Understanding the differences and advantages of multi-agent systems (MAS) over single-agent systems is crucial for leveraging their full potential. This paper explores these distinctions, addressing how MAS overcomes the limitations of single-agent systems and highlighting their functional and application-specific benefits.

Understanding the limitations of intelligent agents is crucial for optimizing their applications and mitigating potential risks. This paper explores the technical, functional, ethical, and practical limitations of agents, highlighting their impact on various domains and suggesting strategies for improvement.

The "Structured AI-Driven Software Development" method leverages AI to write software through detailed planning and documentation. This approach emphasizes the importance of converting comprehensive business requirements into precise technical instructions for AI systems, enhancing the accuracy and effciency of software development. This paper explores the methodology, providing a roadmap for generating detailed documentation and instructing AI code pilots effectively.

This paper explores agents in artificial intelligence, focusing on their definitions, characteristics, and applications. Agents are autonomous entities that perceive, decide, and act to achieve goals, with key attributes like autonomy, reactivity, and social ability. The use of agent-based models in economic and environmental research is highlighted. Future directions and challenges, including ethical considerations and technical improvements, are discussed, emphasizing agents' potential to address complex real-world problems.

Understanding the limitations of Multi-Agent Systems (MAS) is crucial for advancing their development and application. This paper explores the key technical, functional, ethical, and practical constraints of MAS, highlighting their impact on various applications and suggesting potential areas for future research.

This paper explores the diverse applications and future potential of intelligent agents across various domains. By examining reactive, proactive, hybrid, and learning agents, we highlight their unique capabilities and benefits. Detailed examples and case studies illustrate how agents enhance efficiency, adaptability, and personalization in healthcare, finance, robotics, and entertainment. The paper also discusses emerging trends and future developments in agent-based technologies.

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.

This paper explores the dierences between assigning coding tasks to human developers versus AI copilots. By examining instruction types, formats, quantities, qualities, structures, and additional information, we aim to highlight how these elements inuence the expectations and outcomes in software development.

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.

Understanding language is a complex task, both for humans and AI. This paper explores the inherent challenges in language comprehension, focusing on the diculties faced by AI in achieving true context awareness.

This paper explores the importance of tracking the intelligence and effectiveness of intelligent agents. By examining key metrics such as accuracy, response time, and user satisfaction, and discussing practical methods for evaluation, we provide a comprehensive guide to assessing agent performance.

Multi-agent systems (MAS) signicantly enhance various applications by enabling collaboration, coordination, and scalability beyond the capabilities of single-agent systems. This paper explores the key applications of MAS across dierent industries, highlighting the advantages and dierences compared to single-agent systems.