Make Large-Scale Refactors Easy with Parallel AI Agents

Large-scale refactors used to be a nightmare. The kind of work that makes you question your career choices. But I just completed a cross-cutting refactor across a modular monolith using 15 AI agents working in parallel, and it changed how I thought about code maintenance. Here's how I turned a week-long slog into a coordinated distributed AI collaboration. The Pain Here's a real example from a recent project. I had a modular monolith with vertical slice architecture - Timesheets, Projects, Billing, Notifications - all nicely separated. Clean boundaries. Good separation of concerns. Then I noticed an inconsistency: TimeEntry.Stop() was throwing exceptions for error cases, while FlowTask methods were using the Result<T> pattern. Same codebase, two different error handling approaches. The fix was straightforward: Convert TimeEntry.Stop() from exception-based to Result<Success> Update StopTimerEndpoint.cs to handle Result instead of try-catch Do the same for Projects, Billing, and Notifica