Agentic RAG: What It Is, Its Types, Applications, And Implementation

Have you ever felt like the answer you’re getting from an AI tool is “just okay,” or worse, “not good enough”?

Enter Agentic RAG, a relatively new technique that provides businesses with context-aware, intelligent, and dependable AI by combining agent-like reasoning with retrieval-augmented generation. Although the idea is not yet widely accepted, it has enormous commercial potential.

In this article, we discuss what agentic RAG is, the types of agentic retrieval techniques it encompasses, the characteristics of an agentic RAG infrastructure, various types of agentic RAGs, their business applications, and how to implement them. 

Let’s get into it.

What Is Agentic RAG?

First, RAG stands for Retrieval-Augmented Generation. As such, Agentic RAG refers to the use of AI agents to facilitate retrieval-augmented generation. 

Still don’t get it?

Let’s start with the foundation being RAG. 

RAG is an AI application that connects large language models to external knowledge sources. 

Think of it like this: a simple generative AI model is initially developed to generate content based on a user’s query. However, for this model to do this, it has initially been trained on some set of training data. Normally, the output of this generative AI model is often largely based on the data it was trained on. 

The issue with this approach is that it will inevitably become obsolete at some point, as the data on which it is trained will become outdated. 

That’s where (traditional) RAG comes into place. 

By combining a large language model (LLM), i.e., the generative AI, with an external knowledge source (that is constantly updated), the RAG ensures that the user queries are augmented with more context so that the LLM can generate more accurate and context-rich responses. This has made RAG a go-to solution for enterprises that need domain-specific, up-to-date insights rather than generic AI responses.

Now, let’s relate it to Agentic RAGs. 

Clearly, a traditional RAG enhances the quality of a generative AI’s responses by retrieving more recent information. But it lacks the ability to reason through complex tasks, validate whether retrieved content is trustworthy, or adapt its approach when an initial query doesn’t deliver the right result.

Enter agentic RAGs: this type of AI is intelligent enough to determine and execute a course of action independently. More specifically, Agentic RAGs plans, reason, validates, and adapt across multiple steps of the information retrieval process. 

Think of it this way:

• Traditional RAG is like a search assistant that retrieves information and provides a summary.
• Agentic RAG is like a research analyst who not only finds information but also cross-checks sources, fills in missing context, and presents conclusions in a way that aligns with your objectives.

Capisce? 

Types of Agentic RAG Systems

Within an Agentic RAG system, there can be one or more types of AI agents working together to add a layer of intelligence, autonomy, and adaptability to the process. Essentially, various types of AI agents collaborate within the RAG pipeline to ensure the retrieval of smart and accurate information.

The following are some of the most relevant types of Agentic RAG systems. 

1. Routing Agents

Routing agents are AI agents that specialize in determining which external knowledge sources and tools to use in response to a user query. Essentially, they determine which information sources a user query should be directed to. 

As such, instead of sending every user query to all external knowledge sources, they assess, classify, and route them to the appropriate data sources. For instance, a routing agent can direct a customer’s “account balance” question to a transactional database, while routing a “compliance regulation” question to a legal document retrieval system.

2. Query Planning Agents 

Query planning agents are AI agents responsible for processing complex user queries, breaking them down into step-by-step processes, and then orchestrating the retrieval of relevant information for each part of the query before curating a coherent answer. They act like the task managers of the RAG pipeline and are often used as a crucial part of AI orchestration. 

3. Tool-use Agents 

As their name implies, tool-use agents connect the LLM to other tools that can be used to generate relevant and accurate answers to a user’s query. As such, they function beyond text retrieval and help call APIs, run calculators, and even connect with enterprise systems, such as CRM and ERP systems, to enrich the responses to a user query.

4. ReAct Agents (Reason + Act Agents) 

ReAct agents are AI agents specifically designed for handling ambiguous or evolving user queries. They are able to handle these queries through an agent framework that creates multi-agent systems, which then generate and execute step-by-step solutions or instructions. 

For example, a ReAct agent can first generate a reasoning step, then follow it up by taking an action specified by that reasoning step, evaluating the result, and finally repeating the entire cycle again until the user’s query is accurately responded to. If necessary, they can also identify suitable tools that can assist in that context. This loop makes them resilient in handling ambiguous or evolving queries.

5. Plan-and-Execute Agents 

You can think of plan-and-execute agents as cousins of ReAct agents, in that they also execute multiple workflows without needing to call back to the primary agent. However, unlike ReAct agents, which somewhat combine reasoning and action, they separate these vital processes. First, they create a high-level plan for solving the query. Next, they execute the query systematically according to the high-level plan they developed earlier. 

For example, let’s say we’re using a plan-and-execute agent for financial due diligence. Such an agent will first design a plan: (1) fetch recent annual reports, (2) pull regulatory filings, (3) cross-verify risk disclosures, and (4) summarize. It then carries out each step.

6. One-shot Query Planning Agents 

One-shot query planning agents tap information from RAG pipelines by breaking down complex queries into a series of sub-queries that access smaller information nuggets across these different RAG pipelines. After gathering all the useful information, the one-shot query planning agent then aggregates the details into a comprehensive single response. 

As you have seen, these different types of RAG agents serve different purposes. As such, the one used depends on the specific business priority you aim to tackle. 

For example, routing and query planning agents are well-suited for simple tasks, while ReAct and plan-and-execute agents are more capable of handling more mission-critical use cases. Used together, they can serve as strategic levers for building AI systems that scale with the demands of enterprises. Organizations ready to hire AI developers can accelerate the adoption of these advanced agentic RAG solutions.

Related Read: Agentic AI vs. Generative AI: Key Differences

Business Applications of Agentic RAGs

So, what are the actual business problems that agentic RAGs are capable of solving? 

While we can clearly see that these agentic RAGs represent a massive technical upgrade over traditional RAGs, they also open doors to enterprise-ready AI solutions. 

The following are some of the most impactful business applications of agentic RAGs across different industries:

1. Enterprise Data Management 

Enterprises can utilize agentic RAGs to locate information more rapidly and accurately within their proprietary data sources. This is a wonderful application because most enterprises have critical data spread across various enterprise applications, including CRM systems, ERP systems, cloud platforms, and legacy systems. 

And while traditional RAGs can only extract the required data from one knowledge base at a time, agentic RAGs can simultaneously branch out into multiple sources and extract data more accurately. As a result, enterprises can reduce data silos, obtain AI-driven insights quickly, and make informed decisions based on consolidated and trusted data, rather than relying on scattered reports. 

2. Knowledge Management 

Apart from retrieving insightful data from these data sources, agentic RAGs also help enterprises to summarize, validate, and connect insights across different sources. You see, these agentic RAGs act like digital knowledge analysts, gathering not only policies, reports, internal wikis, compliance manuals, and research archives, but also extracting the useful insights that decision makers need from them. As a result, they provide faster access to institutional knowledge, reduced productivity tools, and fewer compliance tools.  

3. Real-time Question-answering, Engagement, and Customer Support

Agentic RAGs can also be utilized as components of customer-facing chatbots and FAQs, providing employees and customers with access to current and accurate information. As such, it helps enterprises to streamline customer support services by handling simpler customer support inquiries. 

They are able to do this by breaking down a customer’s issue into multiple steps, such as pulling product documentation (from multiple knowledge bases and data sources), checking recent tickets, and validating warranty terms before responding. Consequently, the customer service team is better placed to provide accurate, conversational responses that feel personalized. 

4. Financial Services and Risk Management 

The ability of agentic RAGs to retrieve and interpret vast amounts of data can also be very valuable for financial institutions. The fact that these institutions operate in one of the most information-dense and highly regulated environments means that they must conduct extensive due diligence across their day-to-day activities. And that’s exactly where agentic RAGs excel: retrieving and interpreting vast amounts of data. 

As such, financial institutions can utilize agentic RAGs to compile annual reports, analyze risk disclosures, and cross-check risk regulatory filings across various markets and geographical locations. In addition, they can also validate suspicious transactions against multiple sources of evidence (data sources) before flagging them, thereby improving the enterprise’s fraud detection practices. 

5. Productivity and Engineering Assistants 

Agentic RAGs can also serve as crucial components of software engineering assistants. The ability of agentic RAGs to break down complex user queries and create a step-by-step process for providing accurate and relevant responses to these queries makes them ideal for this purpose. While most software engineering teams are already using traditional co-pilots for this purpose, agentic RAGs edge them in that they are capable of multi-step reasoning and data validation. 

For instance, an agentic RAG-powered software engineering assistant is capable of breaking down an engineer’s query into sub-tasks such as retrieving relevant documentation, checking their code against code repositories, and validating compatibility before suggesting a solution. As a result, they improve a developer’s velocity, facilitate strategic planning, and reduce downtime. 

How to Implement Agentic RAGs for Real-world Business Use Cases

So are you sold on the idea of using agentic RAGs for your AI solutions? 

Let’s take a look at the steps involved in building an RAG system. When building these systems, a high-quality AI Agent Development Company like Debut Infotech starts by:

1. Clearly Define High-Value Use Cases and Objectives

We’ve already highlighted some common use cases above, including chatbots, data gathering, and software engineering assistants. The first step towards implementing an agentic RAG for real-world business use cases is to pick one of these and clearly set a specific goal for each use case. This could improve chatbot response accuracy, reduce software development time, or enhance data analysis accuracy. Find one and work towards it systematically. 

2. Choose Components

To handle the retrieval and response generation accuracy and speed for these agentic RAG systems, you need to select the appropriate retrieval system for your chosen use case or objective. Common examples include BM25 and Dense Passage Retrieval. 

3. Audit and Prepare Data 

As with the implementation of all other AI systems, poor data quality equals poor AI system outputs. Therefore, start gathering relevant documents, clean them, and preprocess them for compatibility with your retrieval systems. This process involves connecting your AI components to your preferred CRM, ERP, internal knowledge base, and APIs. Likewise, you also have to clean up redundancies, outdated policies, and incomplete records.

4. Design the Agentic Pipeline

This is where you actually create the agentic RAG’s core infrastructure. 

Remember the different types of agentic RAGs we discussed earlier? This is where you start piecing them together based on how you envision your whole agentic RAG system will work. 

5. Integrate Retrieval and Generation with Enterprise Workflows

With the agentic rag architecture now in place, you need to create an agentic pipeline where the retrieval component actually fetches documents and the generative model also produces actual responses based on the user’s query. The goal here is to ensure the RAG feels like a natural extension of the business process, rather than being another siloed tool. 

6. Fine-tune

After getting the initial outputs, you need to continuously evaluate for accuracy, relevance, and coherence. Doing this helps to sharpen the model’s responses and accuracy. 

7. Establish Governance and Compliance 

Executives care about trust, auditability, and compliance. Therefore, before deploying any agentic RAG system to the live environment, you must have set appropriate guardrails to prevent data leaks, built in explainability (to explain why certain responses are given), and aligned the system’s practices with industry-specific regulations. 

Conclusion 

If you’ve been curious about the next step in enterprise AI solutions development, then this is it right here. By moving beyond mere static data retrieval towards dynamic, reason-driven pipelines, agentic RAGs are redefining how AI systems interact with data and respond to user queries. We have seen this with the various types of AI agents (routing, query planners, and tool users) tackling different real-world challenges across various industries. 

Now, the question for you, as a progressive business leader, is how to integrate this into your organizational processes. For maximum results, you need to:

• Identify real-world problems and use cases within your business where AI reasoning can add significant ROI
• Prepare and integrate your enterprise data for accuracy and compliance
• Finally, design agentic RAG pipelines that integrate seamlessly with your workflows

And if you need some help with all these, you’re in luck because that’s exactly what we do at Debut Infotech Pvt Ltd. At Debut Infotech, our AI development services provide the technical expertise and enterprise context necessary to transform these steps into a production-ready system. Partner with us to build Agentic RAG solutions that unlock smarter, faster, and more trustworthy decision-making.

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