Teams that have run Software as a Service (SaaS) products know the routine: An alert goes off, and everyone scrambles to the observability stack first. Metrics, logs, and traces have been the signals that help engineers figure out what broke, why users are stuck or where a service-level agreement (SLA) may have slipped. And for years, these tools have worked well enough.
But then AI showed up.
Behind all the hype and potential surrounding copilots, chat interfaces, and intelligent assistants, engineering teams have quietly run into something…

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