Agentic RAG

Agentic RAG integrates autonomous AI agents into the RAG pipeline, enabling dynamic query planning, multi-source retrieval, and iterative context refinement for superior LLM accuracy.

This is the next-gen RAG: We embed autonomous AI agents into the retrieval pipeline, shifting from passive data fetch to active, intelligent problem-solving. Unlike traditional RAG’s static, one-shot retrieval, Agentic RAG agents utilize reasoning, planning, and tool-use (via frameworks like LangChain or LlamaIndex) to dynamically manage the workflow. For example, an agent can analyze a complex query, break it into sub-tasks, query multiple sources (e.g., a vector database and a live web API), and then perform iterative refinement to validate the context before final generation. This adaptive process delivers a significant boost in precision: Early enterprise adopters report up to a 40% increase in task automation for intricate, multi-step knowledge retrieval.

https://huggingface.co/blog/agentic-rag