Every workflow, graded. Every problem, visible.
AI Health gives every workflow in your estate a live grade based on error rate, latency, throughput, and drift. One table, sorted worst-first, so the workflow heading for trouble is always at the top of your screen — not buried in execution logs.
The problems that pile up quietly.
A workflow degrades from 99% to 91% success over three weeks and nobody notices until it hits 70%.
Latency creeps up gradually — each day looks normal, but the quarter tells a different story.
Health checks mean opening every workflow one by one, so they happen roughly never.
What changes for your team.
Worst-first triage
The health table sorts your estate by severity. Your morning check becomes a ten-second glance: anything red at the top, or not?
Drift caught early
Obsivara compares every workflow to its own historical baseline, flagging slow degradation that daily monitoring can’t see.
Health history per workflow
Every grade change is recorded with the events that drove it — so "when did this start?" always has an exact answer.
Live in minutes, not sprints.
Auto-baseline
Each discovered workflow gets a rolling baseline for success rate, latency, and volume within its first week of observation.
Grade continuously
Every execution updates the grade. Healthy, warning, and critical thresholds adapt to each workflow’s own normal.
Alert on transitions
When a workflow crosses from healthy to warning — not just to critical — you get a Slack ping with the trend chart attached.
Questions
Each workflow is graded against its own rolling baseline — not a fixed global threshold. Obsivara tracks success rate, latency, and throughput per workflow, then compares the current window against historical norms to produce a Healthy, Warning, or Critical grade.
Yes. Obsivara alerts on transitions, not just critical states — so you get a Slack notification with a trend chart the moment a workflow crosses into Warning, before it becomes a production incident.
That is exactly what it is designed for. Because grades are computed against each workflow's own historical baseline, a gradual decline from 99% to 91% success over three weeks is flagged — whereas threshold-based monitors would miss it entirely.
See it on your own AI stack.
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