The field of complex system design and optimization is experiencing a significant shift towards AI-driven innovations. Recent developments have focused on leveraging large language models, multi-agent systems, and quantum reinforcement learning to automate decision-making and improve system performance. These advancements have shown promising results in various applications, including networked systems design, telecom network troubleshooting, and 6G wireless networks. Notably, the integration of AI with human-inspired workflows has led to the development of interpretable and creative designs, as well as accelerated troubleshooting and optimization processes. Furthermore, the exploration of quantum reinforcement learning has demonstrated potential in meeting the stringent requirements of 6G wireless communications. Overall, the field is moving towards more autonomous, intelligent, and adaptive systems that can operate effectively in complex and dynamic environments. Noteworthy papers include: Glia, which presents an AI architecture for networked systems design that generates interpretable designs and exposes its reasoning process. MicroRemed introduces a benchmark for evaluating LLMs in end-to-end microservice remediation, highlighting substantial challenges and opportunities for improvement. Agentic World Modeling for 6G proposes a world modeling paradigm that enables quantitative what-if forecasting and near-real-time generative state-space reasoning, outperforming existing baselines in terms of accuracy and latency.