As artificial intelligence (AI) technology continues to advance, businesses are beginning to explore whether to rely on a single AI agent or to build a multi-agent network that encompasses a broader range of functions. Recently, the orchestration framework company LangChain conducted related experiments aimed at exploring the performance limits of AI agents when faced with an overload of instructions and tools. LangChain detailed its experimental process in a blog, focusing on the central question: 'What happens when a ReAct agent is asked to handle...

Hugging Face has recently launched an online learning course called 'Agent Course', aimed at helping learners deepen their understanding of the fundamentals and applications of intelligent agents. The course is rich in content and is divided into five modules, progressing from basic concepts of agents to final assessment assignments, assisting students in mastering the necessary skills. The first module is 'Welcome to the Course', providing an overview, guidelines, and required tools to ensure that learners have a solid foundation during their studies. Next is

LangChain is a platform based on language models designed for building and deploying large-scale applications using RAG (Retrieval Augmented Generation) technology. It encompasses query transformation, hypothesis document embedding (HyDE), routing mechanisms, query construction and indexing strategies, retrieval techniques, and the final generation phase. Lately, LangChain has implemented the guide for RAG (Retrieval-Augmented Generation), providing users with a detailed tutorial on how to build