The current trajectory in the research area is marked by a significant shift towards autonomous and AI-driven systems that enhance efficiency and decision-making in various domains. There is a notable emphasis on integrating large language models (LLMs) with specialized tools and platforms to create intelligent agents capable of handling complex tasks autonomously. These agents are designed to understand user intent, plan data processing pipelines, and execute tasks with minimal human intervention, showcasing advancements in both reasoning and tool mastering abilities of LLMs. Additionally, there is a growing focus on optimizing computational costs and context usage in LLM-based agents, which is critical for their practical deployment. The integration of these agents into existing platforms, such as GIS systems and agricultural data management, is paving the way for more accessible and efficient operations, particularly in sectors like agriculture and spatial analysis. The potential for these autonomous systems to achieve high levels of performance, as demonstrated by the success in Kaggle competitions, underscores their transformative impact on data science and beyond. Notably, the field is witnessing a convergence of traditional practices with modern technologies, aiming to create resilient and sustainable ecosystems. The advancements are not only enhancing productivity and sustainability but also democratizing access to sophisticated analytical tools, thereby improving the livelihoods of users in various sectors.