In software development, knowing how to write clean code is important. Knowing when to stop improving it is even more critical. Refactoring can quickly turn from a healthy practice into a costly obsession, slowing teams down and putting projects at risk. In this post, I share lessons from real-world experience on why “good enough” is often the smartest choice, how software lifespan should guide refactoring decisions, and when polishing code truly pays off.
90% of code gets written once and rarely gets a second chance. Yet we keep hoping refactoring will save us. What if the real leverage isn’t in the code, but in us? This article explores first-draft excellence: how to write resilient, high-quality code from day one with possibly one attempt, and evolve yourself as the developer who makes it possible. Stop relying on “later fixes.” Start building smarter, from the first line.
Many engineering teams put great effort into perfect technical decisions, yet overlook one factor that quietly shapes the long-term health of the team. How decisions are shared, who gets to participate, and how much room people have to learn through experience can determine whether a team stays active or slowly becomes passive. In the article below, I explore why relying on a small group of decision makers harms growth, how thoughtful participation builds stronger engineers, and why small risks often create big returns. I hope it resonates with anyone working to build a healthier and more engaged software team.
As AI coding agents become standard tools in 2025, many engineering teams observe a subtle but widespread pattern: the more features an agent implements autonomously, the less confident developers feel about manually modifying the codebase. This phenomenon, often called intervention paralysis, arises because incremental AI contributions gradually reduce the human maintainer’s mental model of the system. Over time, the growing gap between “what the code should do” and “how it actually works” creates significant psychological friction against refactoring or direct intervention. In this article, I examine the mechanisms behind this erosion of ownership and present a practical mitigation strategy used successfully at scale: scheduled human-led refactoring checkpoints combined with component-level agent sandboxing. The approach is straightforward. Pause new feature work after every n AI-added capabilities, invest m focused day in architectural cleanup, and thereafter restrict agent prompts to single, well-bounded modules.
