For over a decade, my identity as a software engineer was tied to the act of creation through code. I spent the better part of those ten years building backend systems where the “magic” resided in the ritual: receiving a set of requirements, dissecting them in my mind, and the methodical process of translating that into code. I took pride in the constant cycle of learning new libraries, mastering emerging techniques, and the tactile process of manually crafting every function and data structure. There was a profound sense of ownership in knowing the lineage of every line in the codebase.

That paradigm has shifted.

The transition happened gradually, then all at once. I moved from using AI as an autocomplete tool to delegating the entire initial phase of requirements digestion to the models. Instead of spending hours internalizing a specification, I now provide the context to an agent and begin an interactive cycle. We reason together. The AI generates the skeleton and the logic, and I function as the reviewer and director. It is a process of refinement rather than raw construction.

I will be honest: when this approach became the industry standard—roughly mid 2025—I felt a deep sense of frustration. It felt as though the “magic” of the profession had been buried under a layer of automation. The joy of solving a complex algorithmic puzzle or optimizing a hot path manually felt diminished when a model could produce a viable solution in seconds. For a time, it felt like the craft I had spent a decade mastering was being reduced to a supervisory role.

However, as I reflect on where we are now in early 2026, I have found a new kind of joy in the profession. The challenge has not disappeared; it has simply moved up the stack.

The magic hasn’t died; it has evolved. Today, the thrill lies in building and managing teams of AI agents. It lies in the architecture of the “orchestration layer”—connecting MCP (Model Context Protocol) servers to give agents the tools they need, dominating the complex flow of agentic workflows, and understanding the nuances of different models. We are no longer just writing instructions for a machine; we are designing systems of intelligence that can act autonomously within the constraints we define.

This is a division point in time. The era of the solo craftsman writing every line is giving way to the era of the orchestrator. My years of experience haven’t become obsolete; they have become the foundation upon which I judge the reasoning of these agents and architect the environments they inhabit.

The craft is different, but the mission remains the same: building systems that work.

Yet, a question remains: where will it end? As the abstraction layers continue to stack and the pace of development accelerates, we have to wonder what the ultimate form of this profession looks like. For those of us who started in the manual era, the challenge isn’t just about adapting—we’ve already shown we can do that—it’s about identifying where the new boundaries of “engineering” will be drawn. As we move from being builders of components to architects of intent, the destination of this decoupling remains our most interesting unknown.