Help: on-call schedule changes not reflecting in the actual rotation

I am currently evaluating the incident response orchestration capabilities of several major platforms (specifically PagerDuty, OpsGenie, and VictorOps) for a multi-team engineering organization. A persistent and critical issue has emerged during our proof-of-concept phase regarding the synchronization and propagation of on-call schedule updates.

The core problem is a lag, or in some cases a complete failure, for schedule modifications made in the web UI to be reflected in the actual active rotation and, consequently, in alert routing. For instance, a last-minute shift swap approved on a Friday afternoon might not be honored by the system over the weekend, leading to alerts being delivered to an engineer who is officially off-duty. This creates a significant pager fatigue risk and undermines trust in the entire on-call system.

Our configuration involves layered schedules with overrides, and we are integrating via REST APIs with our internal monitoring stack (Prometheus Alertmanager) and ticketing system (Jira). The observed behavior is inconsistent:

* In Platform A, changes made via the API reflect immediately, but UI changes can take up to 15 minutes, which is documented but operationally hazardous.
* In Platform B, we encountered a scenario where an override was visually present in the schedule calendar but was not considered by the alert routing engine, a critical bug.
* In Platform C, dependency on a background job queue means propagation delays are variable and non-deterministic.

I am seeking analysis from others who have navigated these deep implementation details. My specific questions are:

1. What is the underlying architectural pattern that leads to this delay? Is it typically an eventual consistency model in the backing database (which I suspect), a caching layer issue (CDN, in-memory cache), or a deliberate but poorly communicated design choice?
2. From a reliability engineering standpoint, how have you mitigated this risk? Have you built idempotent verification checks that poll the schedule state via API after a change to confirm activation?
3. Are there specific configuration patterns or best practices—perhaps eschewing certain UI features in favor of pure API-driven management—that guarantee stronger consistency?

A comparative snippet of how we are currently attempting to validate a schedule change via API (using a pseudocode example) is below. This feels like a workaround, not a solution.

```python
# After a schedule update call, we poll until the effective user matches expectation.
def assert_schedule_update(schedule_id, timestamp, expected_user_id):
max_retries = 12
for _ in range(max_retries):
current_oncall = api_get(f"schedules/{schedule_id}/oncall?time={timestamp}")
if current_oncall['user']['id'] == expected_user_id:
return True
time.sleep(10) # Poll every 10 seconds
raise ScheduleSyncError("On-call schedule did not converge within expected timeframe.")
```

The meta-concern here is that incident response tooling, which must be the bedrock of operational reliability, cannot itself exhibit unreliable behavior in its core scheduling function. I am particularly interested in experiences from those running at scale, where the propagation delay's impact is multiplied across hundreds of schedules and thousands of overrides.