Overview of Agentic RAG: Seven Architectures Unveiled!

Hello everyone! Welcome to a channel focused on AI agents~

Today, I’m sharing a 35-page latest overview of Agentic RAG! There are a lot of pictures, which I believe many of you will enjoy.

1. Why Do We Need Agentic RAG?

Although traditional LLMs are powerful, they are limited by static training data and often cannot adapt to dynamic, real-time query needs. While RAG has made some improvements by introducing real-time data retrieval, its static workflow still has significant shortcomings:

• Lack of contextual understanding
• Inability to perform multi-step reasoning
• Difficulties in handling complex tasks

Thus, we need Agentic RAG~

2. The Evolution of RAG Technology

Before diving into Agentic RAG, let’s take a look at the basic architecture of RAG:

As shown in the figure above, traditional RAG consists of three core components:

• Retrieval Module: Responsible for querying external data sources
• Enhancement Module: Processes the retrieved data
• Generation Module: Combines LLM to generate responses

However, this simple architecture struggles to cope with complex real-world scenarios. For instance, when you ask “Help me analyze the sales data from the last three years and provide improvement suggestions,” traditional RAG might fall short.

3. Core Principles of Agentic RAG

So how does Agentic RAG break through these limitations? Through an agent architecture.

Each AI Agent contains four key components:

• LLM: Acts as the core reasoning engine
• Memory System: Maintains conversation context
• Planning Ability: Decomposes tasks and reasoning
• Tool Usage: Calls external resources and APIs

Agentic RAG introduces four working modes:

3.1 Self-Reflection Mode

Through continuous self-assessment and improvement, the agent can constantly optimize its output quality. Just like an experienced engineer, every time a task is completed, they conduct a review and improve.

3.2 Planning Mode

When faced with complex tasks, the agent first develops a detailed execution plan, breaking large tasks into manageable steps. This is like a project manager who creates a detailed project plan before starting a new project.

3.3 Tool Usage Mode

The agent can flexibly call various external tools and APIs, greatly expanding its capability boundaries. For example, when analyzing sales data, it can simultaneously call database queries, statistical analysis, and visualization tools.

3.4 Multi-Agent Collaboration Mode

Multiple agents can work collaboratively, with each agent responsible for a specific task, together accomplishing complex goals. This is like an efficient team where each member has their expertise, completing the project through collaboration.

4. Detailed Explanation of the Seven Architectures of Agentic RAG

With the development of technology, Agentic RAG has derived various powerful architectures. Each architecture has its unique advantages and applicable scenarios. Let’s take a look:

4.1 Single-Agent Architecture: Simple and Efficient

The single-agent architecture is the most basic form, but don’t underestimate it. Imagine an all-round personal assistant that can:

• Intelligently analyze user questions
• Select the most suitable information sources
• Integrate content from multiple databases
• Generate coherent responses

For example, in a customer service scenario, it can simultaneously query the order system, logistics information, and user profiles to answer user questions all at once.

4.2 Multi-Agent Architecture: The Art of Division and Cooperation

This is like a professional service team, where each member has their expertise:

• Agent 1: Responsible for structured data queries
• Agent 2: Handles semantic searches
• Agent 3: Retrieves real-time information
• Agent 4: Responsible for personalized recommendations

In a financial analysis scenario, one agent is responsible for obtaining market data, another analyzes historical trends, a third predicts future trends, and finally, the main agent integrates the output investment suggestions.

4.3 Hierarchical Architecture: A Model of Ordered Management

Hierarchical architecture is like an efficient company organizational structure:

• Top-Level Agent: Responsible for strategic decisions
• Mid-Level Agent: Executes specific tasks
• Bottom-Level Agent: Handles data retrieval

This architecture is particularly suitable for handling complex research tasks. For instance, in medical diagnosis, the top-level agent formulates diagnostic strategies, mid-level agents are responsible for symptom analysis, medical history queries, and laboratory report interpretations, while bottom-level agents handle specific data retrieval.

4.4 Self-Correcting Architecture: An Evolving System

Self-correcting architecture introduces intelligent quality control mechanisms:

• Relevance Assessment: Ensures the accuracy of retrieved content
• Query Optimization: Dynamically adjusts search strategies
• External Knowledge Supplementation: Timely fills in missing information
• Response Synthesis: Generates high-quality answers

It is like an experienced editor who continually reviews and improves the quality of the output content.

4.5 Adaptive Architecture: A Wise Response to Complexity

The most significant feature of adaptive architecture is its ability to dynamically adjust processing strategies based on the complexity of the problem:

• Simple Queries: Directly use LLM to answer
• Moderate Complexity: Single-step retrieval
• High Complexity: Multi-step reasoning and retrieval

This is like a wise mentor who can provide just the right guidance based on the difficulty of the student’s questions.

4.6 Graph-Enhanced Architecture: The Power of Knowledge Graphs

Graph-enhanced architecture significantly improves the system’s reasoning capabilities by integrating knowledge graphs:

• Relation Reasoning: Understands complex relationships between entities
• Multi-hop Reasoning: Supports cross-domain knowledge associations
• Structured Representation: Optimizes knowledge organization methods

In the medical field, it can easily handle complex questions like “What diseases are related to a certain symptom, and what are the common risk factors for these diseases?”

4.7 Document Workflow Architecture: An Enterprise-Level Choice

This is a complete solution aimed at enterprise-level applications:

• Document Parsing: Smartly extracts key information
• Status Management: Tracks processing progress
• Knowledge Retrieval: Accesses enterprise knowledge bases
• Workflow Orchestration: Coordinates multiple components
• Output Generation: Produces structured reports

For example, in contract review, it can automatically extract key clauses, compare historical contracts, check compliance, and finally generate review reports.

5. Finally, Some Application Scenarios Listed in the Text

(The following is almost a direct translation of the original text, it’s really that simple~)

5.1 Intelligent Customer Service: A New Generation of Service Experience

Taking Twitch’s advertising sales system as an example, through Agentic RAG:

• Real-time acquisition of advertiser data
• Analysis of historical campaign effectiveness
• Research on audience characteristics
• Generate customized suggestions

This not only improves operational efficiency but also brings a significant increase in conversion rates.

5.2 Healthcare: An Assistant for Precise Diagnosis

In the medical field, Agentic RAG can:

• Integrate electronic medical record data
• Retrieve the latest medical literature
• Analyze test report results
• Provide diagnostic support

For instance, when generating case summaries, the system can automatically integrate patient history, current symptoms, and related research literature to provide comprehensive reference information for doctors.

5.3 Financial Analysis: An Intelligent Decision-Making Assistant

Applications in the financial field include:

• Real-time market analysis
• Risk assessment alerts
• Portfolio optimization
• Compliance review support

For example, in insurance claims, the system can automatically process claims applications, verify policy information, assess risk factors, and provide claims suggestions.

5.4 Legal Services: An Efficient Legal Assistant

In the legal field, Agentic RAG can:

• Intelligent contract review
• Legal literature retrieval
• Case relevance analysis
• Compliance risk assessment

Through automated contract review processes, it greatly enhances legal work efficiency while reducing human error.

5.5 Education and Training: A Personalized Learning Partner

Applications in the education field include:

• Adaptive learning paths
• Personalized content recommendations
• Real-time Q&A
• Learning progress tracking

The system can dynamically adjust teaching content and difficulty based on the student’s learning level and progress.

That’s all for what I wanted to share today. If you’re interested in building AI agents, don’t forget to like and follow~