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Three modes of AI big model: Embedding, Copilot and Agent in-depth analysis

Written by

Iris Vance

Updated on:June-18th-2025

The rapid development of artificial intelligence technology has spawned a variety of large-model application modes, among which Embedding, Copilot and Agent, as the three major mainstream directions, are reshaping the boundaries of human-machine collaboration.

In Newton's opinion, these three modes represent different depths of human-machine collaboration, levels of intelligence, and task execution capabilities.

There are also students majoring in computer science and electronic information who often communicate with Newton. They hope to engage in AI-related work in the future, so they must understand these three AI application models.

Today, Newton will analyze the core characteristics, application scenarios and future trends of these three models for your reference.

Embedding mode: invisible intelligent assistant

1. Definition and characteristics

The Embedding mode is the primary form of large model application. Its core is to seamlessly embed the capabilities of the large model into the existing system or business process, providing auxiliary support as an " invisible assistant ".

In Embedding mode, AI does not interact directly with users, but enhances the intelligence level of the system through background services.

For example: the personalized recommendation system of e-commerce platforms and the keyword matching function of intelligent customer service are both typical applications of the Embedding model.

2. Working principle and process

The key to the Embedding model lies in data preprocessing and model customization. The specific process includes:

Data preprocessing : Data cleaning and labeling for specific scenarios (such as user behavior data, domain knowledge base).

Model fine-tuning : Optimize the parameters of large models based on domain data to adapt them to specific tasks (such as recommendation algorithms or semantic search)

API integration : embedding model capabilities into existing systems through interfaces (such as customer service robots automatically replying to high-frequency questions)

User interaction : Users trigger model functions through the front-end interface without being aware of the existence of AI.

3. Application scenarios

Intelligent customer service : automatically handles high-frequency issues (such as order inquiries and refund processes) and reduces manual intervention.

Personalized recommendations : Generate recommendations based on user history (such as products, articles, videos)

Intelligent Search : Combines semantic understanding to optimize the relevance of search results, going beyond traditional keyword matching.

4. Advantages and limitations

The advantages of the Embedding model are low barriers and high compatibility, allowing enterprises to quickly improve efficiency without having to restructure existing systems.

Its limitation is that AI exists only as a " tool " and cannot make proactive decisions or solve problems creatively.

Copilot mode: the golden partner of human-machine collaboration

1. Definition and characteristics

Copilot mode marks a new stage in human-machine collaboration, with the large model acting as a “ co-pilot ” and forming a dynamic, complementary partnership with humans.

In Copilot mode, AI proactively provides suggestions, generates content, or assists in decision-making, but the final judgment is still in the hands of humans.

For example: Our daily chats with DeepSeek are in Copilot mode; GitHub Copilot provides developers with code completion suggestions, and users can choose to accept or modify the suggestions.

2. Working principle and process

The core of the Copilot model lies in real-time interaction and collaborative optimization. The specific process includes:

Task input : Users put forward requirements through natural language or interactive interface (such as " write a Python code to implement sorting ")

Model Response : Large models generate suggestions (such as code snippets, document drafts) and provide real-time feedback.

User correction : Users adjust or optimize results based on suggestions (such as modifying code logic, supplementing content)

Iterative completion : The task is gradually refined through multiple rounds of interaction.

3. Application scenarios

Content creation : Generate copy and correct grammatical errors (such as marketing copy and academic papers)

Programming development : code completion, debugging suggestions (such as GitHub Copilot)

Design optimization : adjust the design according to user needs

4. Advantages and limitations

The biggest advantage of the Copilot mode is that it enhances human creativity. With real-time assistance from AI, users can complete complex tasks more efficiently.

Its limitation is that AI's suggestions depend on the quality of user input, and if user needs are not clear, it may lead to output deviation.

Privacy issues (such as the risk of code leakage) also need to be resolved through means such as private deployment.

Agent mode: intelligent agent that makes autonomous decisions

1. Definition and characteristics

The Agent mode is the highest form of large model application, which gives AI a high degree of autonomy, enabling it to independently decompose tasks, plan steps and call tools to achieve goals.

In the agent mode, AI is not only an " assistant " but also an " intelligent agent " with closed-loop execution capabilities.

For example: users only need to enter " plan a three-day trip to Beijing ", and AI can automatically book hotels, arrange itineraries, and adjust plans according to the weather.

2. Working principle and process

The core of the Agent model lies in autonomous planning and dynamic execution. The specific process includes:

Goal setting : User enters a high-level goal (e.g., “ design a marketing plan ”)

Task decomposition : AI automatically decomposes tasks (such as market research, budget formulation, content creation)

Tool calls : connecting to external services or APIs (such as data analysis tools, design software)

Autonomous execution : completing tasks through multiple rounds of reasoning and environmental feedback (e.g. adjusting plans based on user feedback)

Result delivery : Output complete solution (such as marketing plan, budget list)

3. Application scenarios

Automated customer support : independently handle complex inquiries (such as answering user questions through multiple rounds of dialogue)

Supply chain scheduling : Analyze inventory and transportation data in real time and optimize logistics routes.

Internal enterprise services : Automatically execute employee requests (such as expense review and approval, meeting scheduling)

4. Advantages and limitations

The advantages of the agent model lie in its efficiency and autonomy, and it can replace humans to complete standardized, high-frequency tasks.

Its limitation lies in the high reliance on AI training data and algorithms. If the model contains bias or loopholes, it may lead to incorrect decisions.

Complex tasks still require human supervision to ensure the accuracy of the results.

Comparison of three models and future trends

1. Progressive intelligence

From the " toolization " of Embedding to the " collaboration " of Copilot, and then to the " autonomy " of Agent, the three models reflect the evolution path of AI from auxiliary tools to independent agents.

2. Differences in applicable scenarios

Embedding : Suitable for scenarios that require a slight improvement in efficiency (such as recommendation systems)

Copilot : Suitable for scenarios that require creativity and flexibility (such as content creation)

Agent : Suitable for standardized, high-frequency scenarios (such as customer service, supply chain management)

3. Future Trends

With breakthroughs in multimodal understanding, complex reasoning and autonomous decision-making technologies, the boundaries between the three modes will gradually blur.

For example: an intelligent customer service system may combine the capabilities of Embedding (recommending solutions), Copilot (generating draft responses), and Agent (automatically processing refund requests).