the Sourcerers Orchard

  In the first two articles in this series, I argued that a choice is hidden in the current move toward AI-driven software development. We can use increasingly capable AI to remove developers from implementation: humans describe what to build, agents produce it, and humans review the result. Or we can use the same capabilities to make the development loop itself more powerful, keeping humans and AI involved in exploration, implementation, feedback and learning together. The second article proposed the latter as a synthesis between Agile development and Spec-Driven Development. AI would become more like a member of the engineering team, while specifications, code, and persistent AI memory would evolve together as the team learned. That sounds nice in an article. Now I want to see if it actually works. The tools don't naturally work this way There is an immediate practical problem. Most coding AI tools seem to encourage one of two interaction modes. The first is autocomplete. I write...

AI Should Join the Team, Not Replace the Development Process In my previous article, I explored something that has been bothering me about emerging models for AI-driven software development. AI can make implementation extraordinarily cheap. Specification-driven development can make intent explicit, while agentic systems can take on increasingly complex engineering work. All of this is useful. The problem appears when we conclude that the natural end state is to separate human thinking from machine implementation: humans specify what should exist, while an AI production system turns that specification into software. That may be efficient, but I don't think it is the only future available to us. Another possibility is to  make AI a member of the development team. The vocabulary behind the model A few terms are useful before describing the alternative. Spec-Driven Development (SDD) An approach in which structured specifications make intent, constraints and expected behaviour explicit ...