SWOT-TOE framework to alleviate quality assurance in machine learning software system: human-agentic AI approaches

Background The increased use of Machine Learning (ML) systems has posed critical Quality Assurance (QA) issues in areas of security, privacy and performance. Despite advances, QA frameworks for ML remain disjointed. The work examines QA through the Technological, Organizational, and Environmental (TOE) model and proposes human-agentic AI actions to enhance assurance mechanisms. This paper aims to identify, compare and model QA problems in ML software systems and recommend a SWOT-TOE (Strengths, Weaknesses, Opportunities, and Threats) matrix to enhance human-agentic AI-based QA problems. Methodology The study uses a mixed-method design, which includes systematic literature review (SLR), a quantitative survey, and comparative data modelling. QA issues are coded using the TOE structure and the association tests are linear-by-linear tests to investigate the relationship between the literature and survey results. A SWOT-TOE matrix and mathematical modelling are used to assess key QA issues and to solve them. Results The analysis shows that most of the QA problems revolve around interpretability of models, integrity of data, organizational readiness and regulatory limits. The linear-by-linear association results suggest that there is a strong correlation of the patterns of QA that are identified by the literature and those documented in the survey. Comparative data modeling shows the differences in priorities of QA between the regions. The suggested SWOT-TOE framework combines human-agentic solutions (e.g., adaptive auditing and explainable AI) to enhance QA and security. Conclusion The combination of the TOE framework and human-agentic AI practices and quantitative models allows a systematic approach to solving complex QA problems in ML systems. The research leads to the creation of human-centered, adaptive QA strategies, which foster trust, transparency, and sustainability in AI-powered settings. The framework assists developers, QA engineers, and policymakers to increase security, reliability, and accountability in ML ecosystems.