Agentic RAG - How it works, Use Cases, Comparison with RAG | USAII®

1. HOW IT WORKS, USE CASES, COMPARISON WITH RAG www.usaii.org © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved.

2. AGENTIC RAG- HOW IT WORKS, USE CASES, COMPARISON WITH RAG ““ Nvidia launches AgentIQ toolkit to connect disparate AI agents” The global chipmaker Nvidia presents a new toolkit, packed with a variety of tools, inclusive of the ones to weave in RAG (Retrieval-Augmented Generation), search, and conversational UI into agentic AI applications. While the global chipmaking giant plays around the intention to assist businesses breakdown silos between different agent systems, remarks Paul Chada, Co- Founder of Agentic AI-based software-providing startup 'Doozer AI.' The large language models (LLM) game has been up and about for quite some time, leaving more space for greater advances in the related technologies. LangChain-CrewAI-Microsoft's Semantic Kernel What is the common link between the three mentioned above? These are open-source multi-agent AI systems and frameworks that enable users to create collaborative teams of AI agents that can converse and coexist in peace, all while handling complex tasks. These and many other Agentic AI frameworks are ramping up their capabilities to up their game in the evolving Agentic RAG ecosystem. www.usaii.org 1 © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved.

3. AGENTIC RAG- HOW IT WORKS, USE CASES, COMPARISON WITH RAG Agentic RAG- Is that a new vocab for you? Read on as we deconstruct and elaborate further. The nuanced capability of machines to reason and make decisions has been a long-standing goal. Recent advancements in natural language processing (NLP) have led to the development of various models that can process and generate human-like responses. Retrieval-Augmented Generation (RAG) is the one that we were looking for, which has shown impressive results in generating accurate and informative responses. However, the RAG model is not barred from limitations, however, these have been addressed by the introduction of Agentic RAG. This read will delve into the workings of Agentic RAG, its use cases, and compare it with the traditional model, and much more. Reflecting on the Ancestral Ruins- What is RAG? MAPPING AI CONCEPTS High Real-Time Data Integration Agentic RAG RAG Combines autonomous decision-making with dynamic data retrieval Integrates real-time data but lacks autonomous decision-making. Low Autonomy High Autonomy Agentic AI Traditional AI Autonomous agents with minimal real-time data integration. Operates without real-time data and low autonomy. Low Real-Time Data Integration source: Datacamp.com www.usaii.org 2 © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved.

4. AGENTIC RAG- HOW IT WORKS, USE CASES, COMPARISON WITH RAG The Retrieval Augmented Generation (RAG) is an artificial intelligence application that combines the strengths of retrieval-based and generative models. Its two main components include a retriever and a generator. The retriever is responsible for fetching relevant documents or passages from a large corpus, while the generator uses these retrieved documents to generate a response. The RAG model has shown impressive results in various NLP tasks, such as question-answering, text summarization, and conversational dialogue systems. However, the following are the tricky areas where the RAG model fails big time: Ÿ Lacks Control: The RAG model generates responses based on the retrieved documents but does not control the generation process. Ÿ Limited Contextual Grasp: The RAG model's inability to understand the context of the input prompt leads to irrelevant or inaccurate responses. Adding the Agentic RAG Layers: Agentic RAG revolutionizes the way questions are answered by introducing an agent-based framework. Agentic RAG employs intelligent agents to tackle complex questions that require intricate planning, multiple-step reasoning, and external tool utilization. These agents act like expert researchers, adeptly navigating multiple documents, comparing information, generating summaries, and delivering comprehensive and accurate answers. Its Smart Successor- Understanding Agentic RAG Agentic RAG is an extension of the traditional RAG model that addresses its limitations. The term "agentic" refers to the model's ability to take control of the generation process and make decisions based on its "agency." Agentic RAG is designed to be more flexible and adaptable, allowing it to generate more accurate and relevant responses. Components of Agentic RAG: 1. Retriever: The retriever is responsible for fetching relevant documents or passages from a large corpus, just like in the traditional RAG model. 2. Generator: The generator uses the retrieved documents to generate a response but with a twist. The generator is now equipped with a decision-making mechanism that allows it to take control of the generation process. 3. Decision-making mechanism: This mechanism allows the generator to evaluate the relevance and accuracy of the retrieved documents and make decisions about which documents to use or ignore. www.usaii.org 3 © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved.

5. AGENTIC RAG- HOW IT WORKS, USE CASES, COMPARISON WITH RAG Types of AI Agents for Agentic RAG Routing Agents Agent RAG : Query Engine A Determine which external knowledge sources and tools are used to address a user query. Query Router Response Tools RAG : Query Engine B LLM Query Planning Agents Agent RAG : Query Engine A Query Planner Popular Task managers of the RAG pipeline that process complex user queries to break them down into step-by-step processes. Query Synthesis Response Tools RAG : Query Engine 2 LLM Re-Act (Reasoning and Action) Agents Actions Actions An Agent framework that creates multi-agentic systems that can create and then act in a seriated manner. Reasoning Traces Reasoning Traces LM LM Env LM Env Observations Observations (Reason + Act) ReAct Reason Only Act Only Plan & Execute Query Planner Plan with Steps (DAG) Chain Executor Query Synthesis Response Plan-and-Execute Agents Easily execute multistep workflows without calling back to the primary agent, reducing costs and increasing efficiency. RAG : Query Engine A Tools source: leewayhertz.com RAG : Query Engine 2 LLM www.usaii.org 4 © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved.

6. AGENTIC RAG- HOW IT WORKS, USE CASES, COMPARISON WITH RAG Implementing Agentic RAG Framework Agentic RAG works by combining the strengths of retrieval-based and added benefit of decision-making. Elaborated below is a step-by-step overview of how it works: generative AI models with the 1. Input prompt/Initial Assessment and Planning: The user provides an input prompt, such as a question or a topic. 2. Retrieval: The retriever fetches relevant documents or passages from a large corpus based on the input prompt. 3. Evaluation: The decision-making mechanism evaluates the relevance and accuracy of the retrieved documents. 4. Decision-making: Based on the evaluation, the decision-making mechanism decides which documents to use or ignore. 5. Generation: The generator uses the selected documents to generate a response. 6. Post-processing: The generated response is post-processed to refine its accuracy and relevance. Use Cases for Agentic RAG Agentic RAG has a wide range of applications in NLP, including: Ÿ Conversational dialogue systems: Agentic RAG can be used to build conversational dialogue systems that can engage in natural-sounding conversations with humans. Ÿ Question-answering systems: Agentic RAG can be used to build question-answering systems that can provide accurate and relevant answers to user queries. Ÿ Text summarization: Agentic RAG can be used to build text summarization systems that can generate concise and accurate summaries of long documents. Ÿ Language translation: Agentic RAG can be used to build language translation systems that can generate accurate and fluent translations. Ÿ Automated support: Agentic RAG streamlines customer support services that can be automated and handle simpler customer inquiries. Ÿ Realtime QnA: RAG-powered chatbots and FAQs to provide employees and customers with relevant accurate information. www.usaii.org 5 © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved.

7. AGENTIC RAG- HOW IT WORKS, USE CASES, COMPARISON WITH RAG Key Differentials- Traditional vs Agentic RAG Agentic RAG offers several advantages over traditional RAG, including: 1. Improved control: Agentic RAG provides more control over the generation process, allowing it to generate more accurate and relevant responses. 2. Adaptability: Agentic RAG is a transition from static rule-based querying to adaptive intelligent problem-solving. Multiagent systems encourage multiple AI models to collaborate and check each other's work. 3. Accuracy: Smart AI agents can iterate on previous processes to optimize results over time. 4. Enhanced contextual understanding: Agentic RAG's decision-making mechanism allows it to better understand the context of the input prompt, leading to more accurate and relevant responses. 5. Multimodality: Agentic RAG systems benefit from recent advancements in multimodal large language models (LLMs) to work with a greater range of data types. 6. Scalability: Agentic RAG has greater scalability, as developers can construct flexible and scalable RAG systems that can handle a wide range of user queries. 7. Massive flexibility: Agentic RAG's decision-making mechanism allows it to adapt to different scenarios and generate responses that are tailored to the specific context. Hurdles that Limit Agentic RAG Interpretability and Explainability Privacy and Security Concerns Data Quality and Curation Poor data quality leads to unreliable outputs, making robust data management and quality assurance essential. Developing AI models and techniques that can explain their reasoning and data sources is necessary to foster trust and accountability. Implementing stringent data protection measures, access management systems is vital to guard user privacy and prevent data breaches. Exploring the Future Frontiers Hang in there as the world switches sides toward Generative AI. The evolution of Retrieval-Augmented Generation (RAG) systems has paved the way toward greater generative AI advancements. Each step in the journey has paved its way toward a lasting Agentic RAG ecosystem. Eventually, Agentic RAG emerges as the pinnacle of innovation, redefining how AI systems interact with data and respond to real-world demands. In the future, expect real-time adaptability, greater autonomy, and multi-agent collaboration, elevating the potential of Generative AI for the greater good. Learning, adapting, and optimizing workflows dynamically shall allow for a bigger room for multi-domain applications across industries worldwide. Overwhelmed with the advances expected in Agentic RAG and feeling jittery about your current skillsets? You are covered as the globally accepted Artificial Intelligence Certification Programs can guide the way toward greater autonomy with enhanced competencies in Generative AI and Agentic RAG. Get your hands on the best of AI prompt engineering abilities to ace the emerging Agentic AI RAG! www.usaii.org 6 © Copyright 2025. United States Artificial Intelligence Institute. All Rights Reserved.

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