Building Trust in AI-Generated Code Development

The author discusses various concerns regarding trust in AI-generated code, including trust in the individuals involved in the coding process and the systems that support it. Key questions raised include trust in the ticket creator, the engineer managing the pull request (PR), the coding agent's implementation, the test suite's effectiveness, and the continuous integration/continuous deployment (CI/CD) processes. The author emphasizes the importance of fostering a culture of trust within engineering teams, suggesting that accountability for code delivery can enhance this trust. Engineers should be empowered to use their preferred methods for code production while maintaining quality standards. The author notes that while AI agents can generate code quickly, it is crucial to implement measures to prevent degradation of the codebase's quality. Recommendations for promoting accountability include establishing a clear technology strategy, utilizing deterministic tools for quality assurance, rejecting unreviewable PRs, writing test cases manually, collaborating with product teams for coherent vision, experimenting with different coding approaches, and recognizing that the end result may not always be the code itself. The author concludes by highlighting the increased volume of code and PRs in their company since the adoption of coding agents, noting that the review burden has also significantly increased.