The advent of large language models (LLMs) has revolutionized artificial intelligence, laying the foundation for sophisticated intelligent agents capable of reasoning, perceiving, and acting across diverse domains. These agents are increasingly central to advancing AI research and applications, yet their design, evaluation, and enhancement pose intricate challenges. In this talk, we will offer a fresh perspective by framing intelligent agents through a modular and cognitive science-inspired lens, bridging AI design with insights from neuroscience to propose a unified framework for understanding their core functionalities and future potential. First, we explore the modular design of intelligent agents and propose a framework for cognition, perception, action, memory, reward systems, and so on. Second, we examine self-enhancement mechanisms, uncovering how agents can autonomously refine their skills, adapt to dynamic environments, and achieve continual learning. Third, we address collaborative and evolutionary intelligent systems, highlighting how agents interact, cooperate, and evolve within multi-agent systems and societal structures. Finally, we tackle the imperative of building safe and beneficial AI, focusing on ethical alignment, robustness, and trustworthiness in real-world applications. Our talk aims to provide a holistic and interdisciplinary perspective for intelligent agent research.