AI 写更多 PR，谁来验证？

核心内容

Ali-Reza Adl-Tabatabai 讨论 AI 生成更多、更大代码改动后，CI 和代码审核如何成为质量与速度的瓶颈。发布方介绍围绕智能体验证、失败解释、测试问题与自动修复展开。

演讲还说明团队怎样逐步授权自动批准与合并，以及智能体和程序分析如何配合。这些是受访者所介绍的方法与产品能力，尚未由本站独立测试。

Ali-Reza Adl-Tabatabai discusses how CI and code review become bottlenecks when AI produces more and larger changes. The publisher describes agentic validation, failure explanations, test issues and automated fixes.

The talk also addresses gradually authorizing approval and merging and combining agents with program analysis. These are methods and product capabilities described by the speaker, not independently tested by this site.

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编辑导览，依据原始来源 ↗，整理于 2026-10-10。中英文均由本站概括整理。内容介绍与下方原话、主张的逐条核对分开标注。

生成式 AI 导致代码提交量与单次改动体积急剧膨胀，使代码审查与持续集成成为新的效能瓶颈；唯有将传统静态程序分析（如污点分析、数据流分析）与多智能体验证脚手架深度结合，才能兼顾精度与成本，实现自动审查、自动修复与可信合流。

原话

2逐字核对过；核对了说话人和位置

now being generated, more PRs than before uh thanks to AI and bigger PRs uh all of which can have defects and so your costs in your development life cycle are kind of shifting to the right. You have a lot more code to review. You can have potential for a lot more failures in production and and so your choices are kind of tough here.

自动字幕 · 已核对Ali-Reza Adl-Tabatabai▶ 00:03:02

There's a ton of opportunity here in combining an agentic approach to validation with program analysis. So combining agents with things like u taint analysis, control flow analysis, data flow analysis, software composition analysis. So all the traditional approaches combined with agents. We think there's a ton of opportunity here and that together you can get much better precision, much better coverage

自动字幕 · 已核对Ali-Reza Adl-Tabatabai▶ 00:09:06