From symptom to cause
The symptom is far from the cause
A drop in conversion might start with a slower tool, a new prompt or a model swap two steps earlier. Each lives in a different tool.
All the evidence in one place
Traces, eval scores, token spend and product events share the same store, so you can compare runs before and after a change and see which step differs.
Start from the metric that moved, compare affected and unaffected runs, find the step that differs, and confirm the fix.
Collect
Bring traces, logs, metrics, evals, costs, deployment context, and workflow events into one investigation record.
Connect
Map symptoms to the agent, model, tool, dependency, release, customer segment, or process step involved.
Explain
Identify the likely cause with supporting spans, events, metrics, and affected outcomes.
Act
Route the fix to the owner with enough context to reproduce, prioritize, and close the incident.
What teams can do
- Build causal paths across traces, evals, logs, metrics, and KPIs
- Identify upstream triggers behind quality, cost, latency, and reliability regressions
- Summarize incidents with evidence for engineering, product, and operations teams
- Route the right context to the right owner or workflow
What makes AI root cause analysis different?
AI failures cross model behavior, tool behavior, application telemetry, and business outcomes. Anosys connects those layers instead of treating each signal as a separate dashboard.
Can Anosys explain incidents when dashboards look green?
Yes. Anosys is designed for silent AI failures where infrastructure metrics look healthy but quality, cost, or user outcomes degrade.