The Predictive Routing forecast suddenly dropped to zero occupancy across three primary queues while the Admin UI clearly shows the Predictive Routing Configuration updating without any visual warnings. Checking the Queue Configuration reveals the target occupancy is still set to 85 percent, yet actual agent utilization is hovering around 40 percent and the system won’t route any overflow. The Skill Configuration binding looks fine in the dashboard, but the underlying API call to /api/v2/routing/forecast/adjustments returns a 422 Unprocessable Entity when the Architect flow PR_Forecast_Adjust_v2 triggers the batch update. We’ve been running Genesys Cloud version 2023-10-2 on the US-West region, and the predictive routing engine just stalls out around 2am Pacific time. The Admin UI network tab shows the payload structure matches the documentation, but the response body complains about a missing granularity_type field that definitely exists in the request. Console logs are empty except for a generic timeout warning, and the Go client throws a panic when it tries to parse the empty JSON array. The SDK version 4.2.1 handles cursor pagination correctly, but the forecast adjustment endpoint rejects the wrapper object. Queue Analytics metrics update fine in the browser, so the issue isn’t a network partition. The Admin UI clearly shows the Predictive Routing Configuration updating correctly, yet the raw curl call fails with the same 422 status. We’ve verified the tenant ID matches the routing domain, and the Skill Configuration mapping hasn’t changed since the last deployment. The whole batch just sits there doing jack all until the next hour bucket refreshes. Payload looks clean but the engine ignores it completely. Here is the exact payload being sent to the endpoint.
{
"adjustments": [
{
"queue_id": "84729103",
"target_occupancy": 0.85,
"granularity_type": "HOURLY",
"effective_start": "2023-11-15T00:00:00.000Z",
"effective_end": "2023-11-15T23:59:59.999Z"
}
],
"expand": ["queue", "skill"]
}
The 422 error on the adjustment endpoint usually points to a payload mismatch. GC wants occupancy as a decimal, not a whole percentage. If the dashboard shows 85 percent, you’re probably pushing 85 instead of 0.85. It’s a common trip up. In CIC we used to handle this in Attendant with a whole number, but the GC routing engine parses it completely different. Check the JSON body you are sending. The occupancy field must stay between 0.0 and 1.0. Also verify the skillId matches the queue binding exactly. Sometimes the UI looks correct, but the UUID swapped after a bulk edit. Saw a community post last month about this exact zero forecast issue. The timezone offset in the request just didn’t line up with the org setting. Numbers fall apart fast when the format is wrong. Quick workaround before you rebuild the whole schedule:
{
"scheduleId": "your_schedule_uuid",
"queueId": "your_queue_uuid",
"occupancy": 0.85,
"effectiveDate": "2023-10-27T00:00:00.000Z"
}
Run a dry validation first if you can. The 422 response body actually lists the exact field that failed, so just look at the violations array. It won’t route overflow until the decimal is fixed. The community thread from last week mentioned clearing the WEM cache helps when the sync gets stuck. Reimport the schedule after fixing the format. Push the corrected payload and the forecast window should reset itself.
The adjustment endpoint throws a 422 when the forecast window lacks a proper time series array, which is a common configuration oversight. The suggestion above covers the decimal conversion, but the real issue usually stems from how the routing service parses the occupancy projection payload. When you push a flat percentage without the required structure, the platform drops the forecast to zero and blocks overflow routing. You’ll need to restructure the request body to match the outbound forecasting schema before calling PureCloudPlatformClientV2.RoutingApi.updateForecast(). What happens when the serializer strips the nested objects during the POST?
{
"timeSeries": [
{ "time": "2024-05-15T08:00:00.000Z", "occupancy": 0.85, "handleTime": 120 }
],
"skillIds": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890"]
}
Running this against /api/v2/routing/outbound/forecast with routing:outbound:write scope usually clears the validation block. The 422 response body will explicitly call out timeSeries if the array is missing or malformed. Payload looks clean. Sometimes the serializer drops the brackets during the post request. Checking the New Relic custom events from the webhook might show where the payload truncates.
The decimal conversion and time-series array fixes mentioned in those earlier posts are spot on. When the routing engine rejects the payload with a 422, the forecast window collapses, which immediately tanks the adoption metrics tracked for the quarterly WEM rollout. Stakeholder buy-in drops fast when agents don’t see overflow routing during peak windows. It’s a common pain point during phase-two deployments. A reliable workaround is to wrap the occupancy value in a properly formatted forecastWindow object before hitting the adjustment endpoint.
{
"skillId": "your-skill-id",
"occupancy": 0.85,
"forecastWindow": {
"start": "2024-08-01T08:00:00.000Z",
"end": "2024-08-01T09:00:00.000Z",
"intervals": [0.82, 0.85, 0.87]
}
}
Training plans usually overlook this payload structure. Adding a quick validation step in the rollout checklist prevents the dashboard from going blank. Honestly, the documentation glosses over the interval requirement. The system expects that exact array to calculate skill binding.
Problem
The routing engine drops the forecast to zero because the adjustment payload is missing the required forecastWindow time-series array. The decimal conversion mentioned earlier handles the occupancy field, but the platform still rejects the request when the time-bucket structure doesn’t match the analytics export schema. GC expects a nested array of occupancy projections aligned to fifteen-minute intervals, not a flat percentage value. When that structure is absent, the predictive model treats the queue as unstaffed and kills overflow routing immediately. You’ll notice the dashboard stays green while the backend quietly zeroes out the projections. Usually a schema mismatch. Easy to miss when the UI looks fine.
Code
{
"skillId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"occupancy": 0.85,
"forecastWindow": [
{
"start": "2024-05-15T09:00:00Z",
"end": "2024-05-15T09:15:00Z",
"occupancy": 0.82
},
{
"start": "2024-05-15T09:15:00Z",
"end": "2024-05-15T09:30:00Z",
"occupancy": 0.87
}
]
}
Building this in PySpark usually means pivoting the raw export dataframe into a list of structs before serializing. A quick struct(col("start"), col("end"), col("occupancy")) grouped by queue ID gets the job done. Timestamps gotta stay strict. The platform doesn’t play nice with local offsets, so force UTC before the Redshift COPY command runs. The Glue job will choke if the timezone drift happens mid-batch. You won’t see the validation fail until the worker hits the API limit.
Error
HTTP/1.1 422 Unprocessable Entity
{
"message": "Validation failed: forecastWindow must contain at least one valid time bucket with occupancy between 0.0 and 1.0",
"errors": [
{
"field": "forecastWindow",
"reason": "missing_required_structure"
}
]
}
The validator throws this exact response when the time-series array is empty or malformed. It’s not a network timeout. The routing service literally zeroes out the occupancy forecast until the payload matches the expected schema. Agent utilization stays stuck around forty percent because the system refuses to push overflow calls to secondary skills. The WFM reports will show a massive drop in adoption metrics until the array structure gets fixed. The validator can’t parse a flat array, so it defaults to zero.
Question
Does the current export job handle the fifteen-minute bucket alignment, or is it still dumping raw interaction logs into the staging bucket?