Most conversations about AI-assisted development focus on coding. Which model writes the best code? Which IDE has the best autocomplete? Which agent can generate an entire application from a prompt?
After spending months experimenting with AI coding tools, I came to a different conclusion: The bottleneck wasn’t coding.
The bottleneck was everything surrounding coding. Planning, requirements analysis, architecture decisions, testing, code reviews, documentation, deployment preparation, and validation were still consuming most of my time. AI could generate code quickly, but turning that code into production-ready software remained a fragmented and highly manual process.
That’s when I stopped thinking about AI as a coding assistant and started thinking about it as part of a development system. This led me to build an AI harness: a structured workflow that orchestrates AI across the entire development lifecycle rather than treating code generation as an isolated activity.
The Problem with AI Coding Assistants
