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“Watching an AI Agent in VSCode automatically fix bugs, generate complete feature modules, and even proactively suggest architecture improvements, I realized that the rules of software development have forever changed.”

This is not a scene from a sci-fi movie—it is the reality every developer can experience in 2025. An AI Agent is no longer just a simple code-completion tool. It has evolved into an intelligent programming partner capable of understanding context, making autonomous decisions, and even collaborating across multiple models.

What is an AI Agent?

If traditional AI was just a “chatting tool,” an AI Agent is the assistant that can “do things on its own.”

Feature	Traditional AI	AI Agent
Capability	Can only chat, answer questions, or provide suggestions	Understands context, reasons about goals, and takes autonomous actions
Behavior	Passive — responds when asked	Active — plans and executes tasks proactively
Context Awareness	Limited or none	Deep understanding of environment and intent
Outcome	Gives advice or information	Produces tangible results or completed tasks
Role	Conversational assistant	Autonomous collaborator and executor

Core Operating Mechanism of AI Agents

An AI Agent is no longer a single model. It is a system with intent and action capabilities (an Intelligent Agent). Even using only Gemma3, it can independently manage the entire workflow from planning to generation:

• Generating code
• Writing technical documentation and README files
• Understanding requirements
• Planning project architecture

Installing the Gemma3 Model

Assuming you have VSCode, the Continue extension, and Ollama installed (if not, refer to previous tutorials for Ollama setup):

• Download Gemma3 model:

ollama pull gemma3:27b
ollama list

Check that the download succeeded and verify the model size.

• Start the local service:

ollama serve

• Quick test:

curl http://localhost:11434/api/generate -d '{"model":"gemma3:27b","prompt":"Hello"}'

If you receive a JSON response, the API is working correctly.

Installing the Continue Extension

1. Open VSCode and search for the Continue extension.
2. Open the configuration file: ~/.continue/config.yaml
3. Add the Gemma3 configuration:

name: LocalAI-Gemma
version: 1.0.0
schema: v1

models:
  - name: Gemma3 27B (Doc Agent)
    provider: ollama
    model: gemma3:27b
    roles: [chat, edit, apply]
    defaultCompletionOptions:
      temperature: 0.3

context:
  - provider: file
  - provider: code
  - provider: diff
  - provider: terminal

rules:
  - Give concise and clear answers
  - Always provide code examples if possible
  - Explain your reasoning when making architectural decisions

prompts:
  - name: generate_flask_project
    description: Generate a full Flask project structure with blueprints, models, and config
    prompt: |
      Please generate a complete Flask project structure with main app, blueprints, models, and config files.
      Explain each file and folder briefly.
      Provide the code in markdown code blocks.

mcpServers: []
data: []

AI Agent Collaboration in Practice

Example Requirement:

• “Generate a complete Flask project including app, blueprints, models, config, and README.”

Workflow Demonstration:

• Open VSCode and activate the Continue extension.
• Switch to Agent mode.
• Select the Gemma3 27B model.
• Input the prompt:

Generate a complete Flask project with blueprints, models, config, and README.

AI Agent Auto‑Generated Results

• Project structure

flask_project/
├── app/
│   ├── __init__.py
│   ├── main.py
│   └── config.py
├── blueprints/
│   ├── api/
│   │   ├── __init__.py
│   │   └── routes.py
│   └── auth/
│       ├── __init__.py
│       └── routes.py
├── models/
│   ├── user_model.py
│   └── article_model.py
├── extensions.py
├── requirements.txt
└── README.md

• app/__init__.py

from flask import Flask
from app.config import Config
from extensions import db, migrate
from blueprints.api import api_bp
from blueprints.auth import auth_bp

def create_app():
    app = Flask(__name__)
    app.config.from_object(Config)

    db.init_app(app)
    migrate.init_app(app, db)

    app.register_blueprint(api_bp)
    app.register_blueprint(auth_bp)

    return app

• app/main.py

from app.__init__ import create_app

app = create_app()

if __name__ == '__main__':
    app.run(debug=True)

• app/config.py

import os

class Config:
    DEBUG = True
    SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL', 'sqlite:///db.sqlite')
    SQLALCHEMY_TRACK_MODIFICATIONS = False
    SECRET_KEY = os.environ.get('SECRET_KEY', 'your-secret-key')

• blueprints/api/__init__.py

from flask import Blueprint

api_bp = Blueprint('api', __name__, url_prefix='/api')
from blueprints.api import routes

• blueprints/api/routes.py

from flask import jsonify
from blueprints.api.__init__ import api_bp

@api_bp.route('/test', methods=['GET'])
def test():
    return jsonify({'message': 'API test successful!'})

• blueprints/auth/__init__.py

from flask import Blueprint

auth_bp = Blueprint('auth', __name__, url_prefix='/auth')
from blueprints.auth import routes

• blueprints/auth/routes.py

from flask import jsonify, request
from blueprints.auth.__init__ import auth_bp

@auth_bp.route('/login', methods=['POST'])
def login():
    data = request.get_json()
    # Example only — replace with real authentication logic
    if data.get('username') == 'test' and data.get('password') == 'password':
        return jsonify({'message': 'Login successful!'})
    else:
        return jsonify({'message': 'Invalid username or password'}), 401

• User（models/user_model.py）

from extensions import db

class User(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True, nullable=False)
    password = db.Column(db.String(120), nullable=False)

    def __repr__(self):
        return f'<User {self.username}>'

• Article（models/article_model.py）

from extensions import db

class Article(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    title = db.Column(db.String(100), nullable=False)
    content = db.Column(db.Text, nullable=False)
    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
    user = db.relationship('User', backref=db.backref('articles', lazy=True))

    def __repr__(self):
        return f'<Article {self.title}>'

• extensions.py

from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate

db = SQLAlchemy()
migrate = Migrate(db, db)

• requirements.txt

Flask
Flask-SQLAlchemy
Flask-Migrate

• README.md

# Flask Project Template

## Description

This is a basic Flask project template with blueprints, models, and configuration. It provides a starting point for building web applications with Flask.

## Dependencies

- Python 3.6 or higher
- Flask
- Flask-SQLAlchemy
- Flask-Migrate

## Installation

1. Clone the repository:

   ```bash
   git clone [your-repository-url]
   cd [your-project-directory]
   ```

2. Create a virtual environment (recommended):

   ```bash
   python3 -m venv venv
   source venv/bin/activate  # On Linux/macOS
   venv\Scripts\activate  # On Windows
   ```

3. Install dependencies:

   ```bash
   pip install -r requirements.txt
   ```

4. Initialize the database:

   ```bash
   flask db init
   flask db migrate
   flask db upgrade
   ```

## Running the Application

```bash
flask run
```

The application will be accessible at `http://127.0.0.1:5000/`.

## API Endpoints

- `/api/test`:  Returns a JSON message indicating the API is working.
- `/auth/login`:  Simulates a login endpoint.

## Project Structure

```
flask_project/
├── app/
│   ├── __init__.py
│   ├── main.py
│   └── config.py
├── blueprints/
│   ├── api/
│   │   ├── __init__.py
│   │   └── routes.py
│   └── auth/
│       ├── __init__.py
│       └── routes.py
├── models/
│   ├── user_model.py
│   └── article_model.py
├── extensions.py
├── requirements.txt
└── README.md
```

Conclusion

An AI Agent is an intelligent system capable of autonomous thinking and action. It deeply understands human intent, plans execution steps, and can complete complex tasks. Unlike traditional AI, which passively responds, an AI Agent actively analyzes requirements, formulates strategies, selects tools, and executes concrete actions—from creating full project structures to writing functional code and generating technical documentation, ensuring the final output meets expectations.

More advanced scenarios involve multiple AI Agents forming collaborative networks, working like professional teams. Agents with different specializations—architecture reasoning experts, code implementation experts, documentation experts—coordinate efficiently, forming a high-performance workflow. This collaboration not only improves task quality but also expands the complexity of problems that can be addressed.

This full “understanding-to-execution” capability chain marks a shift from passive tools to proactive partners. AI Agents are no longer just chatbots answering questions—they are intelligent assistants that take action, revolutionizing human workflows and redefining the future of human-machine collaboration.

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