Statistics
Statistics is an operational dashboard for reviewing delivery flow, usage, reliability, and recorded AI cost across workspaces the viewer can access.
Access and filters
The dashboard requires View Statistics. Non-administrators see only workspaces to which they are currently assigned; administrators can report across the installation. Workspace access is checked on every Statistics request, including cached requests.
The workspace and date-range filters apply across all tabs. Presets and custom ranges are available, with a maximum custom range of 365 days. Changing a filter reloads the relevant sections; Refresh retrieves current values.
Most metrics are historical for the selected range. Panels explicitly labeled as lifecycle or current-state snapshots show present ticket state and may ignore the date range. Ticket status and tag filters apply only to the ticket breakdown charts, not cycle-time or current-state panels. Selecting multiple tags includes tickets carrying any selected tag, and punctuation such as commas remains part of the tag value.
Dashboard tabs
The six tabs separate high-level reporting from detailed operational views.
| Tab | Contents |
|---|---|
| Overview | Summary counts, cost, and prior-period comparisons. |
| Tickets | Throughput, cycle time, QA, aging, and type/source/priority/assignment breakdowns. |
| Sessions | Session state, source, model usage, duration, messages, outcomes, skills, subagents, bots, and automations. |
| User Activity | Active-user counts, contributors, sessions, messages, and cost by user. |
| More Metrics | Round tables, insights, workflows, and hook-task results. |
| Cost & Tokens | Recorded spend, tokens, trends, and model-level cost. |
A tab loads when first opened. Filters invalidate previously loaded tab data so the views remain consistent.
Interpret ticket metrics
Ticket metrics characterize delivery flow; use them with the pipeline configuration and date filter visible.
| Metric | Interpretation |
|---|---|
| Completed outcomes | Share of distinct tickets that entered Completed rather than Canceled during the selected period, based on terminal transition time. |
| Cycle time | Time from Pending to first Completed; average and P95 expose typical and long-tail delivery. |
| Time to first action | Delay before a ticket first leaves Pending. |
| Implementation, QA, and PR review time | Time recorded in the corresponding pipeline stage. |
| QA rejection rate | Frequency with which QA returned work for changes. |
| Reopened | Tickets that entered Completed more than once. |
| Open ticket age and blocked age | Current backlog aging, including work that needs intervention. |
Stage configuration, manual overrides, and sparse samples affect interpretation. Compare similar workspaces and periods rather than treating one metric as a service-level guarantee.
Interpret session and automation metrics
Session and automation panels identify capacity, reliability, and workload patterns.
- Session status, source, model distribution, daily trend, duration, message count, pull-request rate, outcomes, and high-cost sessions show how work is running.
- Skill usage shows executions, distinct sessions, last-used time, and daily activity using the name recorded when each tool ran. Missing or malformed attribution is omitted.
- Subagent usage shows named parent-agent delegations and their daily activity. Parent-agent usage records and aggregate subagent rollup records are reported separately; rollups are not assigned a name because those audit records do not retain one.
- Bot usage shows conversation volume and frequently used bots.
- Automation metrics show run volume, success rate, duration, cost, and automations with repeated failures or auto-disablement.
- Workflow metrics show run volume, success rate, duration, and frequently used workflows.
- Hook-task metrics highlight failing, retried, or slow lifecycle tasks.
Investigate repeated failures in the relevant session, automation, workflow, or hook logs. A dashboard trend identifies where to inspect; it does not replace execution logs.
Cost and token data
Cost panels aggregate usage reported for model interactions and related AI processing.
| Metric | Use |
|---|---|
| Total cost and daily cost | Track spend for the selected period. |
| Input, output, cache-read, and cache-write tokens | Understand the recorded usage mix. |
| Cost per ticket | Compare AI spend associated with completed ticket work. |
| Cost by model | Identify which selected models account for spend. |
| Top sessions by cost | Locate sessions for detailed review. |
Values are shown in USD where cost data is available. Missing provider pricing or usage data can produce incomplete totals; reconcile billing with the model provider's invoice. Configure enforcement separately under AI Cost Budgets.
Data scope and retention
Statistics is derived from Polygent records, so report quality follows operational data quality.
- Date filters use recorded server timestamps.
- Workspace access is applied before aggregation.
- Deleted or purged records are unavailable for later reporting.
- Current-state panels can change independently of a historical date range.
- Prior-period comparisons use the immediately preceding range of equal duration.
- Recently completed activity may require Refresh before it appears.
Back up the database according to your retention and audit requirements. Export or capture reports externally when long-term evidence must survive application data retention or deletion.
Troubleshooting
Statistics failures are usually caused by access scope, date selection, incomplete source data, or an unhealthy database query.
| Symptom | Resolution |
|---|---|
| Statistics is hidden or access is denied | Grant View Statistics and assign the user to the required workspaces. |
| A workspace is missing | Confirm workspace membership; administrators see all workspaces. |
| A panel is empty | Confirm the selected range and workspace contain the relevant records and that the licensed feature is enabled. |
| Totals appear stale | Select Refresh and verify the source session or ticket has reached the expected state. |
| Costs do not match an invoice | Check whether all models reported usage and pricing, then reconcile against provider billing. |
| Custom range is rejected | Select a range no longer than 365 days and ensure the end is not before the start. |
| One section fails while others load | Retry that section, then inspect system logs and database health if failure persists. |
| Queries are slow | Narrow the date range or workspace scope, then review database capacity, indexes, and resource pressure. |