The Performance Dashboard keeps painting Agent Utilization at 82% while the conversation detail view logs idle status for nearly three hours straight. Environment’s running Genesys Cloud Release 24.02 with the default Performance Views layout, and the queue metrics don’t match what floor supervisors actually see on their monitors. Wrap-up codes get tagged as ten-minute blocks, but the utilization bar stays pinned above 75% until the overnight shift starts. Screenshot one attached shows the exact timestamp drift between the queue summary tile and the individual agent row. Does the aggregation engine count post-call work as active talk time for the utilization calculation, or is it just padding the denominator? Headcount planning’s stalled because the numbers completely ignore actual call volume.
Console throws a generic “Metric aggregation timeout” when drilling past the thirty-day window, and the export CSV comes back with null values for the wrap-up duration column. You can spot the gap in the second image where the occupancy line spikes but the utilization graph stays completely flat. What exactly triggers the idle threshold to reset after a wrap-up code gets applied? Floor managers are doing jack all to reconcile the gap since the dashboard metrics won’t align with the actual floor reality. Need a clear breakdown of how wrap-up time factors into the utilization formula versus occupancy. The raw JSON payload from the reporting endpoint just shows…
Pretty common issue. It’s usually the afterWork status skewing the Performance Views metrics when the backend batches those ten-minute wrap-up blocks. Run this analytics:read query to isolate the actual state durations:
Yeah, confirmed that behavior on a couple of orgs running similar wrap-up configs. That utilization mismatch screams afterWork configuration drift. The dashboard aggregates those wrap-up blocks as active time, but the detail view drops them if the agent toggled to Idle during the wrap-up window. the earlier post’s curl is on the right track, but parsing that JSON manually gets messy fast.
Here’s a quick Python snippet using the platform client to pull the exact state breakdown. This dumps the raw metric splits so you can see if the afterWork duration is actually eating into the utilization bucket while the status log shows idle.
from purecloudplatformclientv2 import Configuration, ApiClient, AnalyticsApi
import os
config = Configuration()
config.host = "https://api.mypurecloud.com"
config.access_token = os.getenv("GENESYS_TOKEN")
api_client = ApiClient(config)
analytics_api = AnalyticsApi(api_client)
# Query summary for the specific user and date range
body = {
"groupBy": ["user"],
"interval": "P1D",
"metrics": ["utilization", "afterWork", "idle"],
"entityId": "USER_ID_HERE",
"dateFrom": "2024-02-01T00:00:00Z",
"dateTo": "2024-02-01T23:59:59Z"
}
try:
response = analytics_api.get_users_analytics_summary(body=body)
for group in response.groups:
print(f"User: {group.entityId}")
for metric in group.metrics:
if metric.name in ["utilization", "afterWork", "idle"]:
print(f" {metric.name}: {metric.total}")
except Exception as e:
print(f"Error fetching analytics: {e}")
Run that against the user ID causing trouble. If afterWork shows high volume but idle is also spiking, the wrap-up code itself probably has a weird max duration cap or the agent is double-clicking status changes. Check the routingWrapupCodes endpoint for that queue. Sometimes the maxDurationSeconds field forces a hard cut that confuses the analytics engine, stuff like that.
Also worth checking if the performance view filter is set to Include After Work. That toggle lives in the view config JSON. If it’s off, the numbers won’t match the raw API output.
My CLI tool scrapes these configs nightly. Usually, it’s just a stale cache on the view layer. Force a refresh on the dashboard or wait for the next analytics rollup.
The 82% pinning sounds like the analytics bucket hasn’t flushed the previous day’s wrap-up carryover.