Provide an always-available operational view that helps a support lead answer one question quickly: what needs attention now?
The implementation separates urgent work from watch items and background context. It also keeps degraded and high-volume states explicit so the screen remains useful when the operation is under pressure.
- Support leads monitoring queue health and SLA exposure.
- Application support analysts investigating operational signals.
- Implementation teams integrating support platforms with internal reporting.
The dashboard is organized around five questions:
- Which conversations require immediate action?
- Which queue segments are approaching SLA?
- Where did automation hand work to a person?
- Which conversations explain a headline signal?
- Does the same hierarchy remain useful during peak volume?
The repository implements the frontend reference experience:
- operational overview;
- queue under pressure;
- AI-to-human handoff monitoring;
- drill-down to affected conversations;
- peak-volume rendering;
- empty, loading, error, and peak states;
- reusable metric-card and dense-table components.
The repository does not currently implement authentication, persistence, a ticketing-system adapter, WebSocket updates, or production telemetry.
| Constraint | Implementation response |
|---|---|
| Dense operational data | Separate signals into act now, watch, and context on demand |
| Frequent updates | Reserve faster refresh behavior for action-oriented signals |
| Short review window | Keep the primary operational question visible above secondary context |
| Peak volume | Reuse the same hierarchy with different data instead of creating a separate layout |
| Stale or unavailable data | Preserve the last known state and expose its timestamp |
- Problem: equal visual weight makes a dense dashboard difficult to scan.
- Decision: group information into act now, watch, and context on demand.
- Trade-off: secondary information requires a scroll or drill-down.
- Problem: every value updating at once creates noise and makes changes difficult to interpret.
- Decision: action-oriented signals are modeled for faster refresh; contextual metrics keep a stable anchor.
- Trade-off: contextual values may be intentionally less immediate.
- Problem: aggregated queue totals can hide conversations waiting for human action.
- Decision: human-wait and SLA-risk signals are first-class operational states.
- Trade-off: the overview exposes uncomfortable operational conditions instead of smoothing them into totals.
- A support lead can identify the highest-priority signal without opening another view.
- Queue, handoff, drill-down, and peak examples use consistent identifiers.
- Empty, loading, error, and peak states are represented explicitly.
- Sample values are never described as production measurements.
- The project passes lint and production build checks.
- Define a normalized ticket-event contract.
- Implement an adapter for a support platform API.
- Add authentication and role-based access.
- Add stale-data thresholds and reconnect behavior.
- Add contract, accessibility, and end-to-end tests.