AI原生产品分析：为智能体设计的分析工具
This is probably how AI-native product analytics should work.
This is probably how AI-native product analytics should work.
这大概就是AI原生产品分析应该有的样子。
Dashboards was for humans. For AI agents, they’re a pretty bad interface.
仪表盘是为人类设计的。对于AI代理来说，它们是一个相当糟糕的界面。
That’s the direction Human Behavior is taking.
这就是Human Behavior正在采取的方向。
It watches sessions, funnels and errors continuously, works out what changed and why, then hands that context to agents that can act on it: open a PR, file the ticket, post it in Slack, pull the numbers.
它持续监控会话、漏斗和错误，找出发生了什么变化以及原因，然后将这些上下文传递给能够采取行动的代理：打开PR、提交工单、发布到Slack、提取数据。
Its SDK captures sessions, clicks, network calls, and errors.
它的SDK捕获会话、点击、网络调用和错误。
You can ask questions like “why did signups drop?” and its agents work across the available product context rather than treating the question as a standalone prompt. The same context can then flow into tickets, PRs, Slack messages, reports, or follow-up investigations.
你可以提出诸如“为什么注册量下降了？”这样的问题，它的代理会在可用的产品上下文中工作，而不是将问题视为独立的提示。相同的上下文随后可以流入工单、PR、Slack消息、报告或后续调查中。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力