Best Overview of LLM Agents and Agentic RAG

Best Overview of LLM Agents and Agentic RAG

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Best Overview of LLM Agents and Agentic RAG

Paper link: https://arxiv.org/abs/2501.09136

Github repository: https://github.com/asinghcsu/AgenticRAG-Survey

Many links below are in this Github repository, and you can access more information by visiting the Github repository.

Abstract

Agentic Retrieval-Augmented Generation (Agentic RAG) represents a significant leap in the field of artificial intelligence by embedding autonomous agents within the RAG pipeline.This repository supplements the review paper “Agentic Retrieval-Augmented Generation (Agentic RAG): A Survey” and provides insights into the following aspects:

  • Fundamental Principles, including agentic patterns such as reflection, planning, tool usage, and multi-agent collaboration.
  • Detailed Classification of Agentic RAG Systems, showcasing frameworks such as single-agent, multi-agent, hierarchical, corrective, adaptive, and graph-based RAG.
  • Comparative Analysis of Traditional RAG, Agentic RAG, and Agentic Document Workflows (ADW), highlighting their strengths, weaknesses, and best-use scenarios.
  • Real-world Applications Across Industries, such as healthcare, education, finance, and legal analysis.
  • Challenges and Future Directions, involving scalability, ethical AI, multimodal integration, and human-machine collaboration.

Table of Contents

  1. 📜 Summary
  2. 🧩 Introduction
  3. 🤖 Agentic Patterns
  4. 🛠️ Classification of Agentic RAG Systems
  5. 🔍 Comparative Analysis of Agentic RAG Frameworks
  6. 💼 Applications
  7. 🚧 Challenges and Future Directions
  8. 🛠️ Implementation of RAG Agent Classification: Technologies and Tools
  9. 📰 Blogs and Tutorials on Agentic RAG
  10. 🖊️ Notable Related Concepts
  11. 💡 Practical Implementations and Use Cases of Agentic RAG
  12. 📚 References
  13. 🖊️ How to Cite

Introduction

Retrieval-Augmented Generation (RAG) systems combine large language models (LLMs) with retrieval mechanisms to generate contextually relevant and accurate responses.While traditional RAG systems excel in knowledge retrieval and generation, they often fall short in handling dynamic, multi-step reasoning tasks, adaptability, and orchestrating complex workflows.

Agentic Retrieval-Augmented Generation (Agentic RAG) overcomes these limitations by integrating autonomous AI agents. These agents adopt core agentic patterns such as reflection, planning, tool usage, and multi-agent collaboration, dynamically adapting to task-specific needs and providing superior performance in the following areas:

  • Knowledge retrieval across multiple domains.
  • Real-time, document-centric workflows.
  • Scalable, adaptive, and ethical AI systems.

This article explores the evolution from RAG to Agentic RAG, showcasing:

  • Agentic Patterns: Core principles driving system adaptability and intelligence.
  • Classification: A comprehensive categorization of Agentic RAG architectures.
  • Comparative Analysis: Key differences between Traditional RAG, Agentic RAG, and ADW.
  • Applications: Practical use cases across healthcare, education, finance, and more.
  • Challenges and Future Directions: Addressing scalability, ethical AI, and multimodal integration challenges.

Whether you are a researcher, developer, or practitioner, this repository provides valuable insights and resources to help you understand and advance Agentic RAG systems.

Agentic Patterns in Retrieval-Augmented Generation

The intelligence and adaptability of Agentic RAG systems stem from well-defined agentic patterns.These patterns enable agents to tackle complex reasoning tasks, adapt to dynamic environments, and collaborate effectively.Here are the core patterns of Agentic RAG:

1. Reflection

  • Definition: Agents evaluate their decisions and outputs, identifying errors and areas for improvement.
  • Key Advantages:
    • Supports iterative optimization of outcomes.
    • Enhances accuracy in multi-step reasoning tasks.
  • Example: In a medical diagnosis system, agents iteratively optimize diagnostic results based on retrieved data.
Best Overview of LLM Agents and Agentic RAG

2. Planning

  • Definition: Agents create structured workflows and task sequences to solve problems efficiently.
  • Key Advantages:
    • Facilitates multi-step reasoning by decomposing tasks.
    • Reduces computational overhead by optimizing task priorities.
  • Example: A financial analysis system plans data retrieval tasks to assess risks and provide recommendations.
Best Overview of LLM Agents and Agentic RAG

3. Tool Usage

  • Definition: Agents interact with external tools, APIs, and knowledge bases to retrieve and process data.
  • Key Advantages:
    • Extends system capabilities beyond pre-trained knowledge.
    • Supports domain-specific applications by integrating external resources.
  • Example: A legal assistant agent retrieves clauses from a contract database and applies domain-specific rules for compliance analysis.Best Overview of LLM Agents and Agentic RAG

4. Multi-Agent Collaboration

  • Definition: Multiple agents collaborate and divide tasks, sharing information and results to solve complex tasks.
  • Key Advantages:
    • Efficiently handles large-scale and distributed problems.
    • Combines specialized agent capabilities for better outcomes.
  • Example: In customer support, agents collaborate to retrieve knowledge from FAQs, generate responses, and provide follow-up.
Best Overview of LLM Agents and Agentic RAG

Significance of Agentic Patterns

These patterns form the core of Agentic RAG systems, enabling them to:

  • Adapt dynamically to task requirements.
  • Improve decision-making through self-assessment.
  • Leverage external resources for domain-specific reasoning.
  • Collaboratively handle complex distributed workflows.

Classification of Agentic RAG Systems

Agentic Retrieval-Augmented Generation (RAG) systems encompass various architectures and workflows, each optimized for specific tasks and complexity levels.The following is a detailed classification of these systems:

1. Single-Agent RAG

  • Core Idea: A single autonomous agent manages the retrieval and generation process.
  • Workflow:
    1. Query submitted to the agent.
    2. The agent retrieves relevant data from external sources.
    3. Data is processed and synthesized into a response.
  • Advantages:
    • Simple architecture suitable for basic use cases.
    • Easy to implement and maintain.
  • Limitations:
    • Limited scalability.
    • Poor performance on multi-step reasoning or large datasets.

2. Multi-Agent RAG

  • Core Idea: Multiple agents collaborate to execute complex retrieval and reasoning tasks.
  • Workflow:
    1. Agents dynamically allocate tasks (e.g., retrieval, reasoning, synthesis).
    2. Each agent focuses on a specific sub-task.
    3. Results are aggregated and synthesized into a coherent output.
  • Advantages:
    • Performs better in distributed, multi-step tasks.
    • Modular and enhanced scalability.
  • Limitations:
    • Coordination complexity increases with the number of agents.
    • Redundancies or conflicts may exist between agents.

3. Hierarchical Agent RAG

  • Core Idea: Organizes agents in a hierarchy for better task prioritization and delegation.
  • Workflow:
    1. Top-level agents coordinate sub-tasks to lower-level agents.
    2. Each lower-level agent handles a specific part of the process.
    3. Results are iteratively optimized and integrated at a higher level.
  • Advantages:
    • Suitable for large-scale and complex tasks.
    • Modular design facilitates specialization.
  • Limitations:
    • Requires complex coordination mechanisms.
    • Higher levels may become bottlenecks.

4. Corrective Agent RAG

  • Core Idea: Enables agents to iteratively evaluate and optimize their outputs through feedback loops.
  • Workflow:
    1. The agent generates an initial response.
    2. A critique module evaluates errors or inconsistencies in the response.
    3. The agent optimizes the response based on feedback.
    4. Steps 2-3 are repeated until the output meets quality standards.
  • Advantages:
    • Achieves high accuracy and reliability through iterative improvement.
    • Suitable for error-prone or high-risk tasks.
  • Limitations:
    • Increased computational overhead.
    • Feedback mechanisms need careful design to avoid infinite loops.

5. Adaptive Agent RAG

  • Core Idea: Dynamically adjusts retrieval strategies and workflows based on task requirements.
  • Workflow:
    1. The agent evaluates the query and its context.
    2. Real-time adjustments to retrieval strategies based on available data and user needs.
    3. Uses dynamic workflows to synthesize responses.
  • Advantages:
    • Highly flexible for diverse tasks and dynamic environments.
    • Improves contextual relevance and user satisfaction.
  • Limitations:
    • Designing robust adaptive mechanisms is challenging.
    • Real-time adjustments have higher computational overhead.

6. Graph-Based Agent RAG

Graph-based RAG systems extend traditional RAG by integrating graph data structures for advanced reasoning.

6.1 Agent-G: A Graph RAG Agent Framework

  • Core Idea: Dynamically allocates tasks to specialized agents using a graph knowledge base and feedback loops.
  • Workflow:
    1. Extract relationships from the graph knowledge base (e.g., mapping diseases to symptoms).
    2. Supplement with unstructured data from external sources.
    3. Use a critique module to validate results and iteratively optimize.
  • Advantages:
    • Combines structured and unstructured data.
    • Modular and suitable for complex tasks.
    • Ensures high accuracy through iterative optimization.

6.2 GeAR: Graph-Enhanced Agent RAG

  • Core Idea: Enhances the RAG system through graph expansion techniques and agent architectures.
  • Workflow:
    1. Expand graphs related to queries to enhance relationship understanding.
    2. Utilize specialized agents for multi-hop reasoning.
    3. Synthesize responses by merging structured and unstructured information from graphs.
  • Advantages:
    • Excels in multi-hop reasoning scenarios.
    • Improves accuracy in deep contextual tasks.
    • Dynamically adapts to complex query environments.

7. Agentic Document Workflows (ADW)

Agentic Document Workflows (ADW) automate document-centric processes through intelligent agents, extending traditional RAG systems.

Workflow

  1. Document Parsing and Structuring:
    • Extract structured data from documents such as invoices or contracts.
  2. State Maintenance:
    • Track context in multi-step workflows to ensure consistency.
  3. Knowledge Retrieval:
    • Retrieve relevant references from external sources or domain-specific databases.
  4. Agent Orchestration:
    • Apply business rules, perform multi-hop reasoning, and orchestrate external APIs.
  5. Actionable Output Generation:
    • Generate structured outputs tailored to specific use cases (e.g., reports or summaries).

Key Features and Advantages

  • State Maintenance: Ensures consistency in multi-step workflows.
  • Domain-Specific Intelligence: Adapts to customized rules for specific domains.
  • Scalability: Efficiently handles large-scale document processing.
  • Enhanced Productivity: Reduces manual workload and enhances human expertise.

Visual Representations

  • Single-Agent RAG Diagram
Best Overview of LLM Agents and Agentic RAG
  • Multi-Agent RAG Diagram
Best Overview of LLM Agents and Agentic RAG
  • Hierarchical RAG Workflow
Best Overview of LLM Agents and Agentic RAG
  • Graph-Based RAG Workflow
Best Overview of LLM Agents and Agentic RAG
  • ADW Workflow
Best Overview of LLM Agents and Agentic RAG

Comparative Analysis of Agentic RAG Frameworks

The table below provides a comprehensive comparative analysis of three architectural frameworks: Traditional RAG, Agentic RAG, and Agentic Document Workflows (ADW).This analysis highlights their respective strengths, weaknesses, and best-use scenarios, providing valuable insights for applicability across diverse use cases.

Features Traditional RAG Agentic RAG Agentic Document Workflows (ADW)
Focus Isolated retrieval and generation tasks Multi-agent collaboration and reasoning Document-centric end-to-end workflows
Context Maintenance Limited Achieved through memory modules Maintains state in multi-step workflows
Dynamic Adaptability Minimal High Customized for document workflows
Workflow Orchestration None Orchestrates multi-agent tasks Integrates multi-step document processing
Use of External Tools/APIs Basic integration (e.g., retrieval tools) Extends through APIs and knowledge bases Deep integration of business rules and domain-specific tools
Scalability Limited to small datasets or queries Suitable for multi-agent systems Applicable to multi-domain enterprise workflows
Complex Reasoning Basic (e.g., simple Q&A) Multi-step reasoning with agents Structured reasoning across documents
Main Applications Q&A systems, knowledge retrieval Multi-domain knowledge and reasoning Contract review, invoice processing, claims analysis
Advantages Simple, quick setup High accuracy, collaborative reasoning End-to-end automation, domain-specific intelligence
Challenges Insufficient context understanding Coordination complexity Resource overhead, domain standardization

Key Takeaways

  • Traditional RAG is best suited for simple tasks requiring basic retrieval and generation capabilities.
  • Agentic RAG excels in multi-agent collaborative reasoning, suitable for more complex multi-domain tasks.
  • Agentic Document Workflows (ADW) provide customized document-centric solutions for enterprise-level applications (e.g., contract analysis and invoice processing).

Applications

Agentic Retrieval-Augmented Generation (RAG) systems have transformative potential across multiple industries, enabling intelligent retrieval, multi-step reasoning, and dynamic adaptation to complex tasks.The following are key areas where Agentic RAG systems have significant impacts:

1. Healthcare and Personalized Medicine

  • Problem: Rapid retrieval and synthesis of medical knowledge to support diagnosis, treatment plans, and research.
  • Applications:
    • Clinical decision support systems utilizing multimodal data (e.g., patient records, medical literature).
    • Automated generation of medical reports with relevant contextual references.
    • Multi-hop reasoning analyzes complex relationships (e.g., mapping diseases to symptoms or correlations between treatments and outcomes).

2. Education and Personalized Learning

  • Problem: Providing personalized and adaptive learning experiences for diverse learners.
  • Applications:
    • Intelligent tutors designed to retrieve knowledge in real-time and provide personalized feedback.
    • Generating customized educational content based on student progress and preferences.
    • Multi-agent systems for collaborative learning simulations.

3. Legal and Contract Analysis

  • Problem: Analyzing complex legal documents and extracting actionable insights.
  • Applications:
    • Contract summarization and clause comparison, aligning with legal standards.
    • Retrieving precedent cases and regulatory guidelines to ensure compliance.
    • Iterative workflows.

Challenges and Future Directions

Despite the immense potential of Agentic Retrieval-Augmented Generation (Agentic RAG) systems, there are still unresolved challenges and research opportunities:

Challenges

  1. Coordination Complexity in Multi-Agent Systems:

    • Managing communication and collaboration among multiple agents can lead to inefficiencies and increased computational overhead.
    • Balancing task allocation and resolving conflicts between agents remains a key issue.

  2. Ethics and Responsible AI:

    • Ensuring unbiased retrieval and decision-making in sensitive areas such as healthcare and finance.
    • Addressing data privacy concerns and building transparent systems that adhere to ethical standards.

  3. Scalability and Latency:

    • Scaling Agentic RAG systems to handle large datasets and high-frequency queries without sacrificing response times.
    • Addressing latency issues in multi-agent and graph workflows.

  4. Human-Machine Collaboration:

    • Designing systems that effectively integrate human oversight with autonomous agents to tackle tasks requiring domain expertise.
    • Maintaining user trust and control while leveraging the advantages of AI agents.

  5. Expanding Multimodal Capabilities:

    • Integrating text, images, audio, and video data to generate richer and more comprehensive outputs.
    • Handling multimodal reasoning complexities in real-time applications.

    Future Directions

    1. Enhanced Agent Coordination:

      • Developing more robust coordination frameworks for hierarchical and multi-agent systems.
      • Introducing adaptive learning mechanisms to dynamically improve task allocation.

    2. Domain-Specific Applications:

      • Customizing Agentic RAG systems for specific domains such as legal analysis, personalized education, and advanced scientific research.

    3. Ethical AI and Governance Frameworks:

      • Building tools to monitor, interpret, and mitigate biases in AI outputs.
      • Developing policies and guidelines for ethical deployment in high-risk environments.

    4. Efficient Graph Reasoning:

      • Optimizing graph workflows for large-scale real-world applications.
      • Exploring hybrid approaches that combine graph reasoning with neural networks.

    5. Human-Machine Collaboration:

      • Designing intuitive interfaces and workflows that enable humans to interact effectively with Agentic RAG systems.
      • Focusing on explainability and user-centered design.

    Implementation of RAG Agent Classification: Technologies and Tools

    Technology Tools Description Notebook
    Single-Agent RAG LangChain, FAISS, Athina AI Uses AI agents to find and generate answers through vector databases and web searches. View Notebook
    LlamaIndex, Vertex AI (Vector Storage, Text Embedding, LLM), Google Cloud Storage Demonstrates a single-router Agentic RAG system using LlamaIndex and Vertex AI for context retrieval and response generation. View Notebook
    LangChain, IBM Granite-3-8B-Instruct, Watsonx.ai, Chroma DB, WebBaseLoader Builds an Agentic RAG system in Watsonx.ai using the IBM Granite-3-8B-Instruct model to answer complex queries and provide external information. View Notebook
    LangGraph, Chroma, NVIDIA Inference Microservices (NIMs), Tavily Search API This system uses a router-based architecture to decide whether queries should be handled by the RAG pipeline (retrieving from vector databases) or web search pipeline. AI agents evaluate query topics and route them to the appropriate pipeline for information retrieval and response generation, ensuring accurate, relevant, and context-enhanced answers. View Notebook
    LlamaIndex, Redis, Amazon Bedrock, RedisVectorStore, LlamaParse, BedrockEmbedding, Semantic Caching This system implements a ReAct agent-based RAG pipeline, where agents interact with Redis-supported indexing and vector storage to retrieve and process data from PDF documents. It uses Amazon Bedrock embeddings and LlamaIndex to handle documents, build embeddings, and process retrieval-augmented generation. Additionally, semantic caching optimizes the system by reducing redundant LLM queries for repeated or similar user questions, improving response times and efficiency. View Notebook
    Multi-Agent RAG Coordinator AutoGen, SQL, AI Search Index This coordinator leverages multi-agent systems to facilitate complex task execution by coordinating agent interactions. Using factory patterns and various predefined strategies (e.g., classic_rag for retrieval-augmented generation and nl2sql for translating natural language to SQL), the system supports flexible multi-agent collaboration for tasks such as database queries and document retrieval. The coordinator facilitates agent communication, iterative responses, and customizable strategies, providing high adaptability for diverse use cases. View Notebook
    Hierarchical Multi-Agent RAG Weaviate, ExaSearch, Groq, crewAI This approach uses a hierarchical agent architecture, with multiple agents responsible for specific tasks or tools. A manager agent coordinates specialized agents (e.g., WeaviateTool for internal document retrieval, ExaSearchTool for web search, and Groq for rapid AI inference) to handle complex queries. This flexible task-oriented system supports various use cases, such as QA and workflow automation. View Notebook
    Corrective RAG LangChain, LangGraph, Chromadb, Athina AI Refines relevant documents, removes irrelevant documents, or performs web searches. View Notebook
    LangChain, FAISS, HuggingFace Inference API, SmolAgents, HyDE, Self-Query This system combines query reconstruction and self-query strategies to address limitations of traditional RAG systems. It performs iterative retrieval by critically evaluating the relevance of retrieved documents and re-querying when necessary. Agents refine queries to enhance semantic similarity and ensure higher accuracy. A self-scoring mechanism assesses the quality of retrieved information and enhances results through iterative improvement. This system aligns with corrective RAG principles by reducing hallucinated content and improving retrieval relevance. View Notebook
    Adaptive RAG LangChain, LangGraph, FAISS, Athina AI Adjusts retrieval methods based on query types, using indexed data or web searches. View Notebook

    Blogs and Tutorials on Agentic RAG

    1. DeepLearning.AI: How Agents Enhance LLM Performance. DeepLearning.AI

    2. Weaviate Blog: What is Agentic RAG? Weaviate Blog

    3. LangGraph CRAG Tutorial: LangGraph CRAG: Context Retrieval-Augmented Generation Tutorial. LangGraph CRAG

    4. LangGraph Adaptive RAG Tutorial: LangGraph Adaptive RAG: Adaptive Retrieval-Augmented Generation Tutorial. LangGraph Adaptive RAG. Accessed on: 2025-01-14.

    5. LlamaIndex Blog: Agentic RAG with LlamaIndex. LlamaIndex Blog

    6. Hugging Face Cookbook. Agentic RAG: Accelerating Retrieval-Augmented Generation through Query Reconstruction and Self-Querying. Hugging Face Cookbook

    7. Hugging Face Agentic RAG: https://huggingface.co/docs/smolagents/en/examples/rag

    8. Qdrant Blog. Agentic RAG: Enhancing Information Retrieval by Combining RAG with Agents. Qdrant Blog

    9. Semantic Kernel: The Semantic Kernel is Microsoft’s open-source SDK that integrates large language models (LLMs) into applications. It supports agentic patterns, enabling the creation of autonomous AI agents for natural language understanding, task automation, and decision-making. It has been used in scenarios such as ServiceNow’s P1 incident management to facilitate real-time collaboration, automate task execution, and seamlessly retrieve contextual information.

      • GitHub – RAG Using Semantic Kernel
      • GitHub – Semantic Kernel
      • ServiceNow Case Study

    Practical Implementations and Use Cases of Agentic RAG

    1. AWS Machine Learning Blog. How Twitch Uses Agentic Workflows with RAG on Amazon Bedrock to Accelerate Ad Sales. AWS Machine Learning Blog
    2. LlamaCloud Demo Library. Patient Case Summary Workflow with LlamaCloud. GitHub 2025. Accessed on: 2025-01-13.
    3. LlamaCloud Demo Library. Contract Review Workflow with LlamaCloud. GitHub
    4. LlamaCloud Demo Library. Auto Insurance Claim Workflow with LlamaCloud. GitHub
    5. LlamaCloud Demo Library. Research Paper Report Generation Workflow with LlamaCloud. GitHub

    Notable Related Concepts

    • Agent Design Patterns Part 1
    • Agent Design Patterns Part 2, Reflection
    • Agent Design Patterns Part 3, Tool Usage
    • Agent Design Patterns Part 4, Planning
    • Agent Design Patterns Part 5, Multi-Agent Collaboration
    • Building Agentic RAG with LlamaIndex
    • AI Agent Design Patterns with AutoGen
    • LangGraph Agentic RAG

    References

    Research Papers on Agentic RAG

    1. Single-Agent RAG (Router-Based)

    • Search-o1: A Large Reasoning Model for Agent-Augmented Search https://arxiv.org/abs/2501.05366

    2. Multi-Agent RAG

    • Agent-Augmented Retrieval Generation for Time Series Analysis https://arxiv.org/abs/2408.14484

    3. Corrective RAG

    • Agent AI-Driven Technical Troubleshooting for Enterprise Systems https://arxiv.org/abs/2412.12006
    • Corrective RAG (CRAG) https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag/
    • Corrective Retrieval-Augmented Generation https://arxiv.org/abs/2401.15884
    • Agent AI-Driven Technical Troubleshooting for Enterprise Systems https://arxiv.org/abs/2412.12006

    4. Adaptive RAG

    • Langgraph Adaptive RAG https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag/
    • MBA-RAG: A Bandit-Based Adaptive Retrieval-Augmented Approach https://arxiv.org/abs/2412.01572
    • CtrlA: Adaptive Retrieval-Augmented Generation through Intrinsic Control https://arxiv.org/abs/2405.18727
    • Adaptive-RAG: Adapting Retrieval-Augmented Large Language Models through Question Complexity Learning https://arxiv.org/abs/2403.14403
    • AT-RAG: An Adaptive RAG Model Enhancing Query Efficiency through Topic Filtering and Iterative Reasoning https://arxiv.org/abs/2410.12886

    5. Graph-Enhanced RAG

    • GeAR: Graph-Enhanced Retrieval-Augmented Generation Agent https://arxiv.org/abs/2412.18431
    • Agent-G: A Graph Retrieval-Augmented Generation Agent Framework https://openreview.net/forum?id=g2C947jjjQ
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    Best Overview of LLM Agents and Agentic RAG

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