AI coding agents have created a new paradigm in software development, yet practitioners largely operate without systematic methodology: either using AI for ad-hoc assistance (”vibe coding”) or dismissing it as unsuitable for production work. This paper introduces command coding, a structured methodology that applies traditional software engineering management principles, such as architecture, planning, iterative review, and automated quality gates, to the orchestration of AI coding agents. Through an empirical case study of all2md, a production-grade document conversion library supporting 40+ formats ( 88,000 code-lines) completed by one senior engineer in 41 working days, we show via triangulated estimation (COCOMO II and Function Point Analysis) that command coding can achieve 25-40× acceleration over traditional development timelines while maintaining production-quality standards. Command coding amplifies existing expertise rather than replacing it. The methodology requires senior-level skills in architecture, security, and code review, shifting the developer’s role from implementer to orchestrator. We propose “contextual quality contagion” as a key mechanism in AI-assisted development affecting code quality: LLMs generate code that matches the quality patterns of their context, creating feedback loops wherein early code quality decisions compound throughout development. This finding suggests that investment in early code quality yields higher returns in AI-assisted projects than in traditional development. Furthermore, the use of automated tooling (linters, type checkers, static analyzers, etc.) within agentic development cycles is critical to avoiding contextual quality contagion.

@inproceedings{paperbot12190,
  title = {Command Coding: A Methodological Framework for AI-Accelerated Software Development},
  author = {Thomas S. Villani and Martin Kellogg},
  booktitle = {ASE},
  year = {2026},
  doi = {10.1145/3832783.3834536}
}