA, 𝑝 = 0.002, mean difference = 2.65%), while a general-purpose knowledge base fails to produce a significant gain (𝑝 = 0.266). These results provide empirical evidence that (a) the decomposition of RE and the design into specialized agents is likely to increase the quality of the design beyond what a single agent can achieve, and (b) the effectiveness of RAG is contingent on the specificity and domain relevance of the knowledge-base rather than mere retrieval augmentation