Automated Error Detection

The worst way to find out about a platform issue is from a user report. By the time a user reports a problem, it's already been affecting them for some time, probably affecting others too, and the incident has had its full impact.

Automated error detection is designed to catch problems before they reach that point.

What's Monitored

Error rate trends. A sudden increase in errors — even if the absolute number is small — triggers an alert. Catching a spike early is the difference between a 5-minute incident and a 2-hour one.

Response time degradation. Page loads that are slower than the established baseline for that workspace trigger investigation. Not every slow request is a problem; a consistent degradation pattern is.

Failed workflow executions. Workflows that fail repeatedly on the same step, or workflows that start failing after working correctly, are detected and flagged automatically.

Background job failures. Email delivery failures, document generation errors, import failures — background operations that fail silently are monitored and reported.

Unusual access patterns. Authentication failures, unusual request volumes, access pattern anomalies — caught early rather than discovered later.

How Alerts Work

Alerts go to workspace administrators via the notification channel they configure — email, in-app notification, or (for high-severity issues) SMS. The alert includes enough context to understand what's happening without requiring a detailed investigation just to understand the alert.

Each alert is linked to the relevant trace data where applicable, so you can go from "there's a problem" to "here's what's happening" in one step.

The Goal: Zero Surprise Incidents

No system is perfect, and problems will occasionally occur. The goal of automated error detection isn't to prevent all problems — it's to eliminate the category of "problem that existed for hours before anyone noticed."

Catch issues early. Resolve them quickly. Users stay productive.