AI Development Rules Needed Regression Tests Too
I had already reached an uncomfortable conclusion: Giving an AI coding agent better instructions was not enough. So I started building repository-level governance around things like source-of-truth ownership, review behavior, evidence, scope, and lifecycle. The repository became more consistent. The agents behaved better. And then I ran into another problem. How did I know the rules were actually…
The project AIDDSkeleton demonstrates that simply providing an AI coding agent with better instructions is not sufficient for optimal performance. To address this, the author implemented repository-level governance around concepts such as source-of-truth ownership, review behavior, evidence treatment, scope management, and lifecycle control. This governance system resulted in more consistent repository behavior and improved AI agent performance.
However, the author faced a challenge in determining whether the rules themselves were responsible for these improvements or if other factors, such as prompt variations or the agent's prior context, played a role. To overcome this, they began conducting regression tests on the rules themselves. The project's structure allowed them to treat previous commits as fixed fixtures, enabling them to compare the agent's behavior under different governance configurations.
The author's initial regression tests showed promising results, with the agent generalizing and finding related problems effectively. However, a bug in the testing process prevented them from accurately determining the impact of the governance changes. By freezing a historical repository state as a control and applying candidate governance to another repository, the author was able to isolate the effects of the changes and observe more accurate behavioral evidence.
This approach revealed that while the new governance improved the agent's generalization capabilities, it might have hindered its immediate exploration breadth in certain scenarios.
The author concluded that simply reading the governance repository was insufficient to validate its effectiveness. Instead, they emphasized the necessity of observing the agent's behavior within a repository that utilizes the governance rules. Their findings highlight the importance of rigorous regression testing and careful analysis when developing AI coding agents, ensuring that the rules themselves are responsible for the observed improvements and not other external factors.
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