Revolutionizing ERP Test Strategy Generation Through Multi-Agent AI Frameworks

Abstract

The integration of multi-agent artificial intelligence frameworks into Enterprise Resource Planning test strategy generation represents a transformative shift in quality assurance practices for complex business systems. This article examines how specialized AI agents collaborate to automate the synthesis of diverse knowledge sources, including official documentation, business requirement documents, user interface mockups, code repositories, and existing test management systems. It describes how this framework employs advanced techniques, such as semantic knowledge graphs, retrieval-augmented generation, natural language processing, and deep reinforcement learning, to construct exhaustive test strategies that can adapt dynamically to evolving system requirements. Using belief-desire-intention architectures and Prometheus design models, specialized agents distribute cognitive tasks across documentation retrieval, requirement summarization, test case integration, code scrutiny, strategy composition, and validation processes. Implementation requires human-in-the-loop orchestration interfaces to keep AI-generated strategies transparent and auditable, thereby overcoming one of the critical factors that prevent the adoption of automation: trust. Ultimately, this framework has demonstrated significant improvements in test generation efficiency, defect detection capabilities, and regulatory compliance, while also reducing the cognitive burden experienced by testing professionals. This advancement in technology has enabled the transformation of test management from a reactive, document-based process to a proactive and intelligent ecosystem, with continuous learning and adaptation of the ecosystem in line with organizational requirements.