What is a RAG AI agent?

This post introduces and explains the core concepts of Retrieval-Augmented Generation (RAG) and its application in building AI agents. It details the two key functions of RAG: retrieval of relevant, permission-aware information from enterprise sources, and generation by LLMs synthesizing this data. The post outlines the operational flow of RAG AI agents, including query understanding, semantic retrieval, response generation, and optional actions. It highlights the benefits of RAG AI agents for enterprises, such as improved accuracy, reduced hallucinations, enhanced knowledge management, and personalized experiences. Glean's agents are presented as enterprise-grade, secure, scalable, and deeply integrated, offering universal knowledge access, horizontal integration, agentic reasoning, and built-in governance. The post differentiates between single-agent (routing agent) and multi-agent RAG systems, explaining their respective use cases and architectures. It also provides practical guidance on getting started with RAG AI agents, emphasizing starting with a specific use case, connecting the right data sources, choosing a flexible platform, and adopting an iterative approach.