AI Control Framework

AI Control Framework

The AI Control Framework is designed to enhance the reliability and deployability of AI-generated code. With innovative features such as DRS scoring, contract freezing, and a stringent 30-minute mock timeout, this framework ensures that developers can confidently ship code without the typical pitfalls associated with generative AI coding tools like Claude Code, Cursor, and Copilot.

The Challenge

Despite the growing capabilities of AI coding assistants, common failure patterns can lead to significant deployment issues. Statistically, 95% of generative AI pilots fail to transition to production, and up to 42% of AI projects were abandoned in 2025 alone. Typical issues include unaddressed mock data, silent interface changes, and unpredictable scope creep.
The AI Control Framework targets 13 specific failure patterns, providing validated solutions to enhance code readiness before deployment.

Innovative Solutions

The framework implements several mechanisms to ensure deployability:

• Contract Freezing: Utilizes SHA256 hashing of interfaces. Any changes in contracts trigger an immediate halt to further development.
• 30-Minute Mock Timeout: Encourages real service implementations by limiting mock data usage.
• Scope Limits: Restricts the number of files and lines of code per session to maintain focus.
• DRS Score (0-100): Provides an objective measure of deployability, with a target score of 85 or higher indicating readiness.

Proven Results

The effectiveness of the AI Control Framework has been demonstrated through measurable outcomes:

Example Usage

To assess the deployability of a project, users can easily execute a series of commands:

$ ./ai-framework/scripts/check-contracts.sh
$ ./ai-framework/scripts/detect-mocks.sh
$ ./ai-framework/reference/bash/drs-calculate.sh

The output provides insights into contract integrity, behavioral contracts, and overall production readiness, ensuring that developers know when their code is ready to ship.

Expanded Functionality

The framework includes a set of commands to manage sessions efficiently:

Future Developments

Looking ahead, the AI Control Framework aims to introduce a hosted analytics dashboard to track DRS scores over time, team management features, and real-time alerts via Slack or Discord. These enhancements will help teams collaborate more effectively and maintain high-quality standards in their AI projects.

In a landscape where AI coding time is often wasted, this framework stands out as a reliable tool designed to significantly reduce development time while enhancing code quality. It transforms the uncertainty surrounding AI-generated code into a structured and manageable workflow.