Knowledge base continuous update trigger mechanism design

Some systems expose webhooks or change notifications. Microsoft Graph describes notifications for resources that are created, updated, or deleted; an application can use the signal to fetch the changed resource and update a cache or view. Other systems expose a delta token, a modified timestamp, or no incremental API at all.

Make the source contract explicit. Record which changes are visible, how long notifications remain available, whether deletion is observable, and whether the event contains enough data to process immediately. The event should identify work; the worker should fetch the current source version before publishing a derivative.

Events can arrive twice, arrive out of order, or arrive after a source has changed again. Use a stable key based on source identity and version, and make each step safe to retry. Do not create a second knowledge record because the same event was delivered twice.

Keep ingestion, transformation, review, indexing, and retirement states separate. A document fetched successfully is not the same as a derivative approved for current search. Record the source modified time, ingestion time, index time, and review time independently.

A continuous update mechanism that only adds records creates a knowledge graveyard. When a source is deleted, superseded, or access is revoked, remove or tombstone the derivative, cached answer, embedding, and search result according to the retention rule. A summary remains sensitive even when its original document is moved elsewhere.

Run access tests after refresh. Microsoft Graph notes that change notifications can have latency and subscription limits; the design should therefore include expiry monitoring and a reconciliation path rather than assuming every event arrives on time.

The answer layer needs to know whether a source is current, overdue, failed, under review, or superseded. If a required source is stale, say so and withhold a consequential answer when the workflow requires current data. A green response from an old index is not a healthy system.

Use the existing knowledge freshness loop as the operating model, then add source-specific event and reconciliation details for the implementation.

Track event-to-fetch delay, fetch-to-index delay, review queue age, missed-event recovery, duplicate processing, failed source reads, stale answers, permission defects, and deletion completion. Keep a small set of changed-source tests and run them after connector or indexing changes.

The design is successful when the team can explain what changed, what was processed, what was withheld, and who owns the next repair. Processing volume is not freshness evidence.