Agentic RAG = Agent + Retrieval-Augmented Generation (RAG)

Agentic RAG is an advanced form of RAG where a language model acts as an intelligent agent — able to plan, reason, use tools, and retrieve information iteratively to answer complex queries.

RAG

RAG (Retrieval-Augmented Generation) is a technique that combines a retriever (to fetch relevant documents) with a generator (like a language model) to produce more accurate and informed responses by grounding answers in external knowledge.

🔁 Retrieve → 📄 relevant data → 🧠 Generate → 📢 better response

It’s especially useful when the LLM doesn’t have the answer in its training data.

AGENT

An Agent in AI is a system that can perceive, reason, act, and learn to achieve a goal — often using tools or external resources.