Traditional RAG systems retrieve chunks from a vector database to answer simple factual queries. However, these systems struggle with multi-step reasoning, ambiguous questions, or tasks requiring data verification, often leading to hallucinations.
RAG 2.0 focuses on the joint training and fine-tuning of retrievers and language models to improve overall system performance. In contrast, Agentic RAG introduces an autonomous agent that manages the retrieval process through iterative cycles of planning, tool execution, and result validation.
Agentic RAG architectures utilize an orchestrator to break down user requests into subtasks. The agent can select various tools, such as SQL queries, API calls, or code execution, and rephrase queries if initial results are insufficient, continuing the cycle until a satisfactory answer is reached.
While Agentic RAG offers higher accuracy for complex, multi-source tasks, it involves higher latency and token costs compared to traditional RAG. Implementing agentic scenarios requires careful management of step limits, logging, and access control to prevent cumulative errors and security risks.
