🧠 AI Agent Series (Part 1): Decoding AI Agents, MCP, LLM, and RAG

🤖 What is AI?

Artificial Intelligence (AI) is the technology that enables computer systems to perform “intelligent” tasks. These tasks were previously only achievable by humans, such as understanding language, recognizing images, path planning, playing chess, and even creating content. With the rapid development of machine learning and deep learning technologies, AI has become the core driving force of modern software development.

Simply put, AI is about teaching machines four key capabilities:

• Perception: Understanding the surrounding environment and input information
• Reasoning: Analyzing problems and finding logical relationships
• Learning: Improving performance through experience
• Decision-making: Taking action based on analysis results

The Evolution of AI Technology

The development of AI is like human learning, evolving from simple rule memorization to complex creative abilities:

• Symbolic AI: The earliest form of AI, like memorizing a rule manual. Computers operated according to preset logical rules and expert systems, but lacked flexibility.

• Machine Learning: Computers began to “learn by themselves.” Instead of just executing fixed rules, they found patterns from large amounts of data. This includes different learning methods like supervised, unsupervised, and reinforcement learning.

• Deep Learning: Mimicking the operation of the human brain’s neural networks. It can handle complex unstructured data such as speech, images, and natural language, greatly enhancing AI’s understanding capabilities.

• Generative AI: The currently hottest technology. It can not only understand but also create new content, such as natural language, code, images, etc.

The most attention-grabbing now are Large Language Models (LLMs), such as Google Gemini 2.5 Pro, Anthropic Claude Sonnet 4, and OpenAI GPT-4.1. They can not only understand semantics and reason but even create entirely new knowledge content. These advanced AI models are revolutionizing software development processes, bringing unprecedented productivity improvements to programmers.

🚀 What is an AI Agent?

AI Agent is an advanced application of AI technology, far more powerful than simple chatbots. In today’s software development field, AI Agents are becoming a key technology for improving development efficiency and automating workflows.

From Chatbot to Intelligent Assistant

Imagine that ordinary chatbots are like “knowledge-based customer service,” only able to answer questions. But AI Agents are more like an “all-capable digital assistant,” not only able to chat but also proactively help you get things done.

Three core capabilities of AI Agents:

1. Proactive environment perception - Can understand current situations and needs
2. Planning action steps - Knows how to solve problems step by step
3. Executing specific tasks - Actually operates tools to complete work

Working Mode of AI Agents

Think of an AI Agent as a super assistant with the following workflow:

• Understanding requirements: Precisely understand what you want through LLM
• Proactive data search: When encountering unknowns, it searches for data through RAG
• Tool collaboration: Communicates with various external tools through MCP protocol
• Task completion: Integrates all resources to automatically complete complex work

Three Core Components

AI Agent is like a three-person team:

• LLM (Brain): Responsible for thinking and understanding
• RAG (Research Assistant): Specialized in finding and organizing data
• MCP (Communication Expert): Responsible for coordinating and collaborating with external tools

🧩 The Three Core Technologies Behind AI Agents

Next, let’s dive deep into the three technological pillars of AI Agents. Each plays an indispensable role.

1. LLM: The Brain of AI Agents

Large Language Models (LLMs) are the “thinking center” of AI Agents. As the core technology of modern artificial intelligence, LLMs are responsible for three key tasks: understanding what you say, remembering conversational context, and generating appropriate responses. This advanced natural language processing capability enables AI Agents to perform complex code analysis and automated development tasks.

Think of LLM as a learned consultant:

• Can read complex natural language
• Can write various types of code
• Has logical reasoning capabilities
• Can do strategic planning

The Current Top Three LLMs

• Google Gemini 2.5 Pro - Google’s flagship model
• Anthropic Claude Sonnet 4 - A model renowned for its safety
• OpenAI GPT-4.1 - The most well-known generative AI model

Key Limitations of LLMs

Although LLMs are smart, they have an important “memory limitation” called the context window.

This is like human short-term memory:

• Can only remember a limited amount of information simultaneously
• When dealing with huge projects, might “forget” details mentioned earlier
• When information is insufficient, might “guess” or produce inaccurate responses

📌 Practical Impact: When processing large codebases or complex documents, LLMs tend to miss important information, which is why RAG technology is needed to supplement them.

2. RAG: AI Agent’s “Professional Research Assistant”

RAG (Retrieval-Augmented Generation) is the key technology that solves LLM memory limitations. This innovative AI technology combines information retrieval with generative artificial intelligence, enabling AI Agents to handle large-scale datasets and provide precise code analysis results.

RAG is like a professional research assistant:

• When LLM needs specific information, RAG proactively goes to the “library” to search for data
• After finding relevant content, it precisely provides it to LLM for reference
• Allows LLM to respond based on the latest and most accurate information

RAG’s Workflow

Imagine this scenario: You ask the AI Agent: “Please help me summarize the utils functions of this project”

RAG’s three-step working method:

1. Understanding requirements - Analyze that you want to know about “utils function summary”
2. Smart search - Search for all utils-related files and functions in the entire codebase
3. Precise provision - Organize the found code content and deliver it to LLM

Final result: LLM no longer guesses randomly but generates accurate and detailed function summaries based on actual code content.

RAG’s Value

• Breaking through memory limitations: Enables LLM to handle large amounts of data beyond the context window
• Ensuring information accuracy: Responds based on actual data, not imagination
• Dynamic updates: Can obtain the latest information in real-time, not limited by training data timeframes

3. MCP: AI Agent’s “Universal Translator”

MCP (Multi-Component Protocol) is the key bridge that enables AI Agents to communicate with the external world. This advanced communication protocol realizes seamless integration between AI systems and various development tools, making it the core technology for achieving automated software development workflows.

MCP is like a professional translator:

• Translates LLM’s “thoughts” into “commands” that external tools can understand
• Translates external tools’ “responses” into “information” that LLM can process
• Enables AI Agents to seamlessly integrate various development tools

MCP’s Communication Method

Imagine AI Agent helping you refactor code:

LLM’s inner thoughts: “I need to first understand the entire project structure, then find files that need refactoring, and finally execute refactoring and testing”

MCP converts this thought into specific commands:

1. get_codebase_summary - Get project overview
2. analyze_file_dependencies - Analyze file dependencies
3. refactor_file - Execute refactoring
4. generate_unit_test - Generate test programs
5. run_tests - Execute tests for verification

MCP’s Technical Features

Structured Communication:

• Uses standardized JSON format
• Ensures both commands and responses have clear formats
• Supports complex multi-turn conversations

Multi-tool Integration:

• CLI command-line tools
• Git version control
• IDE integrated development environments
• API calls
• File system operations

This is like equipping AI Agent with a “universal toolbox”, allowing it to truly execute actual development work, not just theoretical discussions.

🧠 How Do the Three Technologies Collaborate Perfectly?

Now that we know the three core technologies, how do they work together like a team? Let’s see through a practical example.

Real-world Case: Refactoring Utils Module

Your requirement: “Please help me refactor the utils module in the project to make the code clearer and easier to understand”

Four-step Collaboration Process

Step 1: LLM Understanding and Planning

• LLM analyzes your requirement: “Need to refactor utils module”
• Formulates action plan: “First understand current situation → Analyze problems → Execute refactoring → Verify results”

Step 2: RAG Collecting Data

• Search for all utils-related files in the entire project
• Analyze the structure and functionality of existing code
• Find dependency relationships and potential problem points

Step 3: MCP Executing Operations

• Use analyze_code_quality to check code quality
• Use refactor_functions to reorganize code structure
• Execute generate_documentation to update documentation
• Run run_tests to ensure functionality works properly

Step 4: LLM Integrating Response

• Synthesize all execution results
• Generate refactoring reports and recommendations
• Explain improvements and benefits

Vivid Analogy

This is like a professional development team:

• Project Manager (LLM): Understands requirements, formulates plans, coordinates overall situation
• Data Analyst (RAG): Deeply researches existing code, provides detailed analysis
• Technical Expert (MCP): Responsible for actual code operations and tool integration
• Finally, the project manager integrates all achievements and delivers a complete solution

Core Value of Collaboration

This trinity collaboration model enables AI Agents to:

• Understand complex requirements - Not just superficial command understanding
• Based on real data - Avoid imagination and wrong judgments
• Execute actual operations - Actually complete work, not just suggestions

🎯 AI Agent vs Chatbot: The Leap from Dialogue to Action

Many people ask: “What’s the difference between AI Agents and regular chatbots?” Let’s use simple comparisons to illustrate.

Core Difference Comparison

Practical Application Scenario Comparison

Scenario: “Help me optimize the performance of this React project”

Traditional Chatbot’s Response:

You can try the following methods:
1. Use React.memo to avoid unnecessary re-renders
2. Implement lazy loading
3. Optimize bundle size
4. Use useCallback and useMemo
[Provides general advice but cannot actually operate]

AI Agent’s Actual Actions:

1. 🔍 First use RAG to analyze your project structure
2. 📊 Execute performance testing tools through MCP
3. 🛠️ Actually modify code to implement optimizations
4. 🧪 Run tests to ensure functionality works properly
5. 📈 Generate performance improvement reports
[Not just suggestions, but actual execution]

Evolution Key: From “Saying” to “Doing”

Chatbot: Knowledge-based Consultant

• Good at answering questions and providing advice
• Like a talking encyclopedia
• But cannot actually help you complete work

AI Agent: Executive Assistant

• Not only provides answers but also executes actions
• Like an experienced development partner
• Can actually help you complete complex development tasks

This is why AI Agents are considered the next major breakthrough in AI technology - they make AI evolve from “answering questions” to “solving problems.”

🧱 Series Preview: Deep Exploration of Popular AI Agent Tools

Now that you understand the core principles of AI Agents, the upcoming series articles will take you through hands-on experience with various AI Agent tools.

Upcoming In-depth Analysis

🎯 Development Tool Category:

• Cursor: How to understand and refactor entire projects through AI
• Claude Code: Expert in multi-file reasoning and code collaboration
• GitHub Copilot: The evolved version of code auto-completion

🛠️ Framework Tool Category:

• Trae: The lightweight Agent framework most suitable for developers
• Gemini CLI: Google ecosystem’s command-line Agent
• Kiro: Emerging Agent development platform

📊 Comparative Analysis of Various Tools:

• Learning difficulty, integration convenience, functionality completeness
• Applicable scenarios and best practices
• Developer community and ecosystem

Next Article Preview

👉 “AI Agent Series (Part 2): Trae in Practice - Building Your First Development Assistant”

We’ll start from scratch:

1. Install and configure Trae environment
2. Build your first Agent
3. Integrate LLM, RAG, and MCP
4. Practical case: Automated code review assistant

🤝 Interact with Me

📌 If this article helped you:

• Feel free to share it with friends interested in AI programming
• Leave a comment telling me which AI Agent tool you’d most like to learn about in depth
• Or share challenges you’ve encountered when using AI Agents

Your feedback will help me adjust the focus of subsequent articles!