Everything you need to know about Agentic RAG

Regular RAG systems have some flaw. They do not fully understand conversations. They miss context, forget past interactions and give generic answers.

Agentic RAG, also known an RAG Agents fixes that. Think of it like upgrading a basic RAG system into a smarter agent that thinks for itself. Instead of just pulling information, it remembers context, adapts to the conversation and makes decisions to give you better, more relevant responses. In other words, Agentic RAG (dynamic) turns RAG (rule-based) into a more intelligent and self-directed system.

What are the main components of Agentic RAG architecture?

An Agentic RAG architecture is made up of various interconnected components that enable it to handle complex queries and generate intelligent responses. Let us look at them...

Client interface

The client interface serves as the entry point for your customers or users to interact with the system. So whether through a web portal, mobile application or other interface, this component allows users to submit queries, starting process of retrieving info.

Framework

Manages how the different parts of the system communicate with each other. It makes sure of smooth transfer of data between components and allow your business to integrate external data sources - be it customer data, market intelligence or other tools you already use.

Large language model (LLM)

The agentic RAG LLM is what powers the response generation in the system. Once relevant data is retrieved, the LLM processes it and formulate a response. Which helps create human like, informative text based on the context of the query and the data available.

Routing agent

When a query is received, the routing agent evaluates it and determine the best retrieval method. It applies reasoning to select the optimal pipeline for gathering relevant information.

Query break down agent

For more complex or multifaceted queries, this agent breaks them down into simpler, smaller sub queries. By processing these pieces simultaneously across multiple retrieval pipelines, the agent helps your business to handle more complex request more efficiently.

Fetch agent

A valuable asset for businesses like yours that rely on multiple platforms or services. It can access external tool like vector search engines, web searches, calculators or APIs pulling in additional info from different sources such as social media, email accounts, or enterprise software.

The reasoner

At the core of Agentic RAG is your reasoner that interprets user intent, plan retrieval strategies & evaluate data sources in real time to enhance how clear and relevant the responses are.

Collaborative agent network

A network of specialized agents work together, each focusing on different or specific task for you, paving way for efficient handling of complex queries and diverse datasets.

The Planning & execution

Unlike static systems, Agentic RAG llm can adapt its approach based on evolving information needs which can enable it to manage some really complex queries effectively.

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