The use of multi-agent systems (MAS) as a distributed control method for shop-floor manufacturing control applications has been extensively researched. MAS provides new implementation solutions for smart manufacturing requirements such as the high dynamism and flexibility required in modern manufacturing applications. MAS in smart manufacturing is becoming increasingly important to achieve increased automation of machines and other components. Emerging technologies like artificial intelligence, cloud-based infrastructures, and cloud computing can also provide systems with intelligent, autonomous, and more scalable solutions. In the current work, a decision-making framework is proposed based on the combination of MAS cloud computing, agent technology, and machine learning. The framework is demonstrated in a quality control use case with vision inspection and agent-based control. The experiment utilizes a cloud-based machine learning pipeline for part classification and agent technology for routing. The results show the applicability of the framework in real-world scenarios bridging cloud service-oriented architecture with agent technology for production systems.