The limiting factor on how quickly an on-call engineer resolves an incident is rarely the rate at which they make decisions. It is the rate at which they can look at things — logs, metrics, traces, dashboards, SSH sessions — and assemble a picture of what the system is doing. Decision-making is cheap once the picture exists. Building the picture is where the time goes. This work is structurally single-seat. To bring a colleague in, an engineer has to translate what they have already seen: which log lines, when the metric started climbing, and what the network trace confirmed. The translation cost is high enough that most on-call engineers defer involving others until the problem forces it. The team is nominally 24/7; in practice, investigations happen alone. ...

In most incident response pipelines, customers begin experiencing impact at minute zero. Within a few minutes, the first support tickets arrive. By minute ten, customer-initiated posts appear in shared channels. Public status pages are typically updated somewhere between thirty and ninety minutes. The gap between when a customer first feels impact and when that customer is formally told is a measurable, important, and almost universally unmeasured KPI. Call it Time-to-Customer Awareness, or TTC-Aware. ...

Every on-call veteran has the same intuition: a meaningful portion of what pages us isn’t actionable. What most of us don’t have is a way to prove it — let alone to classify why a given alert is noisy. Without classification, the only lever is blunt: raise the threshold, disable the alert, ignore the channel. None of these distinguishes an alert that’s noisy because the platform self-heals from one that’s noisy because it flaps. ...