This AI monitors itself and opens its own PRs
TL;DR · AI Summary
LangChain engineer Alex Levenson built an AI self-monitoring system that automatically scans codebases to find missing metrics, tests them in shadow mode, filters noise via agent review, then auto-creates PRs and promotes valid alerts to notify engineers.
Key Takeaways
- System uses Inspect tool to auto-scan codebase when new PRs are created or on cr
- Monitor alerts undergo shadow mode testing and agent review; noisy ones get prun
- Qualified monitors auto-create PRs and notify team via Slack, achieving full aut
Outline
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A LangChain engineer created an AI-powered self-monitoring loop that automatically identifies missing monitors in the codebase and creates PRs.
The system uses Inspect tool to listen for new PR events or trigger scans on a cron schedule automatically.
New monitor alerts enter silent mode first, don't bug engineers, and are used to collect initial data and evaluate noise levels.
Another AI agent reviews shadow mode alerts, automatically prunes overly noisy monitors, and retains high-quality signals.
After running for a while, if alerts are stable and not noisy, the agent promotes them to official monitors that notify engineers.
The system notifies the team of final results via Slack, completing the fully automated flow from discovery to deployment.
Mindmap
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- AI自监控系统
- 触发机制
- 新PR事件
- Cron定时任务
- Inspect工具
- Shadow Mode
- 静默测试
- 噪音评估
- 信号收集
- Agent评审
- 自动修剪噪音
- 质量判断
- PR创建
- 升级通知
- 警报提升
- Slack通知
- 工程师响应
Highlights
Key sentences worth saving and sharing.
Whenever a new PR is created or on a cron job, the system scans the codebase to see if there's a missing monitor or metric to add
Monitor alerts go into shadow mode where they won't bug anyone yet, and another agent reviews them for noise levels
If they run for a while and aren't noisy, and the agent deems them good enough, they get promoted to start bugging engineers