In the past two years, AI technology has been moving incredibly fast, with new buzzwords popping up one after another. Concepts like Large Language Models (LLMs), AIGC, RAG, Agents, and MCP can easily make people dizzy—and if you don’t sort them out, it’s very easy to get confused. This article aims to briefly talk about what these technologies actually are, how they work, and how they relate to each other. It’s mainly meant to help readers who aren’t AI specialists get a general understanding.
Of course, each of these technologies could easily take hours to explain in depth. Here, we’ll focus only on the core ideas and basic principles. If there’s anything I’ve explained incorrectly or missed, feel free to leave a comment below.
1. RAG Technology
RAG (Retrieval-Augmented Generation) is an AI framework that combines information retrieval (IR) with the text generation capabilities of large language models (LLMs). Simply put, it lets an LLM “look things up” before answering a question, instead of relying solely on what it already knows.
Although large models are knowledgeable and fluent, they have several inherent weaknesses:
Their knowledge can be outdated—they haven’t learned about newly released information.
When they don’t know something, they may fabricate an answer that sounds plausible (commonly known as “hallucination”).
They usually can’t tell you where their answers come from, making verification difficult.
They often lack knowledge about internal company data or highly specialized domains.
RAG is designed to address these issues. When you ask a question, it first retrieves relevant content from an external knowledge base (such as your company’s document repository). It then feeds that retrieved information—together with your question—into the language model. With this fresh and reliable context, the model can generate answers that are more accurate, grounded, and trustworthy.
2. Intelligent Agents
In computer science and AI, an “agent” essentially refers to something that can perceive its environment, make decisions, and take actions to achieve a goal. It can be software, a robot, or even a human or animal—but in AI, it usually means a software agent.
So how is this different from AIGC? AIGC mainly focuses on generation—writing text, creating images, and so on. Its core is a generative model. An intelligent agent, however, is much more powerful. It’s a complex system that can make decisions and carry out tasks on its own, combining function-calling models (explained below) with software engineering. It can interact with both the model and the outside world. Of course, agents can also use AIGC as a helper to complete specific tasks.
The most powerful capability of an agent lies in its ability to decide which external tools to use—thanks to function calling.
2.1 Function Calling
Function calling is a key capability of modern large language models. Earlier, we mentioned that RAG helps models access external knowledge—but it’s limited to retrieving information. Function calling goes much further. It allows a model to truly understand user intent, prepare structured parameters, and call external functions or tools automatically.
This means the model is no longer limited to “just talking.” It can actually do things—check the weather, send emails, perform calculations, and more.
Traditional models can’t access real-time data and often struggle with precise calculations, especially complex math. Models with function calling can query databases, use calculators or Python for accuracy, and even interact with external systems like email or smart home devices. Instead of outputting plain text, they can also return structured data (such as JSON), making integration much easier.
2.2 Agents in Practice
Function calling was first introduced by OpenAI in June 2023 with GPT-4. OpenAI set the initial standard, and others soon followed. After function calling was released, intelligent agents truly began to take off.
Agents really entered the spotlight in April 2025, when Manus launched its general-purpose agent product. By demonstrating capabilities like computer control and browser usage, it showed the public just how powerful agents could be.
In fact, the function-calling workflow itself already resembles a basic agent. The difference is that a real agent often calls the model multiple times to complete a task, with each tool choice decided by the model itself. For example, a customer support agent might have tools for checking orders, tracking shipments, processing returns, and recommending repeat purchases. If you ask, “Where is my order?”, the agent may first verify the order, then check logistics, and finally respond to you.
Agents are still in an early stage, but development is rapid. They’re already effective in specialized areas like coding (e.g., Cursor, Tencent’s CodeBuddy) and advertising. Truly general-purpose agents like Manus are still rare, but more powerful and versatile ones are likely to emerge.
3. MCP
MCP can be thought of as a “universal plug” in the AI world. It’s a protocol introduced by Anthropic (the company behind Claude) in November 2024, designed to solve the problem of how models connect to external data and tools.
Before MCP, things were messy. Every new tool or model required custom integrations, which was time-consuming and error-prone—an “M×N problem,” like needing a different plug for every appliance. Tools were also isolated from one another.
MCP changes this by defining a unified communication standard—similar to how USB-C standardized device connections. As long as tools and models follow the protocol, they can connect seamlessly, dramatically reducing development costs. That’s why major players like OpenAI, Google, Microsoft, Tencent, and Alibaba quickly adopted it, making MCP a de facto industry standard.
Many companies have since wrapped their existing APIs into MCP services. GitHub Copilot supports MCP for code generation, AWS enables agents to operate cloud resources, and mapping services like Tencent Maps, Amap, and Baidu Maps now offer MCP servers. Even cloud storage services such as Tencent Cloud COS and Baidu Netdisk can be connected via MCP.
4. Real-World Application: 3Chat.ai Customer Support Agent
In practice, intelligent agents are built by combining AIGC (generation), MCP (connectivity), and large language models into more powerful AI applications. Customer service is a perfect example of where this combination shines.
So how does an AI customer service agent differ from human agents or traditional chatbots? And how do these technologies create a new customer support paradigm?
4.1 How Questions Are Answered
Human agents rely on experience and memory, requiring training and having clear capacity limits. They may need to look things up, which slows responses and introduces errors.
Early chatbots relied on keyword matching or fixed scripts. If the keywords didn’t match, they failed—often forcing users through menus and harming the experience.
AI agent customer support, powered by large models, offers fast responses and high concurrency. Multimodal models can understand context and user sentiment, reason with knowledge bases, and produce well-grounded answers—even when questions are phrased differently.
4.2 Multi-System Collaboration
Human agents constantly switch between systems—order management, logistics, CRM—leading to inefficiency and mistakes.
Traditional chatbots usually can’t access internal systems at all.
Agent-based support connects directly to these systems. It can query multiple data sources at once, combine the results into a single response, update records automatically, and notify customers—all in one flow.
4.3 Customer Profiles and Memory
Human agents rely on memory or notes, and new staff often lack customer context.
Chatbots typically lack long-term memory.
AI agents automatically build customer profiles, storing order history, frequent questions, and purchase behavior—so future conversations start with context already in place.
AI agent customer support represents the future of intelligent service. In real-world products like the 3Chat.ai Customer Support Agent, this means understanding natural language regardless of phrasing, pulling and updating data across systems, executing actions directly, building long-term customer memory, and running workflows 24/7 without human intervention—turning customer support into a true business execution engine.
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