The field of distributed computing and process intelligence is rapidly evolving, with a focus on developing innovative solutions to manage and optimize complex systems. Researchers are exploring new approaches to distribute intelligence across heterogeneous devices and platforms, enabling adaptive and resilient service management. The integration of artificial intelligence and machine learning techniques is becoming increasingly important, with applications in areas such as video quality monitoring, process mining, and server load distribution. A key trend is the shift towards decentralized and distributed computing paradigms, such as edge computing and the Computing Continuum, which require novel solutions for configuration selection, load balancing, and quality of service management. Noteworthy papers in this area include:

• A study on distributed stream processing pipelines managed by Active Inference agents, which achieved over 90% service level objective fulfillment.
• A framework for adaptive configuration selection in multi-model inference pipelines, which significantly improved quality of service while reducing costs.
• A comprehensive blueprint for video quality monitoring in remote autonomous vehicle control, which leveraged AI models and proactive decision-making to enhance reliability and transparency.