Amanda Silver, Rong Lu, and Shayne Boyer showcase how to rapidly build and operate cloud-native, AI-powered applications using Visual Studio Code, GitHub Copilot, Microsoft Agent Framework, and Azure tools, highlighting a new era of developer productivity.

From Idea to Production: Building Intelligent Cloud-Native Apps with VS Code, GitHub Copilot, and Microsoft Agent Framework

Authors: Amanda Silver, Rong Lu, Shayne Boyer

At GitHub Universe, the team demonstrated how to transform a simple idea into a production-ready, intelligent cloud-native application—all in under 30 minutes using natural language, your code editor, and AI-driven workflows.

1. Rapid Project Scaffolding with AI

The process begins with GitHub Spec Kit, allowing you to provide a high-level prompt specifying your application’s intent, expected outcomes, and user preferences. AI generates the initial scaffolding, product specs, user stories, and technical design:

  • /constitution: Define principles like code quality and performance standards
  • /specify: Convert app description to full product spec and user stories
  • /plan: Select tech stack (e.g., Node.js on Azure Functions + React) and generate a technical design
  • /tasks: Auto-generate actionable backlog items for implementation

GitHub Copilot then supports automated code writing and task refinement within Visual Studio Code, acting as an AI-powered collaborator.

2. Intelligent Agent Orchestration

The demo proceeded by integrating intelligence into the app using the Microsoft Agent Framework and the AI Toolkit for VS Code:

  • Discover and integrate from over 200 AI models
  • Compose and visualize multi-agent workflows
  • Trace and debug agent reasoning and responses
  • Evaluate agent quality, enforce guardrails, and integrate with observability tools

An example agent combined vision and language models to analyze pet photos, exposing its full reasoning trace directly in the editor for easier debugging and transparency.

3. AI-Driven DevOps & SRE Automation

Operationalizing the application, the Azure AI Agent Evaluation SDK introduces CI for agents, where behavioral tests run on every push. The Azure SRE Agent demonstrated hands-off incident response:

  • Detected HTTP 500 errors from logs
  • Diagnosed root cause (e.g., NullReferenceException)
  • Applied mitigation (scaling services in Azure)
  • Opened and resolved GitHub issues with automated PRs containing code fixes
  • Verified recovery post-fix, closing incidents autonomously

This workflow illustrates “agentic DevOps”—AI agents managing both the build and operational lifecycle, freeing developers to focus on design, validation, and innovation.

4. The Developer’s Role in the Age of AI

Developers are now orchestrators: specifying high-level intent, guiding AI and agent workflows, and validating outcomes. The shift from writing instructions to defining intent amplifies creativity and productivity.

5. Try the Tools

Explore the full agentic developer workflow:

Key Takeaways

  • AI and agentic workflows accelerate application delivery and operations
  • Microsoft tools support the whole journey: from prompt to code, intelligence, and automated ops
  • Developer creativity is multiplied when working alongside AI and automated agents

This post appeared first on “Microsoft Blog”. Read the entire article here