Low-code and no-code platforms have revolutionized software development by leaps and bounds. These tools have enabled individuals with minimal coding experience to create and deploy applications quickly and efficiently. These platforms offer ready to use plugins to accelerate development along with options such as drag-and-drop components, pre-built templates to ease accessing various libraries. The tools help software developers to create software programs at speed unlike traditional methods of manually writing every piece of code. This paper outlines the impact of lowcode/no-code platforms, including their function in speeding innovation and reducing development costs, as well as addressing the growing demands of digital transformation. At the same time, it looks at the challenges and obstacles from scalability through security to customization that organizations need to consider when implementing these platforms. Furthermore, the potential future of low-code/no-code development in forging collaboration between technical and non-technical teams in software development. Ultimately, these platforms will bridge the gap between company needs and IT solutions much faster. However, their integration into enterprise settings must be carefully managed so as to ensure sustainability as well as efficacy.

Customer support process has seen a transformational shift in recent years. With the rapid proliferation of Artificial Integrating (AI), the human driven customer support has leapfrogged into automated customer services & support. The field of Artificial Intelligence (AI) has seen phenomenal rise in the development of agents' capability right from autonomous decision-making and problem-solving abilities to adapt to changing environments. Their proficiency in learning from experience and adaptation greatly influences their effectiveness. They both drive improvement over time and insure response to dynamic customer needs and conditions. Adaptation and learning also permit strategies to be revised in light of new data provided to them. This paper explores the bases and techniques of adaptation and learning in AI agents, focusing on reinforcement learning, supervised learning, unsupervised learning, and the combination. In addition, it also addresses challenges like overfitting, exploration-exploitation tradeoffs and computational economics. The role of these processes in various AI applications, including robotics, natural language processing and autonomous systems help industry reduce dependency on manual effort and improve efficiency to drive growth.