With the recent addition of A2A (Agent-to-Agent) protocol support in CleverChatty, it’s now possible to build powerful, intelligent applications—without writing any custom logic. In this blog post, we’ll walk through how to build an Agentic RAG (Retrieval-Augmented Generation) system using CleverChatty.
🤖 What is Agentic RAG?
The term agentic refers to an agent's ability to reason, make decisions, use tools, and interact with other agents or humans intelligently.
In the context of RAG, an Agentic RAG system doesn’t just retrieve documents based on a user’s prompt. Instead, it:
- Preprocesses the user’s query,
- Executes a more contextually refined search,
- Postprocesses the results, summarizing and formatting them,
- And only then returns the final answer to the user.
This kind of intelligent behavior is made possible by using a Large Language Model (LLM) as the core reasoning component.
The goal of a RAG system is to enrich the user’s query with external context, especially when the required information is not available within the LLM itself. This typically involves accessing an organization’s knowledge base—structured or unstructured—and providing relevant data to the LLM to enhance its responses.