What is Agentic AI? A Practical Guide

01

Introduction to agentic RAG

Artificial intelligence (AI) has made huge steps forward, especially in creating text that sounds like it was written by humans, thanks to powerful tools called Large Language Models (LLMs). LLMs are used in many ways, from powering a customer service chatbot to creating content.

A great enhancement to LLMs is Retrieval-Augmented Generation (RAG). RAG makes the answers the LLMs generate more accurate and secure by checking reviewing additional relevant data from external sources before giving an answer. Usually, RAG works by finding information related to a user's question, adding this information to the question, and then having the LLM generate a better answer. However, sometimes questions are complicated and require information from many different sources. This complexity creates a need for smarter and more independent systems, which is where Agentic RAG comes in.

Agentic RAG is the next step in the development of RAG. It uses AI agents to fetch data and answer questions in a more sopisticated way than traditional RAG. The main improvement is that AI agents can plan, think, and act on their own to qualify answers.

The move from a conventional RAG architecture to an agentic one is significant. Traditional RAG works in a straightforward way – finding information once, and then giving an answer based on that information. Because real-world questions are usually more complex, data must be accessed, collected, and processed from multiple sources, multiple times. The fixed nature of traditional RAG makes it difficult to answer complicated questions effectively. By leveraging independent LLM agents, agentic RAG enables the generative AI (GenAI) model to understand the intent of a question or assignment, moving beyond data gathering to problem solving.

This article will explain what agentic and RAG mean. It will also discuss the need for agentic RAG as well as its structure, the role that functions play, the different types of agents used, the challenges it faces, and its benefits. Finally, it will show how an innovative GenAI Data Fusion solution can help organizations realize the full potential of agentic RAG.

02

Understanding agentic AI

The term agentic AI refers to AI systems that can act independently to achieve specific goals with little human involvement. These advanced AI systems are built on powerful LLMs and sophisticated thinking abilities. Several key features define agentic AI systems and set them apart from more traditional AI.

Autonomy and goal orientation

Autonomy is a primary feature, allowing agentic AI systems to perform tasks on their own without a human in the loop. This indpendence empowers agentic systems to proactively manage long-term goals and complete multi-step tasks – a serious departure from the reactive nature of traditional AI models that usually work on a question-and-answer basis. Agentic AI can start and finish entire projects based on a high-level goal, independently managing the sequence of decisions and actions.

Adaptability and the ability to learn

Adaptability is another crucial feature, enabling agentic AI to learn from interactions, receive feedback, and change its decisions based on past experience and changing conditions. This ability to learn allows agentic systems to improve their performance over time without reprogramming.

Reasoning and decision-making

The final feature, chain-of-thought prompting is also key, allowing for sophisticated decision-making based on context and trade-offs. Agentic AI can also analyze complex situations, evaluate different options, and make informed decisions based on context, logic, and potential consequences.

AI agents, the building blocks of agentic AI systems, basically imitate how people make decisions. LLM-powered autonomous agents include:

Large language models, for understanding and creating natural language.
Memory, both short-term and long-term, for remembering context and past experiences.
Planning abilities, for strategizing and prioritizing actions.
Access to tools and functions, for interacting with their environment.

In summary, traditional AI models operate within set limits and often rely on humans to undertake tasks beyond their capabilities. The agentic aspect fundamentally changes AI from a passive tool, that follows specific commands, to an active, decision-making entity – enabling it to begin a task, make independent decisions, and pursue goals without external direction.

03

Understanding Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a generative AI (GenAI) framework that combines the strengths of traditional information retrieval systems with the advanced abilities of generative LLMs. This powerful combination results in more accurate and relevant answers by basing them on both internal (company) and external (Internet) knowledge.

Typical AI RAG tools consist of several core components that all work together:

Retrieval Model

The Retrieval Model is responsible for locating relevant information from your company’s Internal Sources in response to a user query.
Internal Sources

Your Internal Sources (e.g., enterprise systems and knowledge bases) store the data that the LLM needs to access.
Generation Model

Finally, the Generation Model, usually an LLM, uses the retrieved information to generate a more comprehensive prompt, enriched with context.

Retrieval-Augmented Generation.png A typical enterprise RAG system follows a series of steps to respond to a user query, as illustrated above and described below:

User query

The user asks a question, which lets the Retrieval Model know what kind of data it should be looking for in Internal Sources.
Data retrieval

The Structured Data Retriever queries enterprise systems for tabular information, while the Unstructured Data Retriever queries knowledge bases for docs and pdfs.
Prompt engineering

Based on the data and docs received from Internal Sources, the Retrieval Model adds context to the user’s original prompt and then forwards it, as input, to the Generation Model (LLM).
LLM response

The LLM internalizes the Prompt with Contextual to generate a more accurate and relevant Response, which is then delivered to the user.

RAG improves the abilities of LLMs by:

Providing access to fresh data, overcoming the limitations of LLMs trained on stale data.
Augmenting the LLM's answers with external knowledge, improving accuracy and reducing the risk of AI hallucinations
Citing sources, allowing users to check the facts themselves.

Compared to the resource-intensive process of retraining LLMs on new data, RAG offers a more cost-effective way to keep the model's knowledge current. Finally, RAG enables LLMs to use specific knowledge that a company owns, making them more effective for business applications.

In brief, RAG effectively connects the vast general knowledge of LLMs with the dynamic information needs of today’s users and organizations. LLMs are trained on large static datasets, meaning the information at their disposal is not up-to-date. RAG addresses this challenge by allowing LLMs to find and include relevant information from your company’s internal data sources, thereby improving their usefulness for a broad range of generative AI use cases.

04

Traditional RAG challenges

Traditional RAG challenges¹ include:

1. Data quality

Dirty data
Incomplete, unstructured, or inaccurate information within retrieval sources directly affects the quality of RAG outputs. The presence of noisy data undermines the system's ability to generate reliable responses.
Old news
Using stale knowledge bases leads to responses that are not only irrelevant, but also potentially incorrect and even damaging.
Bad biases
Data quality is foundational to RAG. Biased or misinformed data sources lead to incorrect answers – especially risky in highly regulated industries like financial services and healthcare.

2. Contextual comprehension

Bad answers

RAG is only as effective as its understanding of data context. Retrieving the wrong data leads to unreiable responses that erode user trust.
Good intentions

Poor understanding of user intent leads to the retrieval of irrelevant information, resulting in unresolved user queries.

3. Technical difficulties

Structuring unstructured data

Business docs often include charts, tables, and multiple columns. Conventional document processing techniques don’t comprehend these structures easily, making accurate information extraction and retrieval problematic.
Missing metadata

Treating RAG as simply adding more data is a bad idea. The absence of metadata tagging and semantic search algorithms reduces retrieval precision and contextual relevance.

4. Moral judgement

Honest assessments

It’s not easy to evaluate RAG performance due to the complexity of the generated outputs. Subjective qualities, like coherence and factual accuracy, require nuanced evaluation methods, especially in domains like healthcare.
Bias and bigotry

Like all AI, RAG systems are vulnerable to bias if your LLM’s training data or retrieved enterprise data reflects skewed information. In 2021, MIT demonstrated how RAG models can inadvertently mirror societal prejudices present in their training data.
Compliance and ethics

There are ethical and legal concerns surrounding external data retrieval espcially in the areas of data privacy and security, as well as regulatory compliance. Careful attention must be paid to LLM guardrails and data governance policies.

At a high level, traditional RAG relies on matching content with similar meanings, which doesn’t always capture the intent of the user and the relationships within the data. It retrieves data based on a single search of a knowledge source. But answering complex questions often requires gathering and combining information from multiple datasets and chain-of-thought reasoning – capabilities that don’t exist in basic RAG systems.

Traditional RAG systems can still be plagued by generative AI hallucinations, where the generative model produces faulty information even when given relevant retrieved context.

High latency can also be of concern, because locating and retrieving large amounts of data can cause delays in response times. Additionally, traditional RAG often breaks up documents into small chunks. Filtering and processing these millions of tiny data elements can take a lot of time, affecting the conversational AI responsiveness required by many GenAI apps.

Finally, traditional RAG is not very adaptable, struggling to change retrieval strategies based on specific questions or the initial results obtained. Traditional RAG systems normally dpend on a fixed, pre-set retrieval strategy. They can't easily change their search terms, explore other data sources, or adjust their approach, based on how good the initially retrieved information is.

05

Agentic RAG to the rescue

Agentic RAG directly addresses these challenges by integrating intelligent AI agents into the retrieval process. The agentic approach enables smarter handling of questions by breaking down questions and refining searches. It also makes better decisions thanks to its reasoning capabilities.

Agentic RAG offers greater independence and flexibility in retrieval strategies because it can adjust its approach based on the context and the information retrieved. It can also access and process information from multiple and diverse data sources. By integrating external tools and LLM function calling, agentic RAG goes beyond data retrieval, by being able to calculate, learn, and reason, search the web, and use APIs as it sees fit.

Agentic RAG is highly effective during extended conversations by maintaining context and responding smoothly – thanks to:

Deeper reasoning, surpassing simple information retrieval to offer comprehensive and insightful answers.
Enterprise data access, by breaking down data silos and presenting a unified view of the business entity (e.g., a single customer).
Workflow automation, and support for business logic.
Personalization, based on user context.

Basically, what makes RAG agentic is the addition of AI agents to manage its components and execute tasks (beyond data retrieval and text generation). Agentic RAG employs two main structural patterns:

Single-agent systems

Single-agent systems make one AI agent responsible for finding the information necessary to answer a question. An LLM single action agent acts as a guide, deciding which data source or external tool to use, based on the user's question. While single-agent structures are generally simpler to set up, they can become a bottleneck when dealing with complicated tasks that require detailed reasoning and coordination with external tools.

For example, a single agent might be programmed to first understand the user's question and then determine the most appropriate retrieval process, such as:

Sifting through user manuals, for instructions on how to activate a new feature
Cross-referencing customer, invoice, and payment records, to respond to a customer query about billing

A single agent is a good first step towards realizing agentic RAG, but its ability to handle complex situations is limited by its singlular focus and limited data perspective.

Multi-agent systems

Multi-agent systems, on the other hand, have multiple AI agents working together to perform different parts of the overall task. These systems often include a coordinator agent that’s responsible for planning tasks and ensuring that information flows smoothly between the different specialized agents.

A multi-agent LLM leverages a network of agents, each specializing in a specific area, or focusing on a particular sub-task. This specialization enables more complex reasoning and better handling of multi-step questions.

For example, a multi-agent agentic RAG system might use separate agents to:

Understand the context and intent behind user queries
Search through different sources for the relevant data
Summarize data points to enrich the original user prompt
Check the generated answer for accuracy and coherence

Multi-agent structures offer improved scalability and flexibility, because it’s easy to add, subract, or change individual agents without disrupting the entire system.

The Model Context Protocol (MCP) plays a crucial role in managing the interactions and workflows of multiple agents, systems. Several frameworks, such as LangChain, LlamaIndex, and LangGraph, help in developing and deploying both single-agent and multi-agent agentic RAG structures, providing developers with the necessary means to build sophisticated GenAI apps.

The choice between single-agent and multi-agent structures ultimately depends on the specific needs of the application. For simpler GenAI apps, a single-agent system might be the best solution. But for enterprise systems, a multi-agent structure might be the better choice due to its ability to manage complexity, improve accuracy through specialization and collaboration, and scale qucikly and easily.

06

Agentic RAG components

An Agentic RAG system is made up of several key components that work together to enable intelligent information retrieval and generation, including:

1. Large language models

LLMs are brains behind agentic RAG, enabling the AI agents to understand user questions and generate coherent answers based on the data they access.

2. AI agents

AI agents use various tools and functions to retrieve the data that addresses user queries. Each agent within an LLM agent architecture has its own with specific roles and abilities.

3. Memory

Semantic caching enables AI agents to remember data from past interactions, and use it to react more effectively in the future. Memory can be short-term, for managing current conversations and tasks, or long-term, for storing relevant data and past experiences. Memory allows agentic RAG systems to maintain context across multiple steps and improve performance over time.

4. Planning

Planning mechanisms give AI agents the ability to break down complex user queries into smaller, more manageable sub-tasks and to determine the best next actions. Planning involves reasoning, self-correction, to evaluate potential strategies, and query routing, to select the most appropriate data sources.

5. Internal data sources

Enterprise systems and knowledge bases serve as essential sources of information that are accessed by the retrieval agents within the system. These can be diverse, including vector databases for semantic search, the vastness of the web for up-to-date information, various APIs for accessing specific data or functionalities, and traditional databases for structured data.

The ability to access multiple and varied knowledge sources is a key advantage of agentic RAG over traditional RAG. Real-world information is often spread across different systems and formats. Agentic RAG can use agents to intelligently query and combine information from these diverse sources.

6. Functions and tools

Functions and tools are external components that agents can use to interact with the outside world and perform specific actions that extend beyond retrieval and generation processes. More on that in the next section…

07

LLM function calling enables agentic RAG

LLM function calling is the bridge that transforms a large language model from a text generator (even one enriched with enterprise data and docs) into an active agent that can plan, decide, and interact with its environment.

Thanks to LLM function calling, agentic RAG can:

1. Become truly agentic

With agentic RAG, the entire RAG pipeline – or any part of it – is defined as a function that the LLM agent can choose to call. For example, if management wanted to know how much first-quarter revenue was related to a particular project, the LLM would generate a structured JSON output like:

JSON
{
"function_name": "retrieve_from_Salesforce_database",
"arguments": {
"query": "Q1 2025 revenues for Project Alpha",
"top_k": 3,
"filters": {
"department": "sales",
"document_type": "quarterly_report"
}
}
}

This JSON prompt is then parsed by the application, which executes the actual retrieval against the database. The results are then passed back to the LLM.

2. Orchestrate the retrieval process

Function calling allows the LLM agent to intelligently manage the RAG process itself. It can decide:

Whether or not to retrieve

Not all queries need RAG. The agent might first call a function to see if the answer is common knowledge or available via a simpler tool.
How to query

The agent can use function calling to locate tools that formulate better retrieval queries (e.g., a query expansion tool or a tool that breaks down a complex question into sub-questions).
Which database to use

If multiple RAG sources are available (e.g., customer data vs technical docs), the agent can use function calling to select the most appropriate one.

3. Make smarter decisions

After a RAG function call returns datasets, the LLM agent isn't obligated to just summarize them. It can use function calling multiple times to:

Evaluate relevance

Call a function to assess if the retrieved data is relevant and sufficient.
Optimize results

If the results aren’t optimal, the agent can decide to call the RAG function again with a different query, or call a different function altogether (e.g., a web search API).
Create structured data from unstructured sources

Call a function to parse the retrieved unstructured text into a structured format for better processing and easier comparisons.

4. Simplify multi-step workflows

Function calling is crucial for complex processes, such as:

Pre-RAG processing

The agent might first call a function to get the current date to ensure that the retrieved data is timely, or call a user profile API to personalize the RAG query.
Post-RAG actions

After RAG, the agent might use functions to call a calculation API, based on retrieved financial data; an email API, to send a summary of the findings; or a database API, to update a record based on confimed information.

5. Combine data retrieval with action

Function calling enables agentic RAG to use the information it’s retrieved to take action. It empowers the LLM to:

Deploy RAG strategically as one of many available tools.
Control and refine the data retrieval process dynamically.
Integrate retrieved data into broader reasoning and action sequences.
Interact with external systems in conjunction with its RAG capabilities.

LLM function calling turns agentic RAG into an autonomous agent that can intelligently leverage retrieval as part of a more comprehensive problem-solving or task-completion strategy.

08

Agentic RAG agent types

Agentic RAG systems use a variety of AI agents, each with specific roles and abilities designed to improve the information retrieval and generation process. These agents can be broadly categorized based on their main functions within the system:

Agent type and role	Function	Benefit
Routing agents play a crucial role in analyzing user questions and deciding which RAG process or data source would be the most useful.	Routing agents make the system more efficient by directing questions to the most appropriate resources, thus avoiding unnecessary processing.	By understanding the intent behind a user's question, a routing agent can select the best process, ensuring that the accurate, secure results.
Query planning agents break down complex user queries into smaller sub-questions that can be processed simultaneously or consecutively.	Query planning agents allow agentic RAG to tackle tough tasks requiring multi-step reasoning and iterative information retrieval.	By finding and combining data relevant to each sub-task, these agents create a complete answer that addresses all aspects of the original question.
Tool use agents leverage external tools and APIs to collect additional data or take specific actions that go beyond what standard retrieval methods can do.	Tool use agents enable agentic RAG to interact with the real world and access dynamic data unavailable in static knowledge sources.	By integrating with external tools, these agents can check the weather, book appointments, or access real-time financial data, for maximum versatility
ReAct (Reasoning and Action) agents combine thought with action – using tools or finding data – in a repeating process to solve complex user questions.	ReAct agents can change their approach based on the results they get at each step, leading to more effective problem-solving and decision-making.	The repeatitive process allows the agents to refine their prompts, or try different tools, based on the data already gathered, for greater accuracy.
Plan and execute agents create and carry out a multi-step plan to address a user question, especially for tasks with a sequence of coordinated actions.	These agents are particularly well-suited for handling long-term goals and intricate processes with minimal need for a human in the loop.	Dynamic plan and execute agents go further by adapting and improving their plans in real-time based on changing data and requirements.
Multi-agent systems combine the talents of multiple specialized agents to achieve a common goal or address a complicated user question.	Mujlti-agent systems can tackle very complex and multifaceted tasks that would be beyond the capablities of a single agent working alone.	Such systems distribute the workload among agents with specific expertise in areas, leading to more accurate and complete results.

Note that the categories of agentic RAG agents mentioned above aren’t always separate. Some agents may show the characteristics of multiple types depending on their design and the specific tasks they’re programmed to perform.

The sheer variety of agent types allows for the construction of agentic RAG systems that can be precisely tailored to meet the specific needs and complexities of a wide range of generative AI use cases.

09

Agentic RAG challenges

While agentic RAG offers significant improvements over traditional RAG, it also presents several potential challenges, such as:

Increased cost

Using more agents and implementing complex workflows can lead to higher consumption of computing resources and LLM tokens, creating a potential increase in operational expenses.
Response time balance

Although sometimes faster for complex queries, agentic RAG can experience delays due to multiple LLM calls and intricate reasoning processes. Balancing depth of reasoning with timely responses is crucial.
Reliability of AI agents

AI agents may struggle or fail with complex tasks. Ensuring robustness and reliability of both individual agents and the overall system is essential for practical applications.
Coordination in multi-agent systems

Effective coordination mechanisms are needed for smooth and efficient collaboration among agents in multi-agent systems.
Risk of hallucinations

Despite grounding responses with trusted enterprise data, RAG hallucination issues remain a risk. Therefore, strong validation and error recovery mechanisms are necessary.
Data security and privacy

Accessing and processing data from diverse sources introduces potential security and privacy risks. Strong security measures and access controls are critical.
Complex implementation and maintenance

Agentic RAG systems require careful planning, skilled personnel, and appropriate tools for implementation and ongoing maintenance.
Prompt engineering

Effective prompt engineering is crucial for guiding agent behavior, ensuring understanding of user intent, and achieving desired outcomes.
Observation and governance

Organizations need to monitor agent operations, understand the reasoning behind decisions, and have control mechanisms when intervention if necessary.

Proactively addressing these challenges is crucial for the responsible and effective use of agentic RAG in business environments.

10

Agentic RAG benefits

Using Agentic RAG offers many significant benefits compared to traditional RAG implementations, including:

Increased flexibility

Agentic RAG can pull data from multiple external knowledge sources and use various external tools, enabling it to address a broader range of user questions with more relevant answers.
Adaptability

It shifts from fixed, rule-based querying to intelligent problem-solving, allowing the system to dynamically adjust and refine its strategies based on interactions and the information it finds.
Improved accuracy

AI agents within Agentic RAG validate and optimize results over time, reducing hallucinations and ensuring reliable and coherent answers.
Greater scalability

Networks of RAG agents can work together to handle larger amounts of data and more complex questions, with modular designs that allow for easier expansion and workload management.
Multimodal capabilities

Agentic RAG systems can process diverse data types, such as images and audio files, providing a richer and more comprehensive approach to information processing.
Proactive problem-solving

Agents can identify missing information or additional context needs independently, leading to more complete and relevant answers.
Enhanced user experience

The combination of faster response times, more accurate answers, and intuitive interactions leads to a significantly improved user experience.

Beyond these core advantages, Agentic RAG shows proactive problem-solving abilities, where agents can independently identify missing information or the need for additional context and actively seek it out without needing explicit instructions.

The overall improvement in the quality, efficiency, and adaptability of information retrieval and processing in agentic RAG translates into a significantly better experience for users across a wide range of applications, including AI customer service, business intelligence, and scientific research.

The many benefits of agentic RAG make it a powerful tool for organizations seeking to use sophisticated AI for complex information processing and decision-making, offering a substantial step forward from the capabilities of traditional RAG.

11

Powering agentic RAG with GenAI Data Fusion

GenAI Data Fusion, the RAG tool specifically designed for agentic AI workflows, uses chain-of-thought reasoning to enable AI agents to think through complex problems and take step-by-step actions to resolve them.

GenAI Data Fusion also offers automatic LLM text-to-SQL processing, simplifying the process of accessing and understanding data from different enterprise sources. With over 200 pre-built data processing functions and by integratings both structured and unstructured data, it provides AI agents with a wide range of capabilities – ensuring greater accuracy, completeness, and compliance in LLM responses.

K2view introduces Data Agent Builder, a no-code tool designed to speed up the creation of agentic RAG apps with a highly intuitive visual interface. Unlike other LLM agent frameworks that require extensive manual coding for data retrieval, security, and privacy, Data Agent Builder does away with establishing and maintaining a complex functions libraries and intricate agent coordination flows. It supports features like multi-agent system design and includes a built-in interactive visual debugger, making the development and testing of agentic RAG solutions more efficient and accessible.

Micro-Database™ technology, a foundation of the K2view platform, provides real-time access to data related to specific entities (such as individual customers) – a crucial requirement for GenAI apps and operational use cases that often need instant access to entity-specific data.

With a strong emphasis on data security and privacy, K2view protects PII and other sensitive data wherever it resides – and directly addresses several key agentic RAG challenges, such as the risks of:

Accessing data directly from operational systems
Ensuring adequate context awareness for AI agents
Maintaining AI-ready data

The platform supports a wide range of agentic RAG use cases, including customer support, call center assistance, and self-service apps of every kind.

By focusing on AI-ready data and offering no-code development capabilities, K2view simplifies the complexities of agentic RAG, making it more practical for enterprises to implement and derive value from this transformative technology.

12

Conclusion

Agentic retrieval-augmented generation addresses the limitations of traditional RAG by incorporating autonomous AI agents capable of reasoning, planning, and acting to enhance information retrieval and generation.

Leveraging function calling, agentic RAG enables organizations to tackle complex tasks, integrate disparate data sources, and automate intricate workflows with unprecedented accuracy and scalability. K2view addresses the agentic RAG challenges of cost, latency, and reliability enabling organizations to improve flexibility, accuracy, and user experience.

By combining GenAI Data Fusion with a Data Agent Builder, K2view provides a fast track to building and deploying agentic RAG solutions, allowing organizations to realize the benefits of GenAI apps powered by AI-ready data. As AI continues to evolve, agentic RAG, facilitated by platforms like K2view, will undoubtedly play an increasingly vital role in shaping the future of how we interact with, and extract value from, data.

What is the agentic RAG?

Agentic RAG (Retrieval-Augmented Generation) is a framework where an agent actively retrieves and uses relevant information from a knowledge base to enhance the generation of responses, ensuring they are accurate and contextually appropriate.¹

What is the difference between normal RAG and agentic RAG?

RAG (Retrieval-Augmented Generation) is an AI framework that combines the strengths of traditional information retrieval systems (such as search and databases) with the capabilities of generative large language models (LLMs). Combining your data and world knowledge with LLM language skills makes grounded generation more accurate, up-to-date, and relevant to your specific needs.

Unlike many prior methods that rely exclusively on LLMs, agentic RAG uses intelligent agents to create a plan for addressing especially challenging questions involving sequential reasoning and occasional non-linguistic tools. They work like experienced information searchers who can search several documents, compare the information, synthesize a summary, and deliver comprehensive, conclusive, and accurate responses. This framework is easy to scale. Additional documents may be incorporated while each set of new documents is processed by one sub-agent.²

How to make an agentic RAG?

Step 1: Asking the question

Whether it’s a simple query or a complex problem, it all starts with a question from the user. This is the spark that sets our pipeline in motion.

Step 2: Routing the query

Next, the system checks: Can I answer this?

Yes? It pulls from existing knowledge and delivers the response immediately.

No? Time to dig deeper! The query gets routed to the next step.

Step 3: Retrieving the data

If the answer isn’t readily available, the pipeline dives into two possible sources:

Local documents: We’ll use a pre-processed PDF as our knowledge base, where the system searches for relevant chunks of information.
Internet search: If more context is needed, the pipeline can reach out to external sources to scrape up-to-date information.

Step 4: Building the context

The retrieved data whether from the PDF or the web, is then compiled into a coherent, retrieved context. Think of it as gathering all the puzzle pieces before putting them together.

Step 5: Generating the answer

Finally, this context is passed to a large language model (LLM) to craft a clear and accurate answer. It’s not just about retrieving data, it’s about understanding and presenting it in the best possible way. By the end of this, we’ll have a smart, efficient RAG pipeline that can dynamically respond to queries with real-world context.³

How to evaluate agentic RAG?

Imagine a team of LLM Application Developers building a chatbot designed to answer questions about AI governance topics. One of the data sources for this chatbot comes from IBM watsonx FAQs.

If the LLM application developers were to build a traditional RAG system, then the steps would look something like this:

Split, index, and load the documents into the vector store.
Query the vector store with the user’s question to retrieve the top-k chunks/contexts.
Use these contexts and the user’s question to create a system prompt and run it through an LLM.
Finally, retrieve the answer.

Now, let’s say users of this chatbot begin asking questions about the recently released EU AI Act. The challenge with a traditional RAG system is its lack of adaptability. Such systems may struggle to adjust to changing information sources or evolving user needs. But agentic RAG, with its modular architecture, adapts easily by adding new tools to its flexible framework.

In this case, for the chatbot to answer questions about the EU AI Act, it would need to access the EU AI Act Summary.⁴

What is the architecture of the agentic RAG?

Agentic RAG architecture comprises three main components: the retrieval system, the generation model, and the agent layer. Each component plays a critical role in the overall functioning of the architecture.

The retrieval system is responsible for fetching relevant information from a pre-defined knowledge base.

The generation model, usually a fine-tuned LLM, takes the retrieved information and generates a coherent response.

In agentic RAG, an agent acts as an intelligent intermediary that autonomously manages the retrieval and generation components. It continuously monitors performance, adapts strategies, and learns from interactions to optimize outputs.⁵

What is function calling in agentic RAG?

Function calling is a crucial feature that enables LLMs to interact with external tools and APIs, significantly expanding their capabilities. In the context of agentic RAGs, function calling allows the agent to:

Dynamically formulate and refine queries
Interact with the vector database
Process and analyze retrieved information
Make decisions on whether to continue searching or generate a response

Think of function calling as giving the LLM a toolbox. Instead of trying to solve every problem with its internal knowledge, the LLM can now reach for specific tools (functions) to accomplish tasks more efficiently and accurately.⁶

What are the advantages of agentic RAG?

Pros of agentic RAG⁷

Handles complex queries

Excels at answering questions that require synthesizing data from various sources.
Improved contextual understanding

More refined context selection and utilization by using more advanced tools and steps for retrieval and evaluation. By leveraging intelligent agents, Agentic RAG can better understand nuanced queries and provide more accurate responses.
Dynamic and adaptive

Adapts the retrieval process and flow based on context and user intention.
Improved accuracy

Better at providing accurate and insightful responses by validating the data.
Enhanced reasoning

Can perform complex logical operations and generate multi-step analysis.
Tool usage

Capable of using multiple different tools and APIs.
Autonomous decision-making

Agents can evaluate which tools to use for retrieval and when to re-retrieve data based on user interactions, allowing for real-time adjustments.
Multi-step reasoning

The architecture supports complex tasks that require multiple steps of reasoning, similar to expert researchers synthesizing information from various sources.
Adaptability

Can adjust responses based on user intent and real-time data.

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A practical guide to agentic retrieval-augmented generation

What is agentic RAG?

Table of Contents

Agentic RAG is a model that uses AI agents to enhance RAG by dynamically finding and using data from diverse sources for accurate, secure query responses.

01