It is 2am. The page just fired. Within ninety seconds your on-call engineer has fifteen tabs open: Datadog for the metric, Sentry for the exception, GitHub for the recent deploys, Vercel for the build, Jira for the related ticket, and the Slack incident channel where four people are typing "looking" at the same time.
Twenty minutes later there are still fifteen tabs open and no answer. Not because the engineer is slow. Because the answer is split across fifteen tabs, and a human is the only thing connecting them.
The tabs are a symptom, not the disease
It is tempting to blame the number of tools. Consolidate everything, the thinking goes, and the tabs go away. But each of those tools is good at its job. Datadog is a good metrics tool. Sentry is a good error tracker. The problem is not that you have six tools. It is that they do not talk to each other, so the work of correlating them falls on a person, under pressure, at the worst possible time.
Every tab switch is a small tax: reload context, re-find the time range, re-orient. Fifteen tabs is not fifteen units of work. It is fifteen units of work plus the cost of holding all of them in your head at once while production is down.
What the first twenty minutes actually go to
Watch a real incident and the first twenty minutes are almost never spent debugging. They are spent assembling:
- Correlating the Datadog spike with the Sentry errors, by eyeballing timestamps across two tabs.
- Figuring out which deploy went out right before the spike, by cross-referencing GitHub and the deploy log.
- Finding the Slack thread from last month where someone hit this exact failure, if anyone can remember it existed.
- Working out who is even on call, and whether the right people are already in the channel.
That is assembly work. It is necessary, it is slow, and it is the same every single time. The actual debugging cannot start until it is done.
Assemble once, automatically
The fast incident teams are not faster at the keyboard. They start debugging in minute two instead of minute twenty, because the assembly is already done when they arrive. The deploy that went out before the alert is right there. The correlated metric spike and the exception count are in one place. The thread from last month surfaces on its own.
This is a correlation problem, and correlation is something software is good at. Pull the signals from each source in parallel, line them up by time, and rank them by relevance. The engineer spends the first twenty minutes fixing the problem instead of finding it.
When an incident fires, Malviont assembles the context in parallel across your monitoring, deploys, errors, and discussions, and ranks it, so triage starts in minute two instead of minute twenty.