Over the past two years, AI technology has been rapidly advancing, with new terms popping up one after another. Concepts like large language models (LLM), AIGC, RAG, Agent, and MCP sound dizzying, and it’s easy to mix them up without a clear understanding. This piece aims to simply discuss what these technologies are, the principles behind them, and how they relate to each other. The goal is to help those who are not specialists in AI get a rough idea.

Of course, each technology can be explored in depth for half a day, but here we will focus on the key points to discuss concepts and principles. If I miss anything or get something wrong, feel free to leave a message below.

1. RAG Technology

RAG (Retrieval-Augmented Generation) technology is an artificial intelligence framework that combines information retrieval (IR) with the text generation capabilities of large language models (LLM). Simply put, it is to have the large language model (LLM) check a database before answering a question. Unlike before, it does not rely solely on what it has in its own head.

Think about it: while the large model seems to understand a lot and can articulate well, it has several shortcomings:

1. It may know outdated information and hasn’t learned about new developments.

2. When confronted with something it doesn’t understand, it may sometimes generate a seemingly plausible answer (commonly referred to as "hallucination").

3. It usually can’t tell you where its answers come from, making it hard to verify.

4. In matters specific to your company or a particular field, it may not be well-informed.

RAG is designed to address these issues. Its solution is: when you ask a question, it first checks an external knowledge base (such as your company’s document library) and retrieves relevant information. Then, it presents this information, along with your question, to the large model. This way, the model can provide a more accurate and reliable answer based on this fresh and relevant context.

Image source: Google Images

2. Intelligent Agent

The term “intelligent agent” in the computing and AI circles simply refers to a system that can assess situations, make decisions, and take actions to achieve goals. It can be software, a robot, or even a person or animal, though in AI, it primarily refers to software agents.

What is the biggest difference between it and AIGC? AIGC mainly focuses on generation (such as writing text or creating images), with the core being a generative model. But an agent is much more sophisticated; it is a complex system that can make decisions and perform a variety of tasks, integrating the “Function Call” model (which will be discussed below) and elements of software engineering. It can handle the exchange of information between the model and the outside world. Certainly, agents can also leverage AIGC as an assistant to complete specific tasks.

Image source: Google Images

The most impressive capability of an agent is its ability, thanks to the “Function Call” model, to decide which external tools to use to accomplish a task.

2.1 Function Call Model

The “Function Call” is a key technology of large language models. It was previously mentioned that RAG technology addresses the problem of models not being able to use external data, but RAG only allows models to check materials in knowledge bases. The “Function Call” differs in that it enables the model to understand the true meaning of your words, automatically prepares structured parameters, and calls any external function or tool. This breaks the limitations of merely being able to converse and allows for practical tasks, such as checking the weather, sending emails, or performing calculations.

Function Call Model

Traditional large models can’t check for the latest information and must rely on what they've previously learned; but models that support function calls can actively search for information in a database. When it comes to precise calculations, traditional models are prone to errors, especially with more complex mathematics; but models that support function calls are much smarter—they will seek assistance from a calculator or Python. Interacting with external systems, such as sending emails or controlling smart home devices, is something traditional models cannot handle, but those that support function calls can manage it well. Finally, while traditional models produce plain text that is difficult to use directly, function-call-enabled models can provide you with structured JSON formats that are much more convenient to use.

2.2 Agent

This function call model was first developed by OpenAI in June 2023 and was initially applied in GPT-4. OpenAI leads in this field, setting the API standards that others followed. After releasing the Function Call, intelligent agents truly began to develop. The widespread attention on agents came when Manus released its general-purpose agent product in April 2025, which demonstrated the capabilities of “computer operation” and “browser usage,” showcasing the power of agents for the first time.

Steps for understanding user goals

In fact, the function call process illustrated in the previous image is a rough model of an intelligent agent. The difference is that a true intelligent agent might call the model multiple times to complete a task. Each time, it decides which tools to use based on the model’s assessment itself. For example, if there is a customer service agent, it might have tools for checking orders, logistics, processing returns, and recommending repurchases. If you ask it, “Where is my purchase XX?”, it might first confirm the order details, then check logistics for the location, and finally respond to you.

Logic of response from intelligent customer service

Currently, intelligent agents are still in their early stages but are advancing rapidly. For specialized fields like coding (such as Cursor or Tencent’s CodeBuddy) and advertising, they are already seeing useful applications. However, general-purpose intelligent agents that can handle any task, like Manus, are still quite rare. It is estimated that better and more versatile intelligent agent applications will certainly emerge in the future.

3. MCP

The MCP protocol can be understood as the “universal plug” in the AI world, developed by Anthropic (the company behind the Claude model, whose founder previously worked at OpenAI) in November 2024. It primarily addresses the issues of how large models connect to external data and tools.

Before this regulation, the situation was chaotic. Developing an intelligent agent required creating a new set of interfaces every time a new tool was added or a different large model was used, which was labor-intensive and prone to errors. This is akin to having to make ten different plugs for connecting ten appliances (this is known as the “M×N problem”). Furthermore, tools operated independently, making it impossible to use Excel and a database simultaneously.

Once MCP arrived, the situation changed. It established a unified communication protocol, similar to standardizing all appliances to use the same type of socket (think USB-C). Now, as long as tools and models adhere to this standard, they can be directly used, significantly reducing development costs. Consequently, once the protocol was published, major companies like OpenAI, Google, Microsoft, Tencent, and Alibaba quickly followed suit, making it effectively the industry standard.

Big companies have also repackaged their original API services as MCP services. For instance, GitHub Copilot allows you to generate code using MCP, AWS has developed a tool for agents to directly manipulate cloud resources, and Tencent Maps, Gaode, and Baidu Maps all support MCP Server, even Tencent Cloud COS and Baidu Netdisk can connect using MCP.

MCP Services

4. Practical Applications—3Chat.ai Customer Service Agent

Intelligent agents effectively combine AIGC (for generation), MCP (for connectivity), and large language models to create powerful AI applications. Take customer service as an example—these three elements together are incredibly useful. Many readers may wonder, how do AI customer service agents differ from human customer service representatives or chatbot services? How do these technologies shape a completely new customer service paradigm? Below, I will clarify this question from different angles:

1. Approaches to Answering Questions

• Traditional human customer service relies on experience and memory for responses, which requires training costs. When encountering unfamiliar situations, they must refer to documents, which is slow and prone to errors, and there is a limit to how many questions a customer service representative can handle.

• Early chatbot services relied on “keyword matching” or “fixed scripts,” causing issues if questions didn’t match keywords, or guiding customers through menu options, which could negatively impact the experience of those seeking assistance.

• AI Agent customer service, leveraging large model capabilities, offers remarkable response speed and handling capacity, and can recognize various types of information, understand customer emotional tendencies, consider context, and use knowledge bases for reasoning. Even if the phrasing changes, it can still comprehend and synthesize existing information to provide complete answers.

2. Multi-System Collaboration Capability

• Human customer service has to juggle multiple systems—checking returns in order systems, tracking shipments in logistics backends, and updating information in CRM systems, which is inefficient and may lead to transcription errors; when faced with issues in other areas, customers must wait for transfers, further reducing efficiency.

• Regular chatbots are basically incapable of querying system data, only providing links for users to check themselves.

• Intelligent agent customer service directly connects these system interfaces. It can simultaneously access multiple data sources, check logistics and orders, compile responses into a single sentence, and automatically write results back into the system to notify customers.

3. Customer Profiles and Memory

• Human customer service relies on memory or notebooks; newcomers may not be familiar with customer histories, leading to repetitive questioning.

• Chatbots typically lack long-term memory, treating each interaction as a new conversation.

• Intelligent agent customer service automatically fills in customer profiles, storing historical orders, frequently asked questions, and purchasing habits. The next conversation can proceed directly to the point with this information in mind.

Thus, AI Agent customer service will be the trend of future intelligent customer services. Our practical product, the 3Chat.ai customer agent, will understand natural language without being hindered by varying phrasing; it will retrieve and update data across systems without manual intervention; it will execute actions based on business rules rather than merely “submitting requests”; it will accumulate customer information over time, making each interaction more efficient; and it will run processes around the clock without human intervention, truly transforming customer service into a business execution engine!

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