What is Agentic RAG?

Agentic RAG is an advanced form of Retrieval-Augmented Generation (RAG) that integrates AI agents into the workflow to enable more complex, multi-step reasoning and problem-solving. It goes beyond the linear, “one-shot” approach of standard RAG by introducing autonomous decision-making.

How It Differs from Standard RAG

• Standard RAG: Operates as a simple, static pipeline. A user’s query is used to retrieve a relevant document from a knowledge base, and that document is passed to the large language model (LLM) to generate a response. It’s like a librarian fetching a single book for you.
• Agentic RAG: Functions like a research assistant. An AI agent is placed in the pipeline to dynamically plan a strategy for answering the query. This may involve:
  • Tool Use: The agent can decide which tools to use, such as a web search, a specific database, or a calculator.
  • Multi-Step Reasoning: It can break down a complex query into smaller sub-tasks, retrieve information for each step, and then synthesize the findings.
  • Self-Correction: The agent can evaluate its own retrieved information and decide if it needs to go back and search for more or different sources to ensure a more accurate answer.

In essence, Agentic RAG gives the AI system the autonomy to not just retrieve information, but to actively plan, act, and reason about how to best fulfill a user’s request. This makes it far more capable of handling complex queries that require multiple steps and diverse data sources.