“Final” is a product promise. A stream processor's watermark signals that event time has progressed far enough to fire a window. It does not make future arrivals with older timestamps impossible. A mobile client could upload an incident twenty minutes late, or a Kafka partition could resume after a pause. Flink's window documentation explains that events can arrive after a watermark passes a window. Allowed lateness can retain the window for further updates. With the default zero allowed lateness in this API, an element that arrives after the watermark is dropped from that window, so we first need to learn which behavior the report actually uses.

The design choice is explicit. If the report is provisional until an allowed-lateness window closes, show a provisional label and version. If a late event is accepted after publication, issue a new report revision or a correction record with the old and new count, and make the reader and downstream consumers understand revisions. If the business truly requires an immutable report, place later events in a separate adjustment period and state that rule. Silently rewriting a prior report makes audits and agent citations inconsistent. Silently dropping valid late events makes the count wrong for a different reason.

I would define the time fields before changing code: event occurrence time, source commit time, ingest time, watermark at processing, and report publication time. Check timestamp parsing and timezone first, then trace the late event to its source partition and window. Log how many events arrive beyond each lateness threshold and how often published reports are corrected. The watermark of a multi-input operator can also be held back by a slow or idle partition, so a stuck report is not necessarily a model-generation delay.

A Principal-level answer must carry this to the AI output. An assistant cannot cite “52 incidents last hour” without the report version, as-of time and finality status. On a corrected version it should not blend yesterday's prose with today's count. The incident happened last Sunday. Why did the AI report count it this week? asks which week a 1:30 AM incident belongs to. That is event-time assignment. This question asks what happens when an event for the right hour arrives after that hour was declared complete.