The field of agentic workflow generation and reasoning is witnessing significant developments, with a focus on enhancing the robustness and efficiency of large language models (LLMs) in complex tasks. Researchers are exploring novel training frameworks and parallel execution paradigms to improve the reliability and trustworthiness of LLMs. Notably, dynamic workflow generation frameworks are being proposed to adaptively construct and adjust reasoning procedures based on task requirements and real-time intermediate feedback. Additionally, formally defined and verified methodologies are being introduced to bridge the gap between formal methods and real-world development practice in software engineering. These advancements have the potential to substantially improve the performance and scalability of LLMs in various applications. Noteworthy papers in this area include: RobustFlow, which proposes a novel training framework to teach models invariance to instruction variations, achieving substantial improvements in workflow robustness scores. Flash-Searcher, which introduces a parallel agent reasoning framework that fundamentally reimagines the execution paradigm from sequential chains to directed acyclic graphs, consistently outperforming existing approaches. DyFlow, which proposes a dynamic workflow generation framework that adaptively constructs and adjusts reasoning procedures based on task requirements and real-time intermediate feedback, significantly outperforming existing baselines. PBFD and PDFD, which introduce formally defined and verified methodologies for scalable, industrial-grade full-stack software engineering, demonstrating over 20x faster development and 7-8x faster query performance compared to conventional relational models. A Tale of LLMs and Induced Small Proxies, which introduces Falconer, a collaborative framework that combines the agentic reasoning of LLMs with lightweight proxy models for scalable knowledge mining, reducing inference cost by up to 90% and accelerating large-scale knowledge mining by more than 20x.