15 Years of .NET: From Web Forms to AI Agents

The other day I found an old USB drive with my first production project on it. ASP.NET Web Forms, .NET Framework 3.5, SQL Server 2005. The code had ViewState everywhere, postback event handlers nested three levels deep, and a DataGrid that loaded 10,000 rows without pagination. I deployed it to a Windows Server 2003 box by literally copying files via Remote Desktop.

I built that in 2009. It worked. Users were happy. The client renewed the contract.

I look at it now and cringe, but that’s the point — the fact that I cringe means I’ve grown. Here’s what 15 years in the .NET ecosystem has taught me.

The Eras (As I Lived Them)

2008-2012: The Web Forms Years

ASP.NET Web Forms tried to make web development feel like building Windows desktop apps. It worked, sort of. You could build functional CRUD applications quickly. The trade-off was that you had no idea what HTML was actually being sent to the browser. Debugging ViewState corruption was a weekly ritual.

jQuery was magic. Seriously. After years of writing inline JavaScript and dealing with browser inconsistencies, jQuery’s $('.selector').click(function(){...}) felt like the future.

What I learned: The basics of web development, SQL Server, and how to ship software to real users. Also that “it works on my machine” is not a deployment strategy.

2012-2016: The MVC Awakening

ASP.NET MVC came along and it was like someone turned on the lights. Clean separation of concerns, proper HTTP semantics, no ViewState. I could see the HTML. I could write real JavaScript. I could test my controllers.

This was also when I started paying attention to architecture. Repository pattern, dependency injection, unit testing — concepts that seemed academic suddenly became practical when you’re building systems that need to last years.

Angular.js (the original, pre-rewrite) hit the frontend and suddenly we were building single-page applications. The backend became an API. The frontend became its own project. Teams split into frontend and backend specialists. This felt revolutionary at the time.

What I learned: Architecture patterns that I still use daily. The importance of automated testing. How to lead small teams.

2016-2020: The .NET Core Revolution

.NET Core was the biggest shift in the Microsoft ecosystem I’ve witnessed. Cross-platform, open source, performant. We could run .NET on Linux. We could deploy to Docker containers. The Microsoft tax that drove companies to Java or Node.js was gone.

This is also when microservices went mainstream (and when everyone built microservices for things that absolutely should have stayed as monoliths, but that’s another blog post).

React took over the frontend. TypeScript made JavaScript bearable. DevOps went from “the ops team does that” to “you build it, you run it.” I set up my first Kubernetes cluster and felt simultaneously powerful and terrified.

What I learned: Cloud architecture (Azure primarily), containerization, CI/CD pipelines. Also that managing Kubernetes is a full-time job and you should use a managed service.

2020-2023: The Maturity Phase

.NET 5, 6, 7, 8 — each release was incremental but solid. Minimal APIs, better performance, improved tooling. The framework was mature and boring in the best way. You could focus on building products instead of fighting the framework.

I moved into technical leadership during this period. Less coding, more architecture, more people management. The tech skills that got me here were necessary but not sufficient — the new challenges were organizational.

What I learned: How to lead teams through large migrations. How to communicate technical decisions to non-technical stakeholders. That being the best coder doesn’t make you the best leader.

2023-Present: The AI Wave

And then everything changed again. GPT-3 was interesting. GPT-4 was a “wait, this actually works” moment. Suddenly we’re adding AI features to enterprise applications — semantic search, document Q&A, automated content generation.

The interesting thing: I didn’t abandon .NET for Python. I use both. The .NET backend handles the core application logic, API endpoints, authentication, data access — all the stuff it’s been great at for 20 years. Python handles the AI pipeline — LangChain, LlamaIndex, embeddings, vector databases. They talk to each other via APIs.

This hybrid approach works really well. Each language plays to its strengths.

What I’m learning: AI/ML fundamentals, prompt engineering, RAG architectures, vector databases. How to evaluate when AI actually adds value vs. when it’s just hype.

What Stayed the Same

Across all these eras, some things never changed:

Understanding the problem matters more than the technology. The flashiest tech stack in the world won’t help if you’re solving the wrong problem. I’ve seen teams spend months building microservices architectures for applications that had 50 users.

Clean code is still clean code. Whether it’s C# or Python, good naming, small functions, single responsibility — these principles haven’t changed since I started.

Shipping matters. The best architecture in the world is worthless if it doesn’t get to production. I’ve seen perfect code that never shipped and ugly code that generated millions in revenue. I prefer shipping.

People are the hardest part. Every technical challenge I’ve faced was ultimately simpler than the people challenges. Team dynamics, communication, motivation — these are the things that determine whether a project succeeds or fails.

What I’d Tell 2008 Me

Learn English better — it opens more doors than any programming language. Write things down. Don’t be afraid of failure; every bad decision teaches you something. And start a blog sooner. You’re going to wish you had 15 years of writing to look back on.

Actually, I did eventually start the blog. Only 15 years late. Better late than never.