AI Control Framework

About

Combatting the high failure rate of AI-generated code in production, the AI Control Framework provides essential guardrails to transform undisciplined AI output into deployable, production-ready software. It tackles common pitfalls like undeclared mocks, silent interface changes, and scope creep, offering a structured approach to disciplined AI development that ensures high deployability confidence and significantly reduces rework cycles. The framework integrates seamlessly into developer workflows, guiding AI assistants and developers towards shippable code.

Key Features

• Scope Limits (configurable file and line count limits per session)
• Contract Freezing (SHA256 hash of interfaces to prevent silent changes)
• CI/CD Integration (automated checks for contracts and DRS scores)
• DRS Score (0-100 deployability metric with 13 weighted components)
• 30-Minute Mock Timeout (enforces replacement of mock data with real service calls)
• 2 GitHub stars

Use Cases

• Reducing rework and breaking changes in AI-assisted development projects
• Standardizing AI-generated code quality for production deployment
• Integrating disciplined AI development practices into CI/CD pipelines