From Prototype to Production: Building Apps with GitHub Copilot Spark and Coding Agents

What is GitHub Copilot Spark?

GitHub Copilot Spark is an experimental AI-powered tool that helps developers quickly prototype web applications through natural language conversations. Think of it as your AI pair programmer that can generate entire application scaffolds, components, and features based on your descriptions. Unlike traditional coding assistants that help line-by-line, Spark can create complete, functional prototypes in minutes.

Why Copilot Spark Isn't Production-Ready (Yet)

While Spark excels at rapid prototyping, it's important to understand its current limitations:

• Limited functionality: Generated code often uses mock data instead of interacting with actual external systems
• Limited Error Handling: Generated code often lacks comprehensive error handling and edge case coverage
• Basic Security: Authentication, authorization, and security best practices aren't deeply implemented
• Minimal Testing: No automated tests are generated alongside the code
• Simple Architecture: The generated code structure is functional but may not follow enterprise patterns
• Performance Considerations: Code isn't optimized for scale or performance
• Limited Customization: Complex business logic and integrations require manual intervention

The Power of the Spark-to-Production Pipeline

Despite these limitations, Spark offers a powerful development workflow when combined with GitHub Copilot Coding Agent:

Benefits of Starting with Spark

1. Rapid Ideation: Transform ideas into working prototypes in minutes, not hours
2. Visual Feedback: See your application come to life immediately
3. Iteration Speed: Quickly test different approaches and UI designs
4. Learning Tool: Understand modern web development patterns through generated code
5. Foundation Building: Create a solid starting point that you can enhance

The Coding Agent Advantage

Once you've created your prototype with Spark, GitHub Copilot Coding Agent helps you:

• Add comprehensive error handling
• Implement security best practices
• Write unit and integration tests
• Refactor for better architecture
• Optimize performance
• Add monitoring and logging

Step-by-Step: Building a Weather App from Prototype to Production

Let's walk through creating a weather application, starting with Spark and evolving it into a production-ready app.

Step 1: Create the Prototype with Copilot Spark

Start a conversation with Spark:

"Create a weather app that shows current weather and a 5-day forecast. 
It should have a search bar for cities, display temperature, conditions, 
and weather icons. Use a clean, modern design."

Spark will generate a basic React app with:

• City search functionality
• Probably a mock call to a weather API, rather than a real API like OpenWeatherMap.
• Basic UI components
• Simple state management

Step 2: Export to GitHub Repository

Once satisfied with the prototype:

1. Click on the ellipsis (...) in the upper-right corner
2. Select "Create repository"

1. A private repository will be created for you with a name like your-github-handle/weather-forecast-app.
2. Click on the Create button.

1. When the repository is created, click on the Go to repository button, to open the repo in GitHub.

Step 3: Enhance with GitHub Copilot Coding Agent

Now, navigate to your newly created repository on GitHub and start a conversation with the Copilot Coding Agent directly in your browser. You can either create an agent task, or create an issue or PR and assign it to @copilot.

Here are some things you can do to make it production-ready:

Replace Mock Data with Real Weather API

Since Spark likely created a prototype using mock data, the first step is probably to integrate a real weather API. Ask Copilot Coding Agent to:

"Replace the mock weather data with real data from wttr.in API. 
Use their JSON endpoint format: https://wttr.in/{city}?format=j1
Update the weather service to fetch and parse real weather data."

The Coding Agent will analyze your codebase and create a pull request with changes like:

interface WttrData {
  current_condition: Array<{
    temp_C: string;
    temp_F: string;
    weatherDesc: Array<{ value: string }>;
    humidity: string;
    windspeedKmph: string;
    weatherCode: string;
  }>;
  weather: Array<{
    date: string;
    maxtempC: string;
    mintempC: string;
    hourly: Array<{
      time: string;
      tempC: string;
      weatherDesc: Array<{ value: string }>;
    }>;
  }>;
}

export const getWeatherData = async (city: string): Promise<WeatherData> => {
  try {
    const response = await fetch(`https://wttr.in/${encodeURIComponent(city)}?format=j1`);
    
    if (!response.ok) {
      throw new Error('Failed to fetch weather data');
    }
    
    const data: WttrData = await response.json();
    
    // Transform wttr.in data to our app's format
    return {
      current: {
        temperature: parseInt(data.current_condition[0].temp_C),
        description: data.current_condition[0].weatherDesc[0].value,
        humidity: parseInt(data.current_condition[0].humidity),
        windSpeed: parseInt(data.current_condition[0].windspeedKmph),
      },
      forecast: data.weather.slice(0, 5).map(day => ({
        date: day.date,
        maxTemp: parseInt(day.maxtempC),
        minTemp: parseInt(day.mintempC),
        description: day.hourly[4].weatherDesc[0].value, // Noon weather
      })),
    };
  } catch (error) {
    console.error('Error fetching weather data:', error);
    throw new Error('Unable to fetch weather data. Please try again.');
  }
};

Add Error Handling

Next, you can ask Copilot to improve the error handling in the project. You could direct the agent to improve the entire project, or focus on a small part of it. Ask the Copilot Coding Agent to:

"Add comprehensive error handling to the weather API calls. 
Include specific error cases for network errors, invalid city names, 
and API unavailability."

Other improvements

There's probably no end to the improvements you might want to make to your code base. Here are some things you could ask Copilot to do for you:

• Make the application more user-friendly by showing a spinner while fetching data, displaying clear error messages for users, etc
• Implement caching and rate-limiting threshold back-off patterns to the API calls
• Add automated tests
• Add performance monitoring for API calls, such as tracking duration, success/failure rates, etc., for analytics purpose
• Create a GitHub Actions workflow to help with automated build and deployment. While we're at it, set up deployment to something more robust, scalable, and secure than GitHub Spark. Perhaps consider an Azure Web Application, or a Kubernetes cluster, or anything in between.

Conclusion

GitHub Copilot Spark democratizes app development by allowing anyone to create functional prototypes quickly. While these prototypes aren't production-ready out of the box, they serve as excellent foundations. By combining Spark's rapid prototyping with GitHub Copilot Coding Agent's ability to enhance code quality, add tests, implement security, and optimize performance, developers can maintain the speed of initial development while ensuring the final product meets production standards.

This workflow represents the future of development: AI-assisted rapid prototyping followed by AI-enhanced productionization, allowing developers to focus on creativity and business logic while AI handles the boilerplate and best practices.