Benchmarking AI coding agents for distributed SQL: 350 runs, 17 models
A recent benchmarking study evaluated AI coding agents for distributed SQL across 350 runs and 17 models. The findings revealed that providing a YugabyteDB skill file significantly improved the AI's ability to avoid anti-patterns and adopt positive patterns. The study emphasizes the importance of context in training AI models for specific database environments.
- ▪The study conducted 350 evaluations across 17 AI model configurations.
- ▪Adding a YugabyteDB skill file improved anti-pattern avoidance by 57%.
- ▪The results showed that the tool wrapping the model is as important as the model itself.
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Back to Blog HomeBenchmarking AI Coding Agents for Distributed SQL: What We Learned We ran 350 evaluations across every major model and tool. Here's what the data shows! Dmitry SherstobitovMay 20, 2026In part 1 of this 2-part blog series, we made a bold claim: The AI wasn’t failing because it lacked data. It was failing because it was too well-trained on the wrong database.AI models write vanilla PostgreSQL. If your database is distributed, providing the AI model with a YugabyteDB skill file closes the gap and ensures it writes code that works for your application.In this post, we break down the benchmarking results across 17 model configurations.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Yugabyte.
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