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Apr 21, 2025

10 minutes read

Agentic RAG: A practical guide for enterprises

Learn how to implement agentic RAG in real-world enterprise workflows. This guide covers everything you need to know about RAG agents and their uses.

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Businesses are built on insights—but those have been, to date, curated over lengthy time periods or been very resource-intensive to develop. In fact, one of the drivers of enterprise AI adoption has been the ability to refocus workers on the bigger picture items of strategy setting and creative thinking. And the advent of agentic RAG is adding even more weight to the argument for enterprise AI.

The amount of work hours dedicated to focus groups, testing, analysis, and more can quickly add up, meaning your best people are spending time in the data weeds instead of taking action on those insights. With agentic RAG—that is, retrieval-augmented generation (RAG) combined with autonomous AI decision-making—businesses are empowered to automate complex knowledge tasks to help deal with information overload.

But can agentic RAG really help to improve productivity, speed-up decision making, and create real competitive advantages? Let’s take a look into the possibilities.

##### What is agentic RAG?

Agentic RAG takes standard RAG systems and adds a layer of intelligence that can analyze a request, then figure out what information to look for (potentially across multiple databases), how to understand it, and what to do with it.

The meaning of agentic RAG goes beyond simple question-answering. It has the ability to handle complex tasks that require the model to use reasoning and gather information from multiple places. Unlike regular RAG—which retrieves information to answer questions directly—agentic RAG systems can break down larger and more complex tasks, decide what to search for, and act independently, taking multiple steps to complete a goal without constant user prompting.

###### Components of agentic RAG systems

Where traditional RAG systems are based around retrieval and generation, agentic RAG systems have three key parts working together:

• Retrieval: The RAG agent searches your company's attached knowledge bases the way an expert researcher would, but faster. Unlike regular search tools, the agent decides when to search and which sources matter most for your specific question, just like a skilled librarian who knows exactly which reference books to pull.
• Generation: Using large language models, the system creates (generates) responses based on the information it has retrieved. It can combine data from multiple sources to give you complete, context-rich answers.
• Autonomy: The system makes its own decisions about when to look for information, what actions to take, and how to handle multi-step tasks. It's like having an assistant who doesn't need step-by-step instructions for every task—they understand the goal and figure out the best way to get there on their own. This doesn’t mean a human is not involved; it just means the model does not need to be directed for each component of its search.

###### How agentic AI works

The agentic RAG workflow starts when someone submits a request, typically in the form of a natural language query or task description. Here's what happens next:

1. The agent looks at the request and decides if it needs more information to give a good answer. 2. It searches for relevant data from the databases it’s attached to—things like market reports, industry blogs, and financial news sites as well as the internal company knowledge base—focusing on sources it knows (from experience or programming) are reliable for this type of question. 3. Based on its findings and understanding of the task, the agent might use tools or APIs to take action, process data, or generate insights. 4. The agent keeps refining its approach, possibly making multiple searches and taking several actions before giving its final answer. 5. Throughout this process, it tracks where it found each piece of information, allowing you to see the sources behind its conclusions.

This agentic RAG pipeline enables agentic AI to handle research tasks in minutes that might have taken you hours to complete on your own—and it consistently grounds and cites its answers in reliable and verifiable sources so you can follow the information trail to check accuracy.

##### What's the difference between agentic RAG and traditional RAG?

Traditional RAG is akin to having a research assistant who can find information and summarize it, but needs you to break down complex questions and guide them through research that requires multiple steps or sources. Agentic RAG is more like having an experienced analyst who can take your high-level question and run with it.

Agentic RAG takes things further by:

• Planning its own research. While traditional RAG responds to direct questions, agentic systems can break down a complex question like "How should we enter the Southeast Asian market?" into specific research areas and create a step-by-step plan to investigate each one before giving its answer.
• Using multi-step reasoning. Traditional RAG gives information in one go, but agentic systems can make multiple searches, building on what they find through several rounds of thinking. The user can witness the process first-hand: many agentic RAG pipelines are built to log intermediate steps.
• Using tools. Many organizations will have multiple sets of documents that could potentially answer a question but that may feature conflicting information, or that will require the model to keep searching other databases to get the most up to date, accurate information. While traditional RAG typically provides helpful text responses by pulling relevant data from documents, any multi-step work needs to be pre-defined. Agentic RAG goes further:...