Enforcement-Accelerated Development
AI generates code faster than humans can review it. Architectural drift compounds invisibly. Tests pass. Systems break.
EAD solves this.
The Problem
At 15,000 lines of AI-generated code, our production system approached collapse. UUID handling implemented five different ways. Type contracts fractured. Documentation degraded. Everything passed tests.
Code review missed it. Manually verifying architectural consistency at AI speeds exceeds human cognitive capacity.
The Solution
If an architectural rule can be stated objectively, it can be enforced automatically.
EAD extends TDD with three pillars:
Enforcement Tests: Automated verification of system-wide structural rules. AST parsing catches violations at commit time. Not code review time. Not production.
Context Sharding: Hierarchical decomposition into reviewable chunks. ~500 lines preserves both human review capacity and AI reasoning headroom during debugging.
Evidence-Based Debugging: Logging with exact file:line locations. No speculation. Direct navigation to error source.
Results
Production case study: 150,000 lines of Python. 51,513 production LOC. 98,536 test LOC. 3,700+ passing tests. Zero measurable architectural drift after enforcement.
The same AI that produced collapsing code at 15k lines produced production-quality code at 150k lines. The methodology changed. The AI didn’t.
Learn More
- Whitepaper: The full methodology with empirical validation (Zenodo DOI for citations)
- EAD LangChain Template: Production-ready Python template. The template is the lesson.
- Robocurse: PowerShell replication with VSS. Built with EAD.