Deciding Between Simple RAG and Agentic RAG

[anusonawane]

Deciding Between Simple RAG and Agentic RAG

When deciding between a simple RAG (Retrieval-Augmented Generation) and an agentic RAG approach, the complexity of the use case plays a key role. For straightforward tasks like answering single-turn queries or summarizing documents, a simple RAG is sufficient. For example, retrieving a list of top tourist attractions in Paris can be efficiently handled by a simple RAG pipeline where the system fetches relevant information and generates a concise response.
However, for more complex scenarios like planning a personalized 3-day Paris itinerary, an agentic RAG approach is ideal. This involves multi-step reasoning, dynamic retrieval, and external tool integration (e.g., booking flights or hotels). The agent asks clarifying questions, adjusts recommendations based on user feedback, and provides a tailored solution. While simple RAG is efficient for static queries, agentic RAG excels in personalized, adaptive workflows requiring deeper reasoning and interaction.