What is agentic RAG?

Introduction

Agentic RAG refers to the integration of Artificial Intelligence (AI) agents into the Retrieval-Augmented Generation (RAG) process. By enhancing the standard RAG pipeline with intelligent agents, these systems offer greater adaptability and improved accuracy. Unlike traditional RAG setups, agentic RAG empowers large language models (LLMs) to retrieve information from multiple sources and manage more sophisticated, multi-step tasks.

Retrieval-Augmented Generation, or RAG, is a way to make AI answers more accurate by connecting the AI to an external source of information. Instead of relying only on what the AI already knows, RAG lets it search a knowledge base to find helpful facts. This extra context helps the AI give better answers, especially for specific topics—without needing to retrain the model.

Instead of relying only on what the AI learned during training, RAG-enabled models can pull in up-to-date information in real time using APIs or other data sources. A typical RAG setup includes two main AI components: one that finds the most relevant information and another that uses that information to generate a response.

Agentic AI refers to AI systems that can decide what to do and take action on their own. Most AI agents today are powered by large language models (LLMs) that can use tools or call functions to complete tasks. In general, an AI agent has three key traits:

1. Goal-Driven –

   It can understand a goal or objective.

2. Planning Ability –

   It can figure out the steps needed to reach that goal.

3. Autonomy –

   It can carry out those steps with little or no human help.

Agentic RAG works by adding one or more AI agents into the RAG process. These agents can take on different roles based on their strengths. For example, one agent might be great at searching external databases, while another focuses on scanning emails or browsing web content. Each agent specializes in a specific area or data source, and together they help the system gather the most relevant information to answer a question more effectively.

Agentic RAG enhances results by using function calling, multi-step thinking, and multiple AI agents working together. However, it’s not always the best choice. More agents mean higher costs, and these systems usually use more tokens, which can add up. While agentic RAG can sometimes be faster than traditional RAG, the time it takes for the AI to generate answers might also cause delays.

Also, agents can be unpredictable. They might have trouble completing tasks, especially if those tasks are complex or if the agents aren’t well matched. Sometimes, agents don’t cooperate well and may even compete for the same resources. The more agents involved, the more complicated their coordination becomes, increasing the chance of issues. And even the best RAG system can’t completely avoid AI mistakes, like hallucinations.

Conclusion

Agentic RAG represents an exciting advancement in AI by combining retrieval-augmented generation with intelligent, autonomous agents. This approach enhances the flexibility and accuracy of AI systems, allowing them to handle complex tasks and pull information from multiple sources effectively. While it offers clear benefits over traditional RAG, such as improved adaptability and multi-step reasoning, it also comes with challenges like higher costs, increased complexity, and occasional reliability issues. As AI technology continues to evolve, agentic RAG holds great promise for powering smarter, more capable applications—especially in specialized and dynamic environments.