MA-HAI-RGF: A Multi-Agent Human-AI Collaborative Requirements Generation Framework for Complex Systems

Abstract

Requirements engineering is a critical yet labor-intensive phase in software development, particularly for complex systems where stakeholder needs are diverse, evolving, and often ambiguous. Traditional approaches struggle with scalability, consistency, and completeness when dealing with large-scale requirements. This paper proposes MA-HAI-RGF, a Multi-Agent Human-AI Collaborative Requirements Generation Framework designed to address these challenges through intelligent automation and effective human-AI collaboration. The framework employs a multi-agent architecture with specialized roles including elicitor, analyzer, validator, and prioritizer agents that work collaboratively with human stakeholders. We introduce interaction protocols for human-in-the-loop engagement, automated consistency checking mechanisms, and conflict resolution strategies. To make the empirical basis explicit, we evaluate the framework through a DeepSeek-V4-Flash simulation benchmark over three complex-system domains. Compared with a single-stage AI baseline, MA-HAI-RGF reduces the mean Validator-detected conflict rate from 12.5% to 8.3% and the mean ambiguity count from 5.0 to 0.7. The framework therefore improves the quality and reviewability of generated requirements while maintaining explicit stakeholder approval gates.