何为Agent？在思想、学术与工程领域探寻“好用”真义

Core Viewpoint - The article discusses the evolution and significance of AI Agents, emphasizing their transition from single-function tools to more autonomous and capable systems that integrate various technologies and methodologies [2][3]. Group 1: Definition and Concept of AI Agents - AI Agents are defined as a combination of large models (brain), memory (vector databases), planning (goal decomposition), and tools (API calls), which work together to create a more autonomous intelligent toolset [2][3]. - The exploration of AI Agents reflects human curiosity about the essence of intelligence, leading to both surprising advancements and potential pitfalls in their application [2]. Group 2: Academic and Engineering Insights - The article highlights the need to define AI Agents from both technical and philosophical perspectives, drawing from work and research experiences [3]. - It discusses recent trends and highlights in the academic field regarding multi-agent systems and the unique challenges faced by specialized agents in sectors like healthcare, finance, and mental health compared to general-purpose agents [3][7]. Group 3: Practical Challenges in AI Agent Implementation - The article addresses the core pain points in the practical application of AI Agents, noting that despite their powerful capabilities, they often behave unpredictably in real-world scenarios, akin to "opening a blind box" [3]. - Key technical challenges include weak contextual memory and planning abilities, which affect the usability of AI Agents [3]. - It emphasizes the importance of distinguishing between scenarios where message-based memory suffices and those requiring external knowledge bases for effective long-term memory [3].