How specs make AI coding reliable—and redefine the manager's role
From manager back to builder
I spent years as a tech manager, living in documents and meetings while other people wrote the code.
AI coding agents changed that. Suddenly I could open an editor, describe what I wanted, and get working code in minutes. It felt like cheating, but it also felt like coming home. At the same time, it was obvious that this was not “just” a productivity hack; it was a new skill entirely.
If AI continues its current acceleration, the conventional manager—trapped in endless meetings, shuffling tasks, and chasing status updates—won’t survive. Instead, managers will orchestrate hybrid teams of humans and AI agents, pulling them deeper into architecture, specs, and system validation rather than just coordination. This shift is already routine in startups and mid‑sized companies, with big corporates piloting agentic workflows.
This post is about how I’m learning to develop with AI in that world, and why spec‑driven development has become the backbone of my workflow. I will share another post on more technical aspects of my learnings.
Ground rules for AI‑driven development
Before we get to specs, there are a few general rules that make AI development tolerable instead of chaotic.
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Keep the agent inside your workflow, not in another window.
This sounds basic, but if you generate code in a chat tab, then copy‑paste it into your repo, you’re doing manual CI for an automated age. It’s far better to run the agent where the code lives—inside your IDE or directly against the repository—so it can edit files, run tests, and see the full context.
