Abstract

This blog presents a comprehensive, progressive implementation of Agentic Retrieval-Augmented Generation (Agentic RAG) — an evolution of traditional RAG systems that integrates autonomous agent behavior into the retrieval and generation loop. Starting from foundational concepts, the notebook guides the reader through building baseline RAG pipelines and integrating various agentic patterns such as multi-step retrieval agents, query rewriting agents, reflection-driven agents, and multi-agent orchestration strategies.

Each module highlights different agent behaviors: planning, reasoning, iterative retrieval, and tool selection, showing how they dynamically improve information recall and response quality. The notebook combines theoretical insights with practical, runnable code using commonly used framework LangChain. By the end, readers will understand how to design and deploy scalable Agentic RAG systems tailored for complex real-world tasks.

Technology Stack Used:

• LLMs - Groq
• Framework - LangChain
• Storage & Retrieval - FAISS
• Embedding Model - HuggingFace

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Introduction

In the rapidly evolving landscape of artificial intelligence, traditional models are giving way to more dynamic and intelligent systems. One such advancement is Agentic Retrieval-Augmented Generation (RAG)—that enhances the capabilities of AI system by its integration with autonomous agents into the RAG framework.

At its core, Agentic RAG combines the principles of Retrieval-Augmented Generation with agentic AI. While traditional RAG systems enhance large language models (LLMs) by retrieving relevant information from external sources to generate accurate responses, Agentic RAG introduces AI agents that can autonomously manage complex workflows, make real-time decisions, and adapt to dynamic contexts.

Why Agentic RAG?
Traditional Retrieval-Augmented Generation (RAG) approaches have encountered a set of well-known limitations, such as:

1. Single Query Bottleneck: The system often performs only one retrieval pass using a single query. If the initial query fails to capture the nuance of the user’s intent, the whole answer suffers.
2. Shallow Understanding: There's no iterative reasoning or breakdown of the question. For example, if the question has multiple parts, RAG doesn't analyze or decompose it — it treats it as one flat prompt.
3. Limited Context Window: RAG systems can only inject a small number of retrieved documents (due to token limits), which may not cover the full scope of the topic.

In light of these challenges, the need arose for a more capable system—one that integrates reasoning, multi-step task execution, and tool use. This paved the way for the development of Agentic RAG.

An Agentic RAG system offers several advantages over traditional RAG:

1. Question assessment and dynamic query generation - Unlike traditional RAG, Agentic RAG does not involve directly passing user query for information retrieval. Based on the prompt it has the ability to rephrase query for better information retrieval.
2. Tools - Structured tools for bridging the gap between LLM and the information stored as vectors. These tools allow the model to fetch context using the query it determines to be most relevant.
3. Agent driven retrieval - Based on the retrieved information, Agent can decide if more context is required to answer the question. If so, Agent continues to retrieve information using tool by passing relevant query to the tool for final answer formulation.

Alongside its promising benefits, Agentic RAG also comes with a few concerns that need careful consideration:

1. Token Hungry Approach - Since Agentic RAG often performs multiple retrieval steps, it can quickly consume a large number of tokens, leading to higher token costs.
2. Redundant Retrievals - In some cases, even when enough information has already been gathered, the agent may continue to call tools unnecessarily. This adds to the execution time and increases cost without improving the output.
3. Quality LLMs - To effectively process and generate answers from the large amount of retrieved context, the performance heavily depends on the quality of the language model used and how well the prompt is crafted.

With all this information there arises a question;
"When Should You Opt for Agentic RAG?"

Below table will help you get a clear understanding on which approach can be a better option for your use-case.

Just a suggestion -
When considering whether to implement Agentic RAG, remember the principle: "Don’t build a system unless the problem you’re solving is worth solving."
This is especially relevant in the world of AI and machine learning. While Agentic RAG offers advanced capabilities, it’s not always the best fit for every application. Just because a tool exists doesn’t mean it should be used.

In many cases, traditional RAG might be more than enough, offering simplicity and efficiency for straightforward tasks. Unnecessary complexity — like implementing Agentic RAG where it’s not needed — can result in higher costs, longer processing times, and more maintenance.

By aligning your choice of RAG approach with the actual needs of your application, you ensure that the system is lean, efficient, and built only for what’s required. This not only saves resources but also ensures better performance and a smoother user experience.

Related work

Methodology

In this section, we detail the methodology used to implement an Agentic RAG system for Automated Context gathering based Information retrieval. Now that we have a clarity over the understanding of how agentic RAG works, this section will help you understand system architecture, data gathering, VecDB setup, tool definition, agent design, and generated results used to compare the quality of output generated for similar user query.

Experiments

Results

Discussion

Conclusion

References

Acknowledgements

Appendix