An AI-Integrated Multi-Agent Product Development Pipeline for Autonomous Design-To-Production Software Delivery: Architecture and Evaluation

Abstract

This study addresses the persistent fragmentation of artificial intelligence applications across the software development lifecycle, where isolated tools for requirements, coding, testing, deployment, and monitoring often lack coordinated context, workflow control, and end-to-end validation. Its purpose was to develop and quantitatively evaluate an AI-integrated multi-agent product development pipeline for autonomous design-to-production software delivery. A quantitative, cross-sectional, case-study-based design was employed using a structured five-point Likert questionnaire administered to 246 software-development professionals drawn from cloud, DevOps, AI-assisted, and enterprise-oriented software environments. The model examined five independent variables, namely AI Integration Capability, Multi-Agent Coordination, Autonomous Workflow Orchestration, Automated Testing and Validation, and Continuous Feedback and Production Intelligence, with Autonomous Software Delivery Performance as the dependent variable. Data analysis included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, ANOVA, and multiple regression. Results showed favorable assessments across all constructs, with mean scores ranging from 3.96 to 4.15, while Autonomous Software Delivery Performance Recorded M = 4.11 and SD = 0.58. Reliability was strong, with construct alpha values between 0.86 and 0.91 and an overall instrument alpha of 0.94. All five predictors were positively correlated with delivery performance, ranging from r = .61 to r = .74, p < .001. The regression model was significant, R = .822, R² = .676, adjusted R² = .669, F (5, 240) = 100.15, p < .001, explaining 67.6% of the variance in performance. Autonomous Workflow Orchestration emerged as the strongest predictor, β = .241, followed by Continuous Feedback and Production Intelligence, β = .229. The findings imply that effective autonomous software delivery depends not only on advanced AI models but also on coordinated orchestration, rigorous validation, integrated tooling, and continuous production feedback within governed enterprise software ecosystems. These results provide guidance for organizations seeking scalable, reliable, and accountable multi-agent architectures that reduce manual intervention while maintaining software quality. The methodology establishes the quantitative, cross-sectional, case-study design, professional respondent population, five-point Likert instrument, and SPSS-based descriptive, correlation, ANOVA, and regression analyses. The reported findings are based on N = 246, with all five predictors significantly related to Autonomous Software Delivery Performance and the overall model explaining 67.6% of its variance.