- Overview
- Helpdesk
- AI Dispatch
- Project Work
- User Breakdown
- Coverage
- Individuals
- Time Explorer
- Agents
- Skills
Start with a question
Choose the date range and filters that answer a specific operating question:- Are the intended teams active?
- Which ticket categories or queues have the longest delay?
- How often does work resolve quickly or in a single touch?
- Where does AI Engineer complete work or need a person?
- Which projects are consuming effort?
- Which Custom Agents and skills produce useful outcomes?
- Where is connected-system or workflow coverage incomplete?
Interpret metrics responsibly
Metrics can include ticket volume, active users, median resolution time, first action, tickets resolved within a window, single-touch resolution, funnel outcomes, recorded time, project work, and agent or skill activity. Always check:- the date range and timezone;
- the included users, roles, categories, queues, or accounts;
- whether the source workflow changed during the period;
- whether a low count means low usage, missing coverage, or missing data;
- whether the metric represents a customer outcome or only an activity.
Turn a finding into an experiment
1
Describe the baseline
Record the metric, filter, period, and qualitative evidence.
2
Choose one workflow change
For example, improve a runbook, map an account system, train a pilot group, or narrow an agent.
3
Name the expected signal
Decide what should change and what unintended effect you will watch.
4
Review with users
Pair the chart with technician, project-team, or reviewer feedback.
A dashboard supports judgment; it does not explain causation by itself. Use run details and source records to investigate material changes.