AI 에이전트는 왜 RAG가 필요한가…장기 기억이 만드는 성능 차이

Retrieval-Augmented Generation (RAG) is essential for AI agents to overcome the stateless limitations of large language models by extending their memory and contextual understanding, significantly improving performance and accuracy in enterprise applications. For IT organizations, implementing RAG represents a critical architectural decision that enhances AI agent reliability and capability, requiring careful evaluation of three distinct implementation approaches to maximize business value. This shift from context-window constraints to persistent, retrievable knowledge systems will reshape how enterprises deploy AI agents for complex, knowledge-intensive business processes.