Alert Intelligence

Alert Intelligence owns the quality of your alerting itself: which monitors fire usefully, which fire constantly and get ignored, and what to change about each one. This page explains the outcome the module owns, the workflow inside it, what you need connected, and what the module gives you to act on once it runs.

​

What this module owns

Every alerting setup accumulates monitors nobody trusts. A threshold was right eighteen months ago, the service scaled, and now it fires nine times a week and gets acknowledged without being read. The on-call learns which pages to ignore, and the one that mattered arrives in the same stream as the ones that never do. Fixing it is unglamorous, evidence-heavy work: pull each monitor’s firing history, decide whether its alerts were true, false, or simply not actionable, and then argue for a specific change. Alert hygiene does that work continuously and hands you the proposal — a tuned threshold, a dedupe rule, a suppression window — with the history that justifies it. Good looks like a monitor set where every page is worth reading, and where the count of monitors under review keeps falling instead of quietly growing.

​

The workflow inside it

This module contains 1 workflow.

​

Alert noise reduction

Reads your monitors’ firing history, classifies each one, and proposes the fix. Each step delivers its task to the agents bound to it. The analysis step is where the specialists matter most: one classifies signal quality, one detects flapping patterns, one finds duplicate signals firing on the same underlying condition.

​

What it needs connected

​

Enabling it

​

What you see when it runs

The module’s sidebar entry lists your monitors, each one carrying what the workflow concluded about it:

• Firing history — how often it fired and flapped over the last seven days, and how often anyone acknowledged it.
• The classification, and the rationale for it.
• The proposed fix, with its trade-offs stated, so you can see what you lose by quieting it.

Every monitor carries its review status and the disposition of its recommendations, so the list doubles as your work queue. Because each conclusion is produced by a run, you can open any monitor and read exactly what the agent saw before it made the call.

​

Next steps