Intelligent agents are transforming industries by enhancing efficiency, personalization, and decisionmaking. This paper explores the applications of reactive, proactive, hybrid, and learning agents across healthcare, finance, manufacturing, retail, transportation, and education. Through examples and case studies, we highlight the impact of agents and their potential to drive innovation and optimize operations.

Large Language Models (LLMs) face signicant challenges related to context length when generating code, often resulting in incoherent or incomplete outputs. This paper aims to explore the context length issue, present technical solutions, and suggest future directions for improving context retention in LLMs used for code generation.

Understanding the limitations of Large Language Models (LLMs) in code generation is crucial for optimizing their use in software development. This paper explores these limitations, discusses their impact on coding practices, and oers insights into potential solutions and areas for further research.

This paper addresses the claim that by enabling intelligent, flexible, and autonomous decision-making at the local scale, MAS can substantially strengthen the performance, reliability, and durability of smart grid operations. We discuss the design and implementation of a MAS for smart grid management through an in-depth case study, emphasizing on particular design decisions, difficulties faced, and results achieved. Among MAS's main benefits are its capacity to guarantee grid stability, reduce overload, and maximize the utilization of resources.