Introduction Modern .NET applications are increasingly distributed, integrating APIs, background services, and external AI systems. With the rise of AI coding tools such as GitHub Copilot and frameworks like the Microsoft Agent Framework, developers can now generate large portions of application logic. This raises a question: When AI can generate much of the code, what becomes the core responsibility of a .NET developer? This article will answer you the above question by sharing a practical case study of that shift—highlighting how architecture, contracts, and observability via Aspire - As systems become more dynamic and AI-driven, observability and orchestration become just as important as implementation. To explore this in an easy-to-understand way, I built a simple full-stack e-commerce application (“flowershop”) using: • Vue.js (frontend) Now, let’s explore! 1. System Overview This is final UI (Vue.js + AI Assistant) Figure 1. Vue.js frontend with product listing, admin form, and AI assistant. The frontend communicates with ASP.NET Core APIs, which orchestrates AI agents and external services. 2. Architecture: Orchestrating AI in .NET Sales Assistant Flow Figure 2. Sales Assistant architecture (Vue.js → ASP.NET Core API → Agent → LLM). In this architecture: Writer Flow Figure 3. Writer flow for generating product descriptions. When a product image is uploaded: The API forwards the request to an agent The agent interacts with LLM for image understanding Additional context is retrieved A final description is generated Observability and Orchestration with .NET Aspire Figure 4. .NET Aspire tracing of LLM interactions and tool calls. Using .NET Aspire, I was able to: Trace full request flows: Vue.js → API → Agent → LLM → Tool calls Inspect tool usage: GetFlowerDetails PlaceOrder SearchFlowersByOccasion Monitor: Latency Token usage Execution paths This is essential because: 3. Implementation Challenges Ambiguous Specifications AI requires structured and explicit instructions. Loss of Control Effort shifted from writing code to reviewing and testing it manually. Debugging Complexity AI-generated logic was difficult to trace and fix. AI accelerates generation, but not understanding 4. Evolving the Development Approach Structured Issue Definition API Contract Design Contracts become critical when AI generates both sides of a system. Instruction and Agent Design Figure 3: Setup GitHub Copilot in project Continuous Learning 5. Key Lessons for .NET Developers 6. Discussion: The Shift in Developer Responsibility AI does not remove responsibility—it redistributes it to a higher level. Key questions remain: • Which responsibilities should remain human-controlled? 7. Conclusion AI-assisted development in .NET is not just about generating code—it is about building systems that integrate AI reliably. Success depends on: • Clear architecture 8. Source Code GitHub Repo 9. References GitHub, Inc. 2026. About issues. GitHub, Inc. 2026. Creating an issue Microsoft. 2026. API design best practices OpenAPI Initiative. 2023. OpenAPI Specification. Copilot Academy. 2025. Copilot customization workshop. GitHub, Inc. 2026. Prompt engineering for GitHub Copilot. Microsoft. 2026. Agent Framework Microsoft. 2026. Responsible AI overview