Multi-Agentic Systems for Automated Task Execution

Dear Colleagues,

The direct application of large language models (LLMs) and vision-language models (VLMs) as autonomous agents has become increasingly significant in advancing artificial intelligence. This Special Issue focuses on the development and application of multimodal multi-agent systems designed for automated task execution across diverse environments, including web-based platforms, operating systems, and software applications.

A central theme of this topic concerns agentic workflows that enable agents to interpret textual instructions, decompose tasks into subtasks, and execute them autonomously in a structured, step-by-step manner. Such systems are particularly relevant for web-based automation, where agents process natural language commands to interact with online environments, similar to OpenAI's Operator. Another key area of interest is operating system-level automation, where intelligent agents execute complex tasks on platforms such as Android, iOS, general computing environments, or even embodied environments. Additionally, software and application automation through multi-agent collaboration can optimize workflows for tasks like code debugging, system maintenance, and multi-step data processing.

The Special Issue also highlights the role of reasoning, long-horizon planning, and tool integration in enabling effective multi-agent collaboration. Reinforcement learning and adaptive agentic workflows are essential for enhancing decision-making, ensuring adaptability, and improving efficiency in autonomous task execution. We invite contributions that explore novel methodologies, architectures, and real-world applications of multimodal multi-agentic systems for task automation, addressing key challenges and innovations in this rapidly evolving field.

We look forward to receiving your contributions.

Dr. Yunpu Ma
Dr. Zhen Han
Guest Editors

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