SpecDD is a specification-driven development framework built to solve AI agent context loss by delivering exact, scoped specs at the moment of implementation.
Key Takeaways
The core problem: AI agents fill missing project context with training-data defaults, producing code that is technically correct but wrong for the specific codebase.
Throwing more tokens at the context window treats the symptom; SpecDD aims to give agents exactly the right context in exactly the right place.
The author draws a direct analogy to human onboarding failures: new engineers and contractors violate unwritten rules because no single current picture of the system exists.
Prior art (SRS, TDD, BDD, formal methods) each solved part of the problem but none targeted precise, task-scoped context delivery for AI agents specifically.
The author argues AI expands total software demand rather than replacing developers, consistent with historical productivity leaps from IDEs, package managers, and frameworks.
