Monitoring
1. Overview
The Monitoring screen gives you health control over every application running on the platform — Flows, Apps, and Add-ons — in a single place: executions, errors, resource consumption, AI token consumption, detailed tracing, and active alerts. Instead of hunting for information across several different systems, you can quickly see what's running, what's failing, and why.
2. Filters
In the side panel, filters narrow down the monitoring view:
| Filter | Description |
|---|---|
| Squad | Squad that owns the monitored resource. |
| Environment | Monitored environment (e.g. test). |
| Type | Resource type: Flows, Apps, or Addons. |
| Flow | Specific Flow to monitor (when Type = Flows), or the All Flows (aggregate) aggregated view. |
| Period | Time window of the data shown (e.g. last 15 min). |
3. Aggregated Indicators
At the top of the screen, indicators summarize the overall state of what's selected in the filter:
| Indicator | Description |
|---|---|
| Total Executions | Total executions in the period. |
| Error Executions | Executions that ended in error. |
| OOM Containers | Containers that suffered an Out of Memory event in the period. |
| Restart Deployments | Number of deployment restarts in the period. |
| Running Instances | Instances currently running. |
4. Overview Tab
Shows a summary of the selected resource (e.g. a specific Flow), with the same types of indicator at a finer granularity:
| Indicator | Description |
|---|---|
| Executions | Executions in the period. |
| Errors | Executions with errors. |
| OOMs | Out of Memory occurrences. |
| Restarts | Resource restarts. |
| Running Instances | Instances currently running. |
| CPU Now | Current CPU usage (%). |
| Memory Now | Current memory usage (GB). |
| Deployments | Number of deployments performed. |
Right below, two charts complement the view:
- Top Executions — ranking of the services/flows with the most executions in the period (configurable, e.g. Top 5).
- Top Errored Executions — ranking of the services with the most failed executions in the period ("Services with the most failed executions in the selected period"), with the chart itself indicating when there are no errors in the period ("No errored executions in this period").
5. Metrics Tab
Holds the detailed metrics for the resource selected in the filters, within the time window chosen in Period — the time-series view behind the numbers summarized on the Overview tab.
6. Logs Tab
Queries the execution logs of the monitored Flows/Apps, letting you investigate the detailed behavior of a specific execution without leaving the Monitoring screen.
7. Trace Tab
Shows the complete execution trace — the step-by-step path an execution took (for example, between a Flow's components, or between a Backend App's calls), letting you pinpoint exactly which step a problem or slowdown occurred at.
8. Alerts Tab
Shows the error alerts currently occurring for that monitored instance/resource — a single place to track active problems without having to cross-reference logs manually.
9. Agent AI Tab
Shows the AI token consumption generated by the AI Agent connector within the monitored Flows — useful for correlating AI usage with cost (see also Usage) and with the behavior of executions that use AI.
| Indicator | Description |
|---|---|
| Input Tokens | Total input (prompt) tokens consumed in the period. |
| Output Tokens | Total output (response) tokens generated in the period. |
| Total Tokens | Sum of input and output tokens in the period. |
| Avg Input Tokens | Average input tokens per execution. |
| Avg Output Tokens | Average output tokens per execution. |
| Avg Total Tokens | Average total tokens per execution. |
Time-series charts (Input Tokens/s, Output Tokens/s, Total Tokens/s) show the token consumption rate over the selected period.
Below that, percentile charts (Input Percentiles, Output Percentiles, Total Percentiles) show the per-execution token consumption distribution at P50, P75, and P99 — useful for identifying whether typical consumption is stable or whether some executions consume much more tokens than most.
The Models Breakdown (breakdown by AI model used) and Performance Metrics sections are still under development (Coming Soon).
10. FAQ
What causes an "OOM Container"? It happens when a container exceeds its allocated memory limit and is terminated by Kubernetes due to an Out of Memory condition.