Understanding LLM, RAG, and AI Agent: The Three-Layer Architecture of Intelligent AI Systems

Core Question This Article Answers: What are the differences between LLM, RAG, and AI Agent, and how do they work together to build effective, production-ready AI systems?

In the field of artificial intelligence, many developers and product managers often feel confused about the relationships between LLM, RAG, and AI Agent. Some view them as competing technologies, but in reality, they represent three essential layers of a single intelligent system. Through my experience building practical AI systems over the past two years, I’ve come to understand that only by organically combining these three components can we create truly powerful and deployable AI applications. This article will systematically break down their roles, differences, and collaborative mechanisms to help you understand how to apply these technologies in real projects.

Image Source: Unsplash

Core Question: How Do LLM, RAG, and AI Agent Form a Complete AI Intelligence System?

Many technical teams approaching AI for the first time often treat LLM, RAG, and AI Agent as separate tools, leading to flawed system designs. In reality, these three components represent the thinking, memory, and action capabilities of an AI system, respectively, and only through collaborative operation can they deliver maximum value. This article will explore the functions, limitations, and practical application scenarios of each layer, providing you with a clear framework for understanding and implementing these technologies.

LLM: The Thinking Brain of AI

Core Question This Section Answers: What is a Large Language Model (LLM)? Why is it called the brain of AI, and what are its limitations?

LLM (Large Language Model) serves as the core thinking engine of contemporary AI systems. Trained on massive text datasets, it possesses the ability to understand, generate, and reason with natural language. Think of an LLM as an extremely knowledgeable brain—it can write, summarize, explain concepts, and even perform simple logical reasoning. However, this brain’s knowledge is frozen at the point when its training data was collected, unable to automatically acquire the latest information.

How LLMs Work and Their Core Capabilities

LLMs are based on the Transformer architecture, learning language patterns by predicting the next word in a sequence. This training approach enables the model to capture grammar, syntax, semantics, and even some common-sense knowledge. In practical applications, LLMs excel at tasks that rely purely on language understanding and generation:

• Text Generation and Creation: Ability to write articles, stories, poetry, and various text content based on prompts
• Summarization and Extraction: Condensing long documents into key points while preserving core information
• Explanation and Translation: Explaining complex concepts in simple language or converting between different languages
• Code Generation: Generating code snippets based on descriptions to assist development work

LLM Limitations: The Static Knowledge Problem

Despite their remarkable intelligence, LLMs face a fundamental constraint—their knowledge is static. Taking GPT-4 as an example, its knowledge cuts off at the end of its training period. If you ask it about events that occurred after its training date, such as “What were yesterday’s news headlines?”, it cannot provide accurate answers and may even fabricate seemingly plausible but actually incorrect information.

This limitation poses significant challenges in practical applications. For instance, in rapidly evolving fields like finance, healthcare, or technology, advice based on outdated information could lead to serious errors. LLMs themselves lack mechanisms to verify the factual accuracy of their responses or connect to real-time data sources for current information.

LLM Application Scenarios Analysis

Based on these characteristics, LLMs are most suitable for pure language tasks that don’t rely on current external knowledge:

• Content creation and editing assistance
• Document summarization and format conversion
• Learning tutoring and concept explanation
• Code commenting and basic programming help
• Basic conversational capabilities for chatbots

In these scenarios, LLMs can function independently without additional components. However, when tasks involve specific domain knowledge or real-time information, using LLMs alone exposes their limitations.

Image Source: Unsplash

Personal Reflection: From Over-Reliance on LLM to Understanding Its Boundaries

In my early AI project experiences, I attempted to use LLMs to solve every problem, only to encounter failures in scenarios requiring current information. In one instance, our deployed customer service bot provided outdated product information, causing customer confusion. This taught me that while LLMs are powerful, we must clearly define their appropriate boundaries. They function more like extremely intelligent assistants rather than omniscient entities—understanding this distinction is crucial for designing reliable AI systems.

RAG: The External Memory of AI

Core Question This Section Answers: How does Retrieval-Augmented Generation (RAG) extend LLM capabilities? What problems does it solve?

RAG (Retrieval-Augmented Generation) serves as the system that adds external memory to LLMs. It retrieves relevant information from databases, document repositories, or web resources to provide LLMs with real-time, accurate context. If LLM is the brain, then RAG functions as the extensive external memory bank connected to this brain, instantly bringing static knowledge models to life.

Detailed Working Mechanism of RAG

When a RAG system receives a user query, it doesn’t immediately ask the LLM to generate an answer. Instead, it first executes a retrieval process:

1. Query Understanding: Analyzing the intent of the user’s question and key information needs
2. Document Retrieval: Searching for the most relevant documents or data fragments from connected resource libraries
3. Context Construction: Organizing retrieved information into prompt context that the LLM can understand
4. Augmented Generation: The LLM generating accurate, targeted responses based on the provided context

This process ensures that every answer builds upon the latest, most relevant factual foundation rather than relying solely on the LLM’s built-in knowledge.

Core Advantages and Value of RAG

After integrating RAG, AI systems gain significant capability enhancements:

• Real-time Knowledge Access: Ability to answer questions about recent events, data, and developments
• Domain Specialization: Can access specialized documents, technical manuals, company internal materials, and other specific knowledge sources
• Factual Accuracy: Dramatically reduces LLM “hallucination” problems, providing evidence-based answers
• Traceability: Ability to trace each answer back to its source documents, enhancing credibility and auditability
• No Retraining Required: Updating knowledge only requires updating the retrieval library, without retraining expensive LLM models

Practical RAG Application Scenarios

RAG is particularly suitable for tasks requiring combination of specific knowledge bases and real-time information:

• Enterprise Knowledge Base Q&A: Employees can inquire about company policies, processes, or product information, with the system retrieving answers from internal documents
• Technical Support Systems: Answering user questions based on the latest technical documentation, ensuring advice accuracy
• Academic Research Assistants: Connecting to academic databases to provide literature-supported research answers
• News Analysis Tools: Integrating current news sources to provide fact-based current affairs analysis

In these scenarios, RAG acts as the bridge between LLMs and the real world, ensuring AI system outputs are both intelligent and accurate.

RAG Implementation Example

Consider a technical support scenario where a user asks “How to solve memory leak issues with XYZ device?”:

1. The RAG system first retrieves sections related to “XYZ device” and “memory leak” from the technical documentation library
2. Finds relevant troubleshooting guides and latest patch information
3. Provides this information as context to the LLM
4. The LLM generates step-by-step solutions based on these specific instructions

This approach proves far more reliable than relying solely on the LLM’s general knowledge, especially in specialized domains.

Image Source: Unsplash

Personal Reflection: How RAG Changed Our Expectations of AI Accuracy

Before integrating RAG, our AI systems could generate fluent responses, but factual accuracy remained a persistent concern. After implementing RAG, the most significant change wasn’t technical but rather the improvement in user trust. When users know that every answer is document-backed, they become more willing to rely on the system’s information. This credibility transformation cannot be achieved by simply optimizing LLM prompts—it requires systematic design changes.

AI Agent: The Autonomous Action Capability of AI

Core Question This Section Answers: How do AI Agents empower AI with action capabilities? How do they fundamentally differ from LLMs and RAG?

AI Agent represents systems that add autonomous action capabilities on top of LLMs, achieving goal-oriented behavior through control loops. If LLMs provide thinking capability and RAG provides knowledge access, then AI Agent provides the mechanism to transform these capabilities into concrete actions. AI Agents don’t just answer questions—they can proactively plan and execute complex tasks.

Core Architecture of AI Agent: The Control Loop

AI Agents are built around a continuous control loop that typically includes four key stages:

1. Goal Setting: Clearly defining the task to be completed or problem to be solved
2. Step Planning: Breaking down large goals into executable small steps, determining optimal execution order
3. Action Execution: Calling appropriate tools or APIs to execute each step
4. Review and Reflection: Evaluating execution results, adjusting strategies, and replanning when necessary

This loop enables AI Agents to handle complex tasks requiring multi-step reasoning and external interactions, going beyond single-shot question answering.

Scope of AI Agent Capabilities

AI Agents equipped with action capabilities can accomplish impressive automated tasks:

• Autonomous Research: Given a topic, able to search for information, analyze content, and generate comprehensive reports
• Workflow Automation: Handling end-to-end processes like data collection, processing, analysis, and presentation
• Interactive Tasks: Operations requiring interaction with external systems, such as sending emails, scheduling meetings, or updating databases
• Complex Problem Solving: Solving open-ended problems through multiple attempts and adjustments

Practical AI Agent Application Example

Consider a market research task where an AI Agent can automatically execute the following process:

1. Set goal: “Analyze changes in AI regulatory policies over the past three months”
2. Plan steps: Search relevant news, retrieve government documents, analyze main changes, summarize impacts
3. Execute actions: Call search APIs for recent articles, access government databases for policy texts, use LLM for content analysis
4. Review and reflect: Check information completeness, supplement searches if necessary, finally generate structured reports

This entire process operates completely automatically without human intervention at each step.

Hierarchical Relationship Between AI Agent, LLM, and RAG

The key to understanding AI Agents is recognizing they reside at the outermost layer of the system architecture:

• LLM serves as the “brain” providing reasoning and language capabilities
• RAG serves as “memory” providing knowledge support
• AI Agent serves as “limbs” executing concrete actions

This layered architecture allows each component to develop and optimize independently while collaborating through clear interfaces.

Image Source: Unsplash

Personal Reflection: The Paradigm Shift from Passive Response to Active Action

The biggest challenge in developing AI Agent systems isn’t technical implementation but rather mindset transformation. We’re accustomed to AI as question-answering tools, while Agents require us to think of AI as autonomous assistants. In one project, we initially designed a system that could answer customer questions, then transformed it into an Agent that could proactively identify and solve problems—this shift completely changed the product’s value proposition. The true potential of AI lies not in what it can answer, but in what it can proactively accomplish.

Collaborative Work: LLM + RAG + AI Agent

Core Question This Section Answers: How are LLM, RAG, and AI Agent combined in practical applications?

Using LLM, RAG, or AI Agent individually can solve specific problems, but truly powerful AI systems emerge from their organic combination. This combination creates an intelligent entity with complete capabilities for thinking, memory, and action, able to handle complex tasks in the real world. Understanding how to architect the relationships between these three layers is key to building production-grade AI systems.

Advantages of Layered Architecture

Designing LLM, RAG, and AI Agent as collaborative layered systems brings multiple benefits:

• Complementary Capabilities: Each component compensates for the limitations of others
• Flexible Configuration: Specific layers can be enabled or disabled based on particular needs
• Independent Optimization: Each component can be improved separately without affecting the overall architecture
• Extensibility: New tools, data sources, or models can be easily integrated

Typical Workflow for Combined Usage

A complete three-layer AI system typically processes tasks following this basic workflow:

1. Task Reception: The AI Agent receives user goals or automatically detects tasks requiring processing
2. Planning Phase: The Agent uses LLM for task decomposition and step planning
3. Knowledge Retrieval: For steps requiring external knowledge, calls RAG to retrieve information from relevant data sources
4. Decision and Execution: The LLM makes decisions based on available information, and the Agent executes specific actions
5. Evaluation and Iteration: The system evaluates results, adjusting strategies and re-executing when necessary

This workflow fully leverages the advantages of each component, forming an intelligent closed loop.

Technology Selection Guide

Choose appropriate component combinations based on task complexity:

Task Type	Recommended Architecture	Typical Use Cases
Pure Language Tasks	LLM Only	Content creation, text summarization, basic conversation
Knowledge-Intensive Tasks	LLM + RAG	Document Q&A, technical support, research assistance
Action-Oriented Tasks	LLM + RAG + AI Agent	Automated research, workflow management, complex problem solving

This table provides clear technology selection guidance based on practical application scenarios, helping teams make architectural decisions according to specific needs.

Production System Example: Intelligent Research Assistant

Consider a system that needs to handle complex research tasks:

• LLM Layer: Responsible for understanding research questions, generating search queries, analyzing content relevance, writing final reports
• RAG Layer: Connected to academic databases, current news sources, professional journals, ensuring information timeliness and accuracy
• AI Agent Layer: Coordinating the entire research process—planning research steps, executing search operations, organizing findings, generating and delivering reports

Such a system not only finds information but also actively completes the entire process from problem definition to result delivery.

Integration Considerations

Successful integration of the three-layer architecture requires attention to several key points:

• Interface Design: Ensure clear, efficient communication mechanisms between layers
• Error Handling: Design fault-tolerant mechanisms so the system can gracefully degrade when one layer fails
• Performance Optimization: Consider latency issues, especially when involving multiple retrieval steps and external API calls
• Cost Management: Balance capabilities with costs, avoiding unnecessary complexity

Image Source: Unsplash

Personal Reflection: From Technical Showmanship to Practical AI System Design

In the AI field, it’s easy to be mesmerized by impressive technical demonstrations, but systems that truly create value are often those that robustly integrate multi-level capabilities. The most important lesson I’ve learned is: the best AI systems don’t showcase the most advanced technology, but rather the most appropriate combination of technologies to solve practical problems. A simple but reliable three-layer system far outperforms a technologically advanced but unreliable single-point solution.

Personal Overall Reflection: Understanding AI Systems from a Layered Perspective

Core Question This Section Answers: What are the key insights about LLM, RAG, and AI Agent collaboration from practical experience building AI systems?

Looking back on my journey building AI systems, the most valuable insight was breaking free from the “single technology solution” mindset. Initially, like many developers, I searched for that “universal” AI component—the silver bullet that could solve all problems. But practical experience has shown that truly powerful AI systems come from understanding and appropriately combining different layers of capabilities.

Mindset Shift: From Tools to Architecture

The biggest shift was moving from viewing LLM, RAG, and AI Agent as mutually exclusive tools to seeing them as complementary layers of the same system. This perspective change influences every aspect of system design:

• Design Prioritizes Capabilities Over Components: First clarify what capabilities the system needs (thinking, memory, or action), then choose component combinations that implement these capabilities
• Focus on Interfaces Rather Than Implementations: Define clear data flows and control flows between layers rather than over-optimizing individual components
• Accept Progressive Refinement: Start simple (LLM only), then gradually add capabilities (RAG, then Agent) as needed

This approach avoids over-engineering while ensuring system scalability.

Pragmatism Over Technical Perfectionism

In the AI field, where technology evolves extremely rapidly, chasing the latest models can easily cause projects to spiral out of control. What I’ve learned is: in most application scenarios, appropriately combining mature technologies proves more effective than using the most advanced single component. A system combining LLM, RAG, and basic Agent capabilities, even if each component isn’t state-of-the-art, will outperform a system using only top-tier LLMs but lacking other capabilities.

Sustainable AI System Design

Ultimately, I’ve come to understand that sustainable AI system design rests on several core principles:

• Transparency: Knowing where answers come from (LLM’s intrinsic knowledge, RAG’s retrieved documents, or Agent’s action results)
• Maintainability: Being able to independently update knowledge bases (via RAG) without affecting reasoning capabilities (LLM)
• Evolvability: Being able to add new action capabilities (via Agent) without refactoring the entire system

These principles ensure AI systems can continuously evolve as requirements change and technology advances.

Practical Summary and Action Checklist

Based on the above analysis, here is a practical guide for building AI systems:

Capability Assessment Checklist

Before starting an AI project, ask yourself these questions:

• [ ] Does the task require current or domain-specific knowledge? Yes → Consider adding RAG
• [ ] Does the task require interaction with external systems or multi-step operations? Yes → Consider adding AI Agent
• [ ] Is the task primarily language understanding and generation? Yes → LLM might suffice
• [ ] Does the system need to explain answer sources? Yes → RAG provides traceability
• [ ] Does the task need to adapt to changing conditions? Yes → Agent provides flexibility

Technology Integration Steps

1. Start with Basics: First use LLM to solve core language tasks
2. Add Knowledge Support: Integrate RAG to handle domain-specific or real-time information needs
3. Introduce Action Capabilities: Add AI Agent layer to handle complex tasks requiring autonomous execution
4. Test and Iterate: Verify the value added by each layer, ensuring the whole system outperforms the sum of its parts

Common Mistakes to Avoid

• Don’t assume more complex architecture is always better—start simple, increase complexity as needed
• Don’t neglect interface design between layers—clear data flow is crucial
• Don’t underestimate the application of traditional software engineering principles in AI systems—modularity, testability, and documentation are equally important

One-Page Overview: Core Points of LLM, RAG, and AI Agent

For readers needing quick reference, here’s a condensed version of this article’s key information:

Core Concepts

• LLM = AI’s Thinking Brain (reasoning, language capabilities)
• RAG = AI’s External Memory (knowledge retrieval, fact enhancement)
• AI Agent = AI’s Action Capability (goal planning, autonomous execution)

Application Scenarios

• LLM Only: Writing, summarization, explanation, translation, and other pure language tasks
• LLM + RAG: Document Q&A, technical support, scenarios requiring accurate domain knowledge
• Full Stack (LLM + RAG + Agent): Automated workflows, complex problem solving, tasks requiring autonomous action

Design Principles

• Layered Architecture: Separation but collaboration of thinking, memory, and action capabilities
• Progressive Enhancement: Start simple, add capability layers as needed
• Clear Interfaces: Ensure clear data and control flow between layers

Value Proposition

• LLM provides intelligent foundation
• RAG ensures accuracy and timeliness
• AI Agent enables automation and proactivity

Frequently Asked Questions (FAQ)

1. How is the LLM knowledge cutoff problem solved?

By integrating RAG systems, LLMs can access external data sources for current information without model retraining. RAG acts as the bridge between LLMs and real-time knowledge, effectively solving the static knowledge problem.

2. How does RAG improve answer accuracy in AI systems?

RAG retrieves real documents as context for generating answers, enabling LLMs to produce content based on concrete evidence rather than relying solely on internal memory. This significantly reduces “hallucination” problems and provides answer source traceability.

3. What is the fundamental difference between AI Agent and ordinary LLM applications?

Ordinary LLM applications are primarily question-answering—responding to user queries; while AI Agents are goal-oriented—autonomously planning and executing multi-step tasks without user intervention at each step. Agents possess proactivity and persistence.

4. In what scenarios should the LLM + RAG combination be used?

When tasks require combining LLM’s language capabilities with domain-specific knowledge or real-time information, such as technical support systems, Q&A based on internal documents, academic research assistants, and other scenarios requiring accurate factual support.

5. How to start building AI Agent systems?

Begin by clarifying the Agent’s goals, then define the tools and actions it can use, design the control loop (goal-planning-execution-reflection), and finally integrate LLM for reasoning and RAG for knowledge access.

6. What is the biggest advantage of combining all three?

Creating systems with complete intelligent capabilities—able to think (LLM), access knowledge (RAG), and take action (Agent). This combination enables AI systems to handle complex, dynamic tasks in the real world.

7. Does integrating RAG require retraining LLM models?

No. RAG works by retrieving external information and providing it to the LLM as context, without modifying the LLM’s parameters. This means knowledge can be easily updated without expensive retraining processes.

8. Can AI Agents work independently without LLM and RAG?

Theoretically yes, but in practice AI Agents typically rely on LLM for reasoning and decision-making, and RAG for accurate knowledge. Without these components, Agent action capabilities would be severely limited, making it difficult to handle tasks requiring intelligence.