Demystifying AIGC, Agents, and MCP: Core Concepts and How They Interact

In recent years AI technology has progressed rapidly, bringing forth many new concepts such as large language models (LLM), AIGC, multimodal AI, Retrieval‑Augmented Generation (RAG), agents, and the Model Context Protocol (MCP). This article offers a brief, conceptual overview to help non‑AI developers build a basic understanding of these technologies.

AIGC

AIGC (AI Generated Content) refers to the automatic creation of text, images, audio, video, and other media using AI models, especially large models like GPT and Stable Diffusion. The launch of ChatGPT (based on GPT‑3.5) on 30 Nov 2022 sparked a surge of interest in AIGC.

1.1 Multimodal Technology

Single‑modal : processes only one type of data, e.g., text‑only models like GPT‑3.5 or image‑only models for recognition.

Multimodal : can handle two or more data types simultaneously, such as understanding images and accompanying text, or even combining audio and video for integrated analysis and generation.

1.2 RAG Technology

Retrieval‑Augmented Generation (RAG) combines information retrieval with the text‑generation capabilities of LLMs. Instead of relying solely on internal knowledge, the model first retrieves relevant passages from an external knowledge base and then generates answers using this up‑to‑date context, reducing hallucinations and improving accuracy.

Key limitations of standalone LLMs include:

Knowledge cutoff and outdated information.

Hallucinations—fabricated but plausible‑looking answers.

Lack of source attribution for verification.

Insufficient domain‑specific knowledge.

RAG was created to address these issues.

2. Intelligent Agent

An agent is an entity that perceives its environment, makes autonomous decisions, and takes actions to achieve specific goals. In AI, agents are typically software systems that can integrate with tools and external functions.

2.1 Function Call Model

Function Calling enables LLMs to understand user intent, generate structured parameters, and invoke external functions or tools, extending capabilities beyond pure text generation (e.g., weather queries, email sending, calculations).

The model was first introduced by OpenAI on 13 Jun 2023 and implemented in GPT‑4. Major models supporting Function Calling are listed below:

2.1.1 What Is a Function Call Model?

Function Calling allows the model to generate structured arguments based on user requests and automatically invoke any external function or tool, enabling real‑world interactions such as fetching weather data.

2.1.2 Workflow: Three‑Step Closed Loop

Typical workflow:

Define the function (name, description, JSON‑Schema parameters) that the developer wants the model to use.

{
  "name": "get_current_weather",
  "description": "获取指定城市的天气",
  "parameters": {
    "type": "object",
    "properties": {
      "city": {"type": "string", "description": "城市名称"},
      "unit": {"enum": ["celsius", "fahrenheit"]}
    },
    "required": ["city"]
  }
}

Model decides to call the function, generates structured arguments (e.g., {"city": "北京", "unit": "celsius"}).

Execute the function, return results, and let the model produce the final response (e.g., "北京今天25°C，降水概率30%，建议带伞。").

2.2 Agent Development Platforms

Platforms such as Coze, Dify, and Tencent Cloud Agent Development Platform allow developers to create agents without writing code, by configuring prompts, tools, and execution modules in the cloud.

2.2.3 Multi‑Agent

OpenAI released an Agent SDK (2025‑03‑11) that supports multi‑agent collaboration, allowing developers to define several specialized agents that can hand off tasks to each other, overcoming the limitations of single‑agent systems.

3. MCP (Model Context Protocol)

3.1 What Is MCP?

MCP, released by Anthropic on 24 Nov 2024, is an open standard that defines how large language models interact with external data sources and tools, acting as a “USB‑C interface” for AI applications.

3.2 Why MCP Is Needed

Before MCP, integrating multiple models with multiple tools required bespoke point‑to‑point interfaces (the “M×N problem”), leading to high development costs and fragmented ecosystems. MCP standardizes communication, turning the problem into an “M+N” model.

3.3 MCP Adoption and Future

Since its release, major companies (OpenAI, Google, Microsoft, Tencent, Alibaba, Baidu) have adopted MCP, making it an emerging industry standard. Service providers such as mcp.so, mcpmarket, Baidu’s Qianfan MCP Plaza, and Alibaba’s ModelScope MCP Plaza offer extensive plugin ecosystems, allowing developers to build agents without creating custom plugins.

Future trends include integration of MCP into AI operating systems (e.g., Huawei’s HMAF) for cross‑device scheduling, and the risk of “closed‑loop” ecosystems that could fragment standards, highlighting the need for cross‑platform collaboration.

Overall, agents orchestrate capabilities from AIGC, MCP, and LLMs to deliver more complex AI applications.