The field of human-agent collaboration and large language model applications is rapidly evolving, with a focus on developing more reliable, efficient, and trustworthy systems. Researchers are exploring ways to integrate human-provided information, feedback, and control into agent systems to enhance performance, safety, and reliability. Another key area of research is the application of large language models in various domains, such as education, healthcare, and software engineering, to automate complex workflows and improve decision-making processes. Furthermore, there is a growing emphasis on ensuring the privacy, security, and transparency of these systems, with proposals for frameworks and methodologies to mitigate potential risks and vulnerabilities. Notably, the development of open-source, autonomous multi-agentic frameworks and platforms is facilitating the creation of more accessible and reproducible solutions. Overall, the field is moving towards more sophisticated and human-centered approaches to human-agent collaboration and large language model applications. Noteworthy papers include: Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants, which proposes a methodological framework for integrating LLM agents with digital twins to improve fault handling in process plants. mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging, which introduces an autonomous multi-agentic framework for end-to-end development and deployment of medical AI models.