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Message Popups Flows

List and facets flow

sequenceDiagram
    participant UI as MessagePopup
    participant API as message APIs
    participant DATA as message tables
    participant FAC as facet builder
    UI->>API: Load message list and facets
    API->>DATA: Query message logs and related aggregates
    API->>FAC: Build filter facets from current result space
    API-->>UI: Return rows and facet values

This split matters because row retrieval and facet generation can drift in cost and behavior as message volume grows.

Detail flow

sequenceDiagram
    participant U as User
    participant UI as MessagePopupDetails
    participant API as detail endpoints
    participant DATA as runs, headers, payloads, attachments
    U->>UI: Select message
    UI->>API: Load message detail
    API->>DATA: Query related runtime tables
    API-->>UI: Return detail payload

The detail view is where correlations, retries, runs, attachments, and payloads converge. It is therefore the key diagnostic bridge between raw message rows and operational incident analysis.

Statistics flow

Past-hour statistics can be opened directly from KPI cards, which means the popup sometimes starts with a precomputed time range rather than only a generic filter state.

Cross-module follow-up

When a message popup reveals repeated failures, the next technical hop is often:

  • Alerting for the alert state built on top of those messages
  • Archives when message-related retention or historical export matters
  • Tenant Settings when query scope, auth, or retrieval intervals need adjustment

AI investigation flow

Technical failures can be forwarded to the AI chat endpoints, but that is an optional secondary analysis layer on top of the stored runtime message data.