What is Agentic RAG

Agentic Retrieval-Augmented Generation (RAG) marks an advancement in AI applications by incorporating AI agents into the RAG pipeline, enhancing its flexibility and accuracy beyond the limitations of traditional RAG. While traditional RAG relies on a single knowledge source and lacks validation of retrieved information, agentic RAG uses AI agents to orchestrate retrieval processes with access to multiple tools and sources, enabling more robust and dynamic responses. These agents, equipped with reasoning capabilities, perform additional tasks such as query formulation and context evaluation, thereby improving the quality of information retrieval. The evolution from vanilla to agentic RAG is supported by frameworks like LangChain and CrewAI, which simplify the integration of agents, although challenges such as increased latency and potential unreliability persist due to the inherent limitations of language models. Despite these challenges, enterprises are increasingly adopting agentic RAG systems to benefit from their enhanced capability to autonomously perform tasks and interact with human users effectively.

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