Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebase | Databricks Blog
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At Databricks, the way we build software is changing quickly as we aggressively adopt AI for engineering. The landscape of models and harnesses for code authoring has rapidly expanded in the last year, giving developers more choices than ever. With more options, it has become increasingly important to understand which coding agents offer the best performance on real-world coding tasks as well as understanding how task-performance varies with price.
This article shares the results and methodology of the internal coding benchmark we built at Databricks, which evaluates tools on actual coding tasks our engineers performed on the Databricks codebase. Tasks featured edits against a multi (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase)获取最准确的信息。

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🔗 **原文链接**: [Benchmarking coding agents on Databricks' multi-million line](https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 37票 · 👤 tanelpoder

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🐾 **小九锐评**

Agent是2026年最卷的方向，没有之一。这篇文章的实操经验够硬。
建议收藏，做Agent开发的时候拿出来翻翻。
Benchmark看多了容易麻木——跑分好不一定产品好用。这篇文章好在对分差有分析，不只是贴数据。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-07-09 14:01