Generative AI Series

Hands on — Agentic RAG (1/3)

In this blog, I compare traditional RAG with Agentic RAG and do a quick tutorial on how to build a simple Agentic RAG application with basic Python libraries.

Courtesy: ChatGPT

I had blogged about RAG ages back (in GenAI world, days are years :-D, Things are changing so fast that by the time we learn a new technology, it’s old by the time we understand it.:-D ). Thankfully RAG is one of the patterns that is only getting better, as LLMs are becoming more mature, and Agentic AI is taking over.

Here are some of my blogs on RAG

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation(RAG) — Chatbot for documents with LlamaIndex

Retrieval Augmented Generation(RAG) — Chatbot for database with LlamaIndex (Text2SQL)

Retrieval Augmented Generation(RAG) — Chatbot for Wikipedia with LlamaIndex

Retrieval Augmented Generation(RAG) — Chatbot for Youtube with LlamaIndex

Agentic AI and Agentic RAG Explained

I am sure that you have heard of Agentic AI. Instead of simply generating predictions or responses like a language model, an agentic AI acts independently to accomplish the user-defined goals. By combining perception, planning, decision-making, and execution, an agentic AI can orchestrate multiple tasks, such as querying APIs, scheduling actions, or adapting its own workflow, without constant human oversight. This ability to set objectives, monitor progress, and take corrective steps makes agentic AI particularly powerful for complex, multi-step processes.

As LLMs become increasingly intelligent with reasoning capabilities, agentic AI, equipped with powerful reasoning models, can perform most tasks autonomously.

Some of the key features of Agentic ai systems is

• Goal-oriented behavior
• Decision-making autonomy
• Learning and adaptability
• Dynamic problem-solving

Agentic RAG is an advanced evolution of traditional Retrieval-Augmented Generation that empowers AI systems with autonomous decision-making capabilities for information retrieval and processing. Unlike conventional RAG systems that follow predetermined retrieval patterns, agentic RAG enables AI agents to dynamically plan their approach to answering queries by deciding when and what to retrieve, selecting from multiple data sources (sometimes external sources) and tools, and iteratively refining their searches based on initial results.

The agent can break down complex questions into sub-queries, evaluate the sufficiency of retrieved information, and even take actions beyond text generation — essentially transforming RAG from a passive information lookup system into an active, reasoning-capable assistant that adapts its strategy to each specific query’s requirements.

Let’s have a quick comparission of Agentic RAG over Traditional RAG.

Traditional RAG vs. Agentic RAG

Summary

In summary Agentic RAG excels in the following use cases

Complex, Multi-Step Tasks

• Use Case: Financial report analysis where you must index dozens of PDFs, run calculations, fetch live stock data, then synthesize a summary.
• Agentic application: A “DataFetchAgent” can call a finance API, a “CalcAgent” can run spreadsheets, and the QAAgent orchestrates these in sequence.

Tool Integration & Error Handling

• Use Case: Legal document review that needs OCR of scanned images, followed by context extraction, then citation formatting.
• Agentic application: A dedicated OCR agent first cleans and extracts text; if OCR fails, a “FallbackAgent” switches strategies — traditional RAG cannot easily adapt.

Iterative Refinement & Chaining

• Use Case: Scientific literature survey: retrieve relevant papers, summarize each, compare methodologies, then draft an outline.
• Agentic application: You can chain a “SummarizationAgent” and a “ComparisonAgent,” looping until a coverage threshold is reached. Traditional RAG is flat.

Dynamic Tool Discovery

• Use Case: Software development assistant that writes code, runs tests, debugs, and commits to a repo.
• Agentic application: CodeAgent → TestAgent → DebugAgent → GitAgent pipeline; each agent invokes the next based on test results. With MCP providing the standards to access and interact with tools, there is a host of MCP servers, that powers the agentic RAG to pretty much integrate with any source or knowledge base, and also act upon.

In summary, traditional RAG is great for straightforward QA over static text. Agentic RAG shines when you need autonomous decision-making, multi-tool workflows, and iterative or conditional logic.

Agentic RAG significantly advances traditional RAG by incorporating autonomous decision-making, modular workflows, and dynamic adaptability. While traditional RAG follows a static, linear process of retrieving information and generating responses, Agentic RAG employs multiple specialized agents for tasks like data retrieval, analysis, and decision-making, enabling real-time adjustments, error handling, and continuous learning. This architecture supports complex, multi-step tasks, integrates diverse tools, and allows iterative refinement, making it ideal for applications such as financial analysis, legal document review, scientific research, and software development. Agentic RAG excels in environments requiring flexibility, dynamic problem-solving, and enhanced interactivity.

In part 2, we will be building a simple Agentic RAG system to understand the real power of Agentic RAG..in the meantime, please leave your comments.