Regenerative Software (Developing Scalable, Maintainable Systems with AI)

The assumptions underneath modern software development have quietly broken. Version control assumes humans make changes incrementally. Code review assumes a human authored the code. Testing assumes you can reason about what changed. When AI generates, modifies, and regenerates code autonomously, these abstractions don't just strain—they stop making sense.

Regenerative Software by Chad Fowler introduces a new model built for this reality. Rather than maintaining codebases indefinitely, this new model treats code as a derived artifact. With practical frameworks for provenance tracking, selective regeneration, and agent-driven architecture, this is the architectural reference the industry has been waiting for since AI became the primary author of code.

• Design systems where code is a derived artifact, not a permanent accumulation of decisions
• Implement provenance tracking that replaces version control as the source of truth
• Build architectures where AI agents operate as first-class developers
• Evaluate trade-offs between regeneration and stability across production systems
• Introduce regenerative principles incrementally into existing codebases