Struggling to figure out why the Agent Performance dashboard in Genesys Cloud exhibits significant data lag during peak hours in the Europe/Paris timezone. The view configured for queue activity shows accurate real-time data for conversation counts, yet the talk time and after-call work metrics remain static for up to 45 minutes. This discrepancy impacts our ability to provide timely feedback to agents and affects our service level agreement reporting.
The environment is running on the standard Genesys Cloud instance with no recent architectural changes. The dashboard filters are set to display data for the last 24 hours, segmented by agent group. While the system health dashboard indicates normal operation, the performance data appears stale compared to the real-time monitor.
Is this behavior related to the aggregation window for performance metrics, or could there be a configuration issue within the dashboard settings? The following JSON snippet represents the filter configuration applied to the view:
{
"id": "dashboard_perf_01",
"filters": [
{
"field": "group.id",
"operator": "in",
"values": ["group_support_eu", "group_sales_eu"]
},
{
"field": "timestamp",
"operator": "gte",
"value": "now-24h"
}
],
"metrics": ["talkTime", "afterCallWork", "handleTime"]
}
Any insights into the expected latency for these specific metrics or potential configuration adjustments would be appreciated. The business requirement is for near-real-time accuracy to support operational decisions.
This looks like a synchronization issue between the real-time WebSocket feed and the batch processing pipeline for historical metrics. The Agent Performance dashboard relies on aggregated data that is not always immediately available in the same cache as live conversation counts. Talk time and after-call work metrics often require finalization of the interaction record, which can introduce latency if the system is under heavy load during peak hours in the Europe/Paris timezone.
Instead of relying solely on the standard dashboard view, consider implementing a direct query to the Analytics API for more granular control over data retrieval. This approach allows you to specify the exact time window and aggregation intervals, potentially bypassing the caching delays inherent in the UI. You can use the /api/v2/analytics/interactions/queues endpoint to fetch real-time or near-real-time data with specific filters.
Here is an example configuration for a Python request using the Genesys Cloud SDK:
from purecloudplatformclientv2 import ApiClient, Configuration, AnalyticsApi
configuration = Configuration()
configuration.host = "https://api.eu-genesyscloud.com"
configuration.access_token = "your_access_token"
analytics_api = AnalyticsApi(ApiClient(configuration))
# Define the time range with a small offset to ensure data processing
time_range = "last-15-minutes"
# Fetch queue interactions
response = analytics_api.post_analytics_interactions_queues(
analytics_query_request={
"interval": "PT1M",
"date_from": "2023-10-01T00:00:00Z",
"date_to": "2023-10-01T23:59:59Z",
"group_by": ["queueId", "interval"],
"filters": {
"type": "queue",
"ids": ["your_queue_id"]
}
}
)
print(response)
This method provides more reliable data for reporting purposes. For further details on the Analytics API, refer to the official documentation. Adjust the date range and filters according to your specific needs to ensure accurate metric retrieval.