Hello, I am the Fisherman.

Today, I am sharing a 35-page overview of the latest Agentic RAG.

The core problem this paper aims to address is the outdated, inaccurate outputs, and hallucinations that arise when today’s large language models (LLMs) rely on static training data to handle dynamic, real-time queries.

It starts from the fundamental principles and the evolution of the RAG paradigm, introducing the 7 architectures of Agentic RAG. It also highlights the performance in 5 application scenarios, such as key applications in healthcare, finance, and education, in great detail.

Principles of Agentic RAG

First, let’s see how Agentic RAG breaks through traditional limitations and becomes a new direction for exploration.

Generally, a typical Agent consists of 4 parts:

• LLM (with defined roles and tasks): serves as the main reasoning engine of the Agent, helping users query, generate responses, and maintain coherence.
• Memory (short-term and long-term memory): context and dependency data during interactions; short-term memory tracks the state of the conversation, while long-term memory has the ability to store data over time.
• Planning (reflection and self-evaluation): can guide the Agent’s iterative reasoning through self-reflection and self-assessment.
• Tools (such as vector search, web search, APIs, etc.): relying solely on the above three parts makes the Agent too simple; expanding capabilities through tools enhances the Agent’s abilities, such as calling external resources and obtaining data in real time.

Then, Agentic RAG introduces 4 new mechanisms.

(1) Self-reflection

By critically evaluating the correctness, style, and efficiency of its outputs, it continuously verifies the results’ discrepancies to enhance further. It possesses the ability to self-cultivate like humans, assessing retrieved results for iterative improvement.

(2) Planning

The planning mechanism can break down complex tasks into subtasks and dynamically adjust the execution order, adapting to dynamic environments and handling uncertain tasks, driving innovative applications. This capability is crucial for performing multi-hop reasoning and iterative problem-solving in dynamic and uncertain scenarios.

(3) Tool Usage

Tools can expand the capabilities of the Agent, making the Agent system more flexible and powerful.

(4) Multi-Agent Collaboration

This means that multiple different Agents work together collaboratively, with clear divisions of labor where each Agent is responsible for specific tasks. Similar to teams in enterprises working together to complete tasks, these are highly efficient.

7 Architectures of Agentic RAG

(1)Single-Agent Architecture

The single Agent architecture performs excellently in handling simple tasks but is less efficient in dealing with complex tasks. Its advantages lie in simple design and efficient resource optimization, emphasizing straightforward and easy tasks.

Case: Customer Support

(2)Multi-Agent Architecture

The multi-Agent architecture excels in handling complex tasks, capable of processing multiple query types in parallel, enhancing system scalability and accuracy. The challenge of this architecture lies in how to manage the complexity of coordination and computational overhead among multiple Agents.

Case: Multi-domain Research Assistant

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(3)Hierarchical Agent Architecture

The hierarchical Agent architecture performs best in relatively complex multi-faceted query scenarios by prioritizing tasks, improving overall response accuracy and coherence. A significant challenge is how to maintain communication and resource allocation among multi-level Agents.

Case: Financial Analysis System

(4) Self-Correcting Architecture

The core idea is to improve the accuracy and relevance of responses by dynamically assessing the relevance of retrieved documents and making corrections.

Case: Academic Research Assistant Tip: What are the latest findings in generative AI research? Comprehensive response: “The latest findings in generative AI highlight advancements in diffusion models, reinforcement learning for text-to-video tasks, and optimization techniques for large-scale model training. For more details, please refer to the studies published in NeurIPS 2024 and AAAI 2025.”

(5) Adaptive Architecture

The core principle is its ability to dynamically adjust retrieval strategies based on the complexity of queries.

(6) Graph RAG Agent Framework

The core principle is its ability to dynamically allocate retrieval tasks to specialized agents, utilizing graphical knowledge bases and textual documents. This is a relatively novel Agent architecture that combines graphical knowledge bases with unstructured document retrieval, thereby enhancing the accuracy of retrieval-augmented generation (RAG) systems.

Overview of GeAR, a graph-enhanced agent for retrieval-augmented generation.

(7) Enterprise-Level Architecture

Provides a relatively complete solution for enterprise-level applications, typically in scenarios like “invoice payment workflow”.

Real-World Applications of Agentic RAG, Success Cases

Below are successful cases of the Agentic RAG system in real-world applications, showcasing the advantages of Agentic RAG.

(1) Twitch uses the Agentic RAG system to optimize the advertising sales process.

(2) Medical institutions use the Agentic RAG system to generate patient case summaries.

(3) Legal institutions use the Agentic RAG system for contract review.

(4) Insurance companies use the Agentic RAG system to automate car insurance claims processing.

(5) Higher education institutions use the Agentic RAG system to assist researchers in generating research paper summaries.

That’s all for this 35-page overview.

Recommended Reading

4-Layer Architecture of AI Products

Li Feifei and others wrote an 80-page overview of “Agent AI”, worth reading!

Anthropic officially released “Building Efficient Agents Guide”, covering 5 common design patterns for Agents in production.

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I am the Fisherman, a programmer, currently all in AI, exploring small but beautiful business models, including AI side jobs, personal IP, sharing technology, and experiences of non-technical career changers, etc. Welcome to follow and grow with the Fisherman.