This paper proposes and implements a Generative Automatic Matching (GAM) approach, an innovative solution to handling the heterogeneity of systems and architectural diversity in developing modern applications. GAM overcomes the limitations of existing methods by fully automating the matching and model generation process, eliminating reliance on fixed algorithms and heuristics that lack adaptability. By embedding a multi-agent system—a focused area of artificial intelligence—GAM enables distributed problem-solving and enhances the efficiency of system integration across heterogeneous environments. Implemented on the .NET platform, GAM utilizes intelligent agents to coordinate and execute matching operations, demonstrating a significant improvement over traditional methods that often require manual or semi-automated processes. The approach is applied to the Elementary Case Study (ECS) to showcase how GAM tackles the complexities of diverse system architectures. System-to-system interactions are fully automated through the multi-agent framework, ensuring complete integration. In the developed .NET application, performance evaluation using machine learning quality metrics underlines this scalable, adaptable framework’s high degree of accuracy and reliability, positioning GAM as a versatile and dependable framework for addressing current and emerging challenges in system development and integration.