Could someone explain the latency and data inconsistency observed when retrieving evaluation scores via the Genesys Cloud Quality API compared to the real-time dashboard? We are managing a complex environment with 15 BYOC trunks, and while our SIP registration and carrier failover logic remain stable, we are seeing significant drift in the quality metrics reported for agents in the Singapore timezone. Specifically, when querying the /api/v2/quality/evaluations endpoint using the Python SDK (v1.12.0), the score field often returns a value that is 5-10 points lower than what is displayed in the Quality Management UI. This discrepancy appears immediately after an evaluation is submitted but resolves itself after approximately 45 minutes, suggesting a background aggregation job is running asynchronously.
The issue is particularly problematic for our automated reporting pipelines, which rely on near-real-time data to trigger corrective actions in our Architect flows. We have verified that the evaluation criteria and weightings are correctly configured and that the status field returns completed in both the API response and the UI. However, the raw score calculation seems to be cached or delayed in the API layer. We have attempted to invalidate the cache by re-fetching the evaluation details with the ?forceRefresh=true parameter, but this only increases the response time without correcting the score value. The API response headers do not indicate any caching directives that would explain this delay, and the X-Request-Id logs show consistent processing times across multiple attempts. Given our reliance on precise analytics for carrier performance correlation, this lag creates a blind spot in our operational visibility. Is there a specific configuration or API version update that addresses this synchronization gap, or are we required to implement a polling mechanism with a manual offset to align the data?
The docs actually state that real-time dashboard metrics and bulk export data utilize distinct processing pipelines, which often leads to perceived discrepancies in latency and score calculation. From an architectural perspective, this is not necessarily a bug but a design choice to optimize performance for different user groups. The Quality Assurance module processes evaluations asynchronously, meaning the UI might show a preliminary state while the backend is still aggregating final scores for the bulk export job.
To mitigate this drift, especially in high-volume regions like APAC, one should adjust the evaluation_completion_timeout within the specific quality profile settings. Setting this parameter to a lower value, such as 300 seconds, can force earlier finalization of scores for dashboard visibility, though it may impact the depth of automated scoring rules. Additionally, ensure that the auto_complete_evaluation setting is enabled. This prevents manual evaluations from lingering in a “pending” state, which often skews the real-time averages compared to the finalized dataset exported via API.
Furthermore, consider implementing a data_refresh_interval of 60 seconds in your custom dashboard widgets. The default interval is often longer, causing the UI to display stale data while the API pulls the most recent committed records. It is also crucial to verify that the timezone_offset is correctly applied in the export query parameters. Misalignment here can cause evaluations to be grouped into incorrect calendar days, leading to significant score drift when comparing daily reports.
A common fix involves aligning the reporting_timezone in the Quality profile with the operational timezone of the agents. This ensures that all timestamp-based aggregations are consistent across both the UI and the exported data. By standardizing these configuration parameters, the variance between real-time views and bulk exports should decrease significantly.