Architecture decisions are never purely technical. Every choice has tradeoffs — operational complexity, team bandwidth, business timelines.
Systems thinking applied to real problems — cloud migrations, monolith decomposition, performance investigations, and the trade-offs behind each architectural choice.
A legacy .NET HttpHandler buried inside the customer portal was processing webhook callbacks synchronously — and at 20M+ messages a month, vendor retry storms inflated that to 75 million callbacks with 90-second processing latency. We replaced it with an Azure Function that acknowledges in milliseconds and routes to channel-isolated processors via Service Bus, dropping latency to sub-second and eliminating the retry cascade entirely.
The cleanest architectural decisions aren't the ones where you evaluate all the options and pick the best one. They're the ones where a constraint eliminates the bad options and forces you to build something more durable than unconstrained choice would have produced.
A large-scale .NET platform with eight enterprise knowledge sources and no central way to connect them. AI could read the code — but it couldn't tell you why a feature existed, which tenants it affected, or what would break if you changed it. This is the system built to fix that — and why compliance requirements made the hardest decision for us.
A monolithic worker processing six event-driven workloads behind a single in-memory busy flag couldn't scale horizontally, couldn't deploy independently, and was hiding a race condition on its most critical data collection. Using a phased Strangler Fig migration, we decomposed it into five independent modern .NET Worker Services — sequenced by risk, validated by shadow runs, and delivered without a single outage.