The recent advancements in the research area have primarily focused on enhancing the collaboration between humans and intelligent systems, as well as improving the efficiency and adaptability of multi-agent systems. A significant trend is the development of frameworks that dynamically adjust the level of assistance and communication based on task complexity and user needs, aiming to optimize cognitive load and user satisfaction. These frameworks leverage advanced models, such as Fitts' Law and Large Language Models (LLMs), to benchmark performance and facilitate real-time adjustments in human-robot interaction. Additionally, there is a growing interest in extending traditional process modeling languages to accommodate human-agentic collaborative workflows, ensuring clarity and efficiency in mixed human-agent scenarios. Novel approaches to process discovery are also being explored, with a particular emphasis on handling complex loop structures to improve the accuracy and simplicity of process models. These developments collectively push the boundaries of human-AI collaboration and multi-agent systems, offering scalable solutions for enterprise applications and beyond.