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.

What are the two types of agentic RAG?

Agentic RAG is really flexible. It can be structured in a different way depending on the complexity of your information retrieval needs. Some businesses require a simple setup, while others benefit from a more advanced, multi layered approach.

Single-agent RAG

At its most basic level, the Single agent RAG uses a single routing agent. This is the agent responsible for determining which knowledge source to access when a query is submitted.

While straightforward, this system is ideal for business with simpler information retrieval needs. The routing agent makes decisions on the best retrieval path but it is limited by its single-agent structure, making it suitable for smaller scale use cases.

Multi-agent RAG systems

In contrast Multi agent RAG systems are more complex and versatile. These systems involve multiple specialized agents each focusing on a specific task or data source. The agents work collaboratively to retrieve, process and generate responses.

For example, one agent might handle internal data another might manage external sources and another could specialize in customer specific requests.

What are the use cases of Agentic RAG?

Agentic RAG makes your workflows smoother, decisions faster and data retrieval very easy. So whether you’re dealing with customer queries, managing business operations or handling compliance, Agentic RAG can help. Let us break it down on what are the agentic RAG use cases and how you can use agentic RAG in different area of your business.

Automated data retrieval

You don’t have to dig through multiple platforms to find information you need. Agentic RAG can handle complex queries by breaking them down in to smaller parts, pulling in data from internal and external sources & presenting clear contextually relevant answers.

No need to waste time switching between API, search engine and databases, you get all the information in one place really fast.

Intelligent information synthesis

Takes things a step further. It can merge data from multiple place, consolidating everything into one actionable response. You can make informed decisions without sifting through disconnected pieces of information.

Enhanced search capabilities

The system adapts its search strategy based on what your looking for, choosing the best tool for the job—whether it’s a vector search engine, an API or a specific database. It’s also tool-agnostic, meaning it can connect with a variety of sources to ensure you get the most comprehensive results possible.Check out RAG Cognitive Search to see how easy it is to intelligently retrieve and synthesize information from multiple sources.

Automated decision-making and workflow optimization

Agentic RAG decides whether more details are needed and refines its search strategy. By breaking queries into smaller parts and process them simultaneously, it speeds up response times and handles complex requests with ease. You get accurate information without unnecessary delays.

Context-aware responses

It tailors its output to your needs. Agentic RAG llm adapts based on previous interactions, current context and available data, so responses feels relevant and specific rather than generic. You can even customize how queries are processed to ensure the results align with your exact requirements.

Seamless multi channel integration

You do not have to worry about fragmented datas. Whether you need information from emails, chat platforms, social media or internal docs, Agentic RAG can pull everything into one centralized system. You create a unified knowledge hub, ensuring consistent and accurate responses across different channels.

Scalability and flexibility

Make Agentic RAG adaptable to your needs. You use a simple setup with a single agent or a more complex multi-agent system. Whatever it is, Agentic RAG llm can adjust to different level of complexity. Plus it can integrate with external tools like web scraping service, machine learning model & enterprise software making it useful across various industries.

Floatbot.AI

We bring you the power of Agentic RAG to your business to enable smarter more efficient information retrieval. Handle customer support, manage large scale data, optimize internal workflows effortlessly. Floatbot’s Agentic RAG can seamlessly integrate into your operations without disruption. What you can expect:

1. Reduce support costs by 40%
2. Increase CSAT by 90%
3. Reduce bot training efforts by 98%