Ensures AI-generated code is production-ready by enforcing quality guardrails and deployability standards.
Sponsored
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.
主要功能
01Scope Limits (configurable file and line count limits per session)
02Contract Freezing (SHA256 hash of interfaces to prevent silent changes)
03CI/CD Integration (automated checks for contracts and DRS scores)
04DRS Score (0-100 deployability metric with 13 weighted components)
0530-Minute Mock Timeout (enforces replacement of mock data with real service calls)
062 GitHub stars
使用案例
01Reducing rework and breaking changes in AI-assisted development projects
02Standardizing AI-generated code quality for production deployment
03Integrating disciplined AI development practices into CI/CD pipelines
