Rag 20

Rag 2.0 represents an advanced approach to building Retrieval-Augmented Generation (RAG) systems optimized for AI agents. Unlike traditional RAG implementations that treat retrieval as a straightforward lookup mechanism, Rag 2.0 integrates agentic capabilities that enable systems to autonomously decide what information to retrieve, when to retrieve it, and how to apply it during reasoning tasks. This shift moves RAG from a static document-matching tool toward a dynamic decision-making component within broader AI agent architectures.

Integration with Knowledge Graphs

A key feature of Rag 2.0 is its integration with knowledge graphs. By combining agentic RAG with structured knowledge graphs, the system can leverage semantic relationships and entity connections to enhance retrieval accuracy. This hybrid approach allows the AI agent to navigate complex data structures, infer missing links, and provide more contextually relevant responses compared to standard vector-based retrieval methods alone.