What is Agentic RAG? A Practical Guide for Data Teams

Imagine a data team trying to make sense of thousands of unorganized documents, trying to find clear answers with the help of AI. It can be a challenge on the best of days. These tools use large language models (LLMs) to process documents like contracts, reports, and manuals, but they sometimes hallucinate,producing inaccurate or fabricated information, which makes them less than ideal for critical enterprise tasks.

Traditional retrieval-augmented generation (RAG) helps solve this by grounding AI responses to trustworthy data sources so their answers are more accurate and backed up by citations. However, it starts to fall short when queries become more complex and require several steps of reasoning to find an answer.

This is where agentic RAG emerges as an advanced solution. It pairs retrieval and generation with agents that reason, plan, and act.

In this article, we'll provide a detailed guide on agentic RAG, explaining what it is, how it works, and how it differs from traditional RAG. We'll also cover how Domo's platform helps build the trustworthy data foundation necessary for agentic AI.

But first, we need to look more closely at why traditional RAG may not always enough.

Why traditional RAG isn't enough

To understand agentic RAG, we have to first to examine the problem that traditional RAG was built to solve and where it falls short.

LLMs operate on parametric knowledge, which means they use information that was built into their parameters during training. However, this "knowledge" is fixed and can quickly become outdated. Additionally, when LLMs don't know the answer to a question, they tend to generate flawed yet convincing responses. In other words, they hallucinate.

Types of hallucinations occurring in LLM responses | Source

Traditional RAG addresses this by linking the language model to trustworthy external information sources. Here's the typical workflow of the RAG:

1. Indexing: Documents from a knowledge base are broken into small pieces (chunks), converted into embeddings (numerical forms), and stored in a vector database. ‍
2. Retrieval: When a user asks a question, the system converts the question into an embedding and searches the vector database to find the most relevant pieces of text. ‍
3. Augmentation and generation: The retrieved text pieces are combined with the original user prompt and fed to the language model. The model then uses this combined information to produce an accurate, fact-based answer.

Illustration of a typical RAG setup | Source

This linear process works well for easier questions but starts to break down when faced with more complex challenges. Traditional RAG often exhibits limitations in several key areas:

• Struggles with multi-step queries
  A query like, "Compare the termination clauses in our 2023 contracts with our 2024 contracts, and summarize the key differences in liability," requires more than a single retrieval step. A traditional RAG system might only retrieve information for the first part of the query, leading to an incomplete answer. ‍
• Linear and inflexible process
  The retrieve-then-generate process is static. If the initial retrieval is weak or incomplete, the system can't adapt. It can't recognize that the retrieved context is insufficient and doesn't attempt a different approach. The quality of the final output is entirely dependent on that first retrieval step. ‍
• Lack of tool use
  Traditional RAG is limited to the knowledge base it was connected to. It can't interact with other systems, such as querying a SQL database, searching the web for real-time information, or accessing a customer relationship management (CRM) system. This limits the scope and richness of the answers it can provide.

But Agentic RAG was designed with these issues in mind and seeks ways to address them. Let's explore what makes it different.

What is Agentic RAG?

Agentic RAG is an advanced AI system that introduces AI agents into the RAG workflow to control the language model's retrieval and response generation.

Instead of a fixed process, the agent gets to choose which action to take next, such as accessing external data or creating customized outputs. RAG agents even self-correct their approach based on the information they find.

The diagram above is a blueprint of an agentic RAG system. | Source

What makes agentic RAG different?

• Reasoning and planning
  Agentic RAG puts intelligent AI agents between your query and the final response. When it receives a complex query, it doesn't immediately fetch documents. Instead, agents actively think through problems, break the query down into a row of logical sub-tasks, and decide what info to retrieve and validate before presenting. For example, it may first find all relevant contracts from the database, next look for specific sections in each, and finally compare those sections. ‍
• Adaptation and multi-step processing
  The adaptation of agentic RAG is the most powerful capability compared to the one-shot method of traditional RAG. If a retrieval step yields poor or insufficient information, RAG AI agents can reformulate queries, retry with a different approach, seek out more sources, or request clarification. This cycle of thinking, acting, and observing helps it manage uncertainty and fix mistakes early on. ‍
• Tool use
  RAG agent systems aren't limited to a single vector database. They can interact with various external tools, including web searches, API access, and running code. This lets the agent collect information from multiple sources to build a thorough response.

Guiding Principles of Agentic RAG

Types of RAG agents

An agentic RAG system usually consists of a coordinated team of specialized agents rather than a single agent. Common archetypes include:

Query analysis agent

The query agent is the first contact, analyzing user prompts to clarify ambiguity and identify core intent. It then decides whether the query is simple enough for traditional RAG or complex enough to require a multi-step plan.

Routing agent (or dispatcher agent)

This agent acts as a traffic controller based on the query analysis. It chooses which tool or sub-agent is best suited for the next step. Should it perform a vector search? Use a web search API for the latest information? The router makes that strategic decision.

Reflection Agents

These agents add a layer of quality control. Using techniques like chain-of-thought reasoning, they review the retrieved information or generated answers for quality, coherence, and completeness. Suppose they find missing context or a low-confidence result. In that case, they can prompt the system to revisit earlier steps, perform additional searches, and fix its own work before the final output.

How does agentic RAG work?

While implementations can differ, the workflow of an agentic RAG system follows a cyclical, intelligent process that sharply contrasts with the linear path of traditional RAG.

1. Query reception and planning: The primary AI agent, often supported by a query analysis agent, receives the input, assesses its complexity, and begins planning. ‍
2. Contextual understanding: The agent uses memory mechanisms, including short-term and long-term memory, to understand the conversation's context and past interactions. It ensures responses build on prior data, improving accuracy in tasks like intelligent document processing, where context matters. ‍
3. Tool selection and retrieval strategy: The agent formulates a retrieval strategy for each step in its plan. Next, it selects the most appropriate tool for the job, be it a vector database, a document API, or a web search engine. ‍
4. Iterative retrieval and action: The agents execute the plan, retrieving relevant data from diverse sources. It oversees the output of each action and iterates as needed to gather more relevant data chunks. ‍
5. Synthesis and generation: Once the agent is confident it has gathered all the relevant information, it combines the retrieved context with the system and query prompts. ‍
6. Final reasoned response: The agent then passes this complete info to the LLM, which generates a final, reasoned response for the user.

Now, let's explore some of the key agentic RAG architectures.

Types of agentic RAG architecture

Agentic RAG brings autonomy to the RAG pipeline by using AI agents to improve contextual understanding and adapt to complex tasks. These systems vary in architecture based on complexity and design principles.

Here are the different types of agentic RAG systems:

Single-agent agentic RAG: Router

A single-agent RAG architecture features a centralized decision system where a single agent handles finding, directing, and putting together information. It's best for setups with a few tools or data sources and with well-defined tasks.

• Use case: A single-agent RAG is mainly used in customer support, where it can retrieve tracking details from a database and get real-time updates from a shipping API to provide delivery status.

An overview of single agentic RAG | Source

Multi-agent agentic RAG systems

Multi-agent RAG is a modular and scalable design for managing complex tasks at the same time with different query types by using multiple specialized agents. Instead of relying on a single agent, work is divided among several agents, each designed for specific roles or data sources.

• Use case: It functions as a research assistant, where multiple agents gather and compile information from diverse domains, including data from academic papers, news updates, economic statistics, and expert commentary to provide comprehensive responses.

An overview of multi-agent agentic RAG systems | Source

Hierarchical agentic RAG systems

Hierarchical systems employ a multi-tiered approach where they organize agents in different levels. Higher-level agents supervise and guide lower-level ones. This enables decision-making at multiple levels for more efficient query handling.

• Use case: A financial analysis system, where a master agent gets a question and assesses its difficulty, then decides which other agents or sources to use first. The lower-level agents then do specific searches, and their results are combined and summarized by the master agent before being sent back to the user.

An illustration of hierarchical agentic RAG | Source

Adaptive agentic RAG

An adaptive agentic system automatically changes query handling strategies based on the complexity of the incoming query. It uses a classifier (a smaller language model) to judge the complexity of the question and then chooses the best way to answer.

• Use case: A customer support assistant asks, "Why is my package delayed, and what alternatives do I have?" The classifier identifies it as a complex query, triggering a multi-step retrieval process, which the LLM then synthesizes into a detailed response.

An overview of Adaptive agentic RAG | Source

Once we understand how agentic RAG works and its different architecture, we can explore the benefits it brings to enterprises.

What are the benefits of agentic RAG for enterprises?

The shift from a static retrieval process to a dynamic, reasoning-driven one brings significant benefits for enterprises:

• Smarter decision-making: Combining information from many sources and handling complicated questions helps the system give more detailed insights and lead to informed business decisions. ‍
• Greater automation: Agentic systems can automate entire workflows that previously required human intervention, such as compliance checks, risk analysis, and detailed report generation. In fact, agentic RAG automation has been shown to reduce manual research time by 63 percent. ‍
• Improved accuracy and depth: The system can correct itself and improve over time, which makes it less likely to give wrong or incomplete answers. It can also dig deeper to find the right information, even if it's not obvious. For example, agentic RAG can cut error and hallucination rates to under 10 percent compared to over 20 percent in traditional baseline systems. ‍
• Scalability across functions: The agentic framework can be applied to various business functions, like legal work and sales, to handle large volumes of data and complex queries efficiently. ‍
• Improved customer experiences: Agentic RAG in customer service chatbots and virtual assistants can understand complex user needs and provide accurate and personalized solutions.

Agentic RAG vs traditional RAG

Agentic RAG differs from traditional RAG by having an AI agent control a multi-stage process, while traditional RAG retrieves information and generates responses in a single step. Here are the key differences between these approaches:

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How is agentic RAG reshaping the field of document processing and knowledge management? Let's take a look at some real-world use cases where agentic RAG is producing transformative results.

Agentic RAG use cases and applications

Agentic RAG is suitable in scenarios where large volumes of complex documents need to be processed, understood, and acted upon.

Intelligent document processing (IDP)

Many businesses handle documents like invoices, research papers, and regulatory forms. Agentic RAG can help manage these documents by automatically summarizing, sorting, and labeling them at scale.

Example: A financial firm can use an agentic RAG system for credit risk analysis. The system start by finding the company's latest economic reports from a private database. Next, it would use an internet search to find recent news and analysis about the company. It might then check a database for any regulatory issues. The system would then combine all that information into a detailed risk assessment report.

Internal knowledge management

Many companies struggle with "knowledge silos," where information is stored in various locations, such as HR systems, project tools, and shared files, so it's difficult to access for people in different parts of an organization. Agentic RAG can help by building a searchable knowledge layer on top of these disparate sources, integrating them into a single resource.

Example: An agentic RAG system can create a unified and searchable knowledge base within a data environment like the Domo platform. It can continuously ingest and organize internal documents from Confluence, SharePoint, and shared drives. An employee might ask, "What was our marketing strategy for Product X in Europe last year, and how did it perform compared to the US launch?" The agent would be able to retrieve strategy documents, pull performance data from a BI dashboard, and create a brief summary.

Customer support

Agentic RAG helps build next-generation customer support bots that go far beyond simply fetching information and can adapt their responses to the specific context of a customer's issue.

The road ahead: Trends and future of agentic RAG

Agentic RAG is set to become a core technology in enterprise AI, with both industry and academia signaling a great shift in retrieval-augmented systems development.

RAG trends to watch

Multimodal capabilities

Future RAG systems will handle different kinds of data, such as text, pictures, sound, and videos. This will help them give more detailed and context-aware answers.

Multi-agent collaboration and proactive systems

Future agentic RAG will feature teams of specialized RAG AI agents collaborating on complex tasks. They will also be able to foresee what is needed and take action without being told.

Hybrid architectures and integration with advanced models

Emerging systems will combine vector databases, knowledge graphs, and rule-based reasoning with large action models (LAMs) to improve decision-making. Deploying these systems on edge devices enhances privacy and performance. Moreover, making them open-source increases accessibility and safety of intelligent retrieval (agentic RAG).

An overview of GeAR: Graph-enhanced agent for retrieval-augmented generation | Source

Domo helps you prepare your data ecosystem for agentic AI

The success of any AI system, including agentic RAG, depends on the quality, accessibility, and governance of the underlying data. A strong reasoning engine can't be built on a swamp of disorganized and poorly managed data. Having a powerful data platform becomes crucial for AI readiness.

With Domo, you getthe foundational layers necessary to prepare your data ecosystem for agentic AI and make it easier to test, scale, and govern these advanced workflows. Here's what Domo offers:

• Governed data access: You can bring all your data together in one place using over 1,000 pre-built connectors. Domo's strong governance and access controls mean that your agents are operating on trusted and quality data. ‍
• Integration layers: Your agents need a way to connect with different systems and tools. Domo's many integrations and APIs give agents the ability to access databases, initiate actions in other apps, and get the information they need to do their jobs. Our scalable data pipeline tools like Magic ETL, SQL DataFlows, Adrenaline (live cache), and Integration Studio keep your data clean, current, and ready to use. ‍
• Bring Your Own Model (BYOM): With Domo's BYOM feature, you can choose the best LLMs for your needs, whether it's DomoGPT, models from other major providers like OpenAI and Anthropic, or specialized, customized models. You're free to choose and update your AI approach as new models become available. ‍
• Agent-building capabilities: Our agent-building tools, Agent Catalyst, help you structure the routing, retrieval, and reasoning logic of your agents on top of your existing Domo data assets. This makes it easier and faster to develop, test, and expand your intelligent agents without starting from scratch.

Ready to learn more? For a deeper dive into the fundamentals, read our complete guide to Traditional RAG. And if you're interested in building a solid data foundation for agentic AI, check out our AI Readiness Guide.

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