In this study, a distributed agent-oriented integration model is proposed for the effective integration of business intelligence systems within cloud computing environments. The existing integration challenges such as data compatibility, security, resource constraints, and real-time synchronization are analyzed. The study investigates the use of autonomous agents to enhance adaptability, scalability, and performance of business intelligence systems. Key components of the model, including data collection, analysis, integration, visualization, and monitoring, are examined in detail. It is identified that agent-based interaction supports parallel processing and improves decision-making speed. A multi-layered model structure is presented, where each agent performs specific tasks and communicates through defined protocols. Mathematical formulations for each stage of the integration process are developed to ensure clarity and precision. The model is implemented in an educational context to illustrate its versatility and effectiveness. Comparative evaluation results demonstrate that the proposed model surpasses traditional and centralized systems in terms of speed, efficiency, and optimized resource usage. The overall contribution of the study is the development of a reliable and scalable integration framework for intelligent data-driven decision-making systems in cloud environments.