Rethinking UX for AI-driven alerting

Steve Boak discusses how advanced statistical methods are transforming the user experience (UX) of alerts in monitoring tools. He highlights four main dimensions used to define alerts: scope, metric, threshold, and time. However, these constraints have limitations that can lead to static thresholds not adapting to changing conditions, warning thresholds being a crutch for false positives, and scope needing to be defined upfront. This is where algorithmic alerting comes in, introducing three main varieties: forecasting, anomaly detection, and outlier detection. These methods offer flexibility in tracking thresholds and utilizing time, but still require upfront configuration. Algorithmic feeds may use some of these methods with loose constraints, allowing for a more dynamic experience. However, the future of alerting UX relies on matching patterns to people, which will determine the quality of the experience and potentially make manually defined alerts unnecessary.

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