Context:
Region: Europe/Paris
Environment: Genesys Cloud Performance Dashboard
Question:
What is the correct way to isolate Agent Scripting duration from total Agent Handle Time (AHT) in the Queue Performance view? The current metrics show inflated AHT values during peak hours, likely due to mandatory compliance scripts. The business requires accurate productivity reporting, excluding scripted interactions. Standard filters do not separate script time from talk time. Is there a specific metric configuration or Architect flow adjustment needed to exclude these segments from the primary AHT calculation?
Oh, this is a known issue… The standard Queue Performance view in Genesys Cloud aggregates handle_time as a single block from answer to disposition. It does not natively split script_duration from talk_time or work_time in the default dashboard widgets. This causes the inflation you are seeing during peak hours when compliance scripts are mandatory.
To isolate the scripting duration, you must look at the underlying data structure rather than the summary view. The handle_time metric is a sum. You need to query the interactions API endpoint directly to get granular timestamps. Specifically, look at the wrap_up_code and disposition fields alongside the start and end timestamps of the interaction segments.
If you are using JMeter for validation or custom reporting, you can extract the script_execution_time from the analytics:interactions:details export. The JSON response contains a script object with start_time and end_time. Calculate the delta there.
Example calculation logic:
let totalAHT = interaction.end_time - interaction.start_time;
let scriptDuration = script.end_time - script.start_time;
let pureHandleTime = totalAHT - scriptDuration;
However, be aware of API rate limits when pulling this data for high concurrency. If you are querying for thousands of interactions per hour, you will hit the 429 limit on the analytics endpoints. The default limit is around 100 requests per minute for detailed interaction exports. You must implement exponential backoff in your script.
For dashboarding, the best approach is to use the Genesys Cloud Analytics API to push this calculated pure_handle_time into a custom data warehouse or a simple CSV export scheduled via the API. Then, visualize that cleaned data in PowerBI or Tableau. The native dashboard cannot filter out script time natively. This is a limitation of the current metric definitions in the Paris region configuration, not a bug. The metric definition treats script time as part of the agent’s active handling.
If I recall correctly, the Performance Dashboard aggregates handle time without distinguishing script segments. A better approach is using the Bulk Export API to pull interaction metadata. This allows external processing to subtract script_duration from total handle_time for accurate AHT calculations.
Warning: Ensure your S3 bucket permissions allow the export job to write the detailed metadata files.
Make sure you check the API rate limits before running bulk exports. The /api/v2/analytics/interactions endpoint throttles heavily after 100 requests per minute. This creates bottlenecks during peak load tests.
Use JMeter to simulate the export job. Verify the x-rate-limit-remaining header drops to zero. Adjust thread counts accordingly to avoid 429 errors.