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PolarDB-X uses an Agent pipeline for database defect repair

AI summary

A PolarDB-X team article describes an Agent workflow that receives alerts or tickets, gathers logs, monitoring data and other system information, reproduces defects, modifies code, adds test cases, runs regression tests, and submits the results for developer review. The team says all alerts received first-pass analysis and handling, with a first-pass coverage rate of 100%. It also says that over six months, the workflow identified and recorded more than 200 defects from over 10,000 alert-handling instances; more than 70% of the defects could be reproduced stably and were fixed by the Agent before entering code review. These operational figures are team-reported and were not independently verified in the article.

In-depth insight

The account describes an end-to-end database defect-repair workflow in which an Agent handles investigation, reproduction, coding and testing while developers retain authority to review and approve the results.

作者:盛涛涛(无沐)

本文是摘录自开源项目 Alibaba Cloud AI Agent Handbook 实践篇章,该开源项目已上线 Agent 工程闯关小游戏,欢迎体验。

项目地址:github.com/aliyun/ai-a…

年初,我修过一个让我印象深刻的 Bug。最后真正要改的代码,其实只有两行,但前面查日志、看监控和分析问题,就花了我一整天时间。 为了复现问题,我只能在各种会议的间隙中抽空反复调试,前后接近一周才完成。之后还要补测试用例、跟进代码评审,直到把代码合进去。

现在,AI 已经能帮我们写代码了。但如果只是帮我写那两行代码,其实也没省下什么时间。那些真正费时的工作,能不能也交给它?

本文将分享我们在 PolarDB-X 团队做的一些工程实践:让 Agent 从一条报警或工单出发,自主完成排查、复现、修复和验证,再把结果交给研发评审。

#PolarDB-X#Alibaba Cloud#PolarDB-X Zero#Alibaba Cloud AI Agent Handbook#JDB CLI#SLS#JDB#Kubernetes

Original · 12 min readRead the original
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