In traditional systems, a user’s query is sent directly to a vector database, and the results are fed into a model. If the initial search fails to find relevant information, the system typically returns an incomplete or incorrect answer.

Secondly, agentic RAG models can enable more efficient and adaptive interaction with complex environments. For example, in dialogue systems, agentic RAG models can be used to selectively retrieve and generate responses based on the user's input and preferences.

Progressive Agentic RAG integrates the concepts of RAG and agentic architecture to create a more advanced and flexible AI framework. The key features of Progressive Agentic RAG include: