Benchmarking AI Coding Agents for Distributed SQL: What We Learned

In a comprehensive analysis involving 350 evaluations across 17 AI model configurations, the study identifies significant insights into enhancing AI coding agents for distributed SQL environments, such as YugabyteDB. The primary discovery is that AI models trained on standard PostgreSQL often fail in distributed contexts due to a lack of specific contextual knowledge, rather than a deficiency in data. Incorporating a YugabyteDB skill file drastically improves performance, particularly in avoiding anti-patterns like UNLOGGED TABLE and SERIAL PKs, by injecting relevant context at inference time. The evaluation highlights the importance of the tool wrapping the model, showing that it can significantly impact performance, sometimes more than the model version itself. Findings also indicate that the skill file’s effectiveness varies with the nature of the task, as procedural patterns requiring control flow tend to regress when only text rules are provided, underscoring the need for working code examples. Additionally, the study advises a two-layer approach for skill files: a universal skill for general database patterns and a project-specific skill for workload-specific guidance, ensuring models avoid silent failures and apply learned strategies effectively.