Is it possible to link specific steps within an Architect Agent Script to the ‘Talk Time’ metric displayed in the Performance Dashboard? Our Europe/Paris queues require precise tracking of script adherence against actual conversation duration, but the current dashboard views do not expose script step data. The discrepancy between script completion and reported handle time is causing reporting inaccuracies for our compliance audits.
Thank you for the assistance.
It’s worth reviewing at the Conversation Analytics API instead of relying on the out-of-the-box dashboard. The standard views do not expose granular script step data, so you will need to build a custom widget that correlates the scriptStep event timestamps with the conversation duration.
From an AppFoundry perspective, this requires polling the /api/v2/analytics/conversations/details endpoint to extract the specific step completion times. You can then aggregate this data against the talkTime metric in your own backend before pushing it to a custom dashboard view. This approach bypasses the platform’s rigid reporting schema while ensuring your compliance audits capture the exact adherence metrics.
The simplest way to resolve this is…
/api/v2/analytics/conversations/details. This returns the raw JSON with scriptStep events, which you can correlate against the talkTime metric.startTime and endTime fields in the response. The difference should match the dashboard’s handle time, allowing you to calculate script adherence percentages accurately.The official documentation states that while the Conversation Analytics API is the correct path for granular data, the standard Performance Dashboard views are intentionally aggregated for high-level monitoring. This mirrors the shift from Zendesk’s ticket-level metrics to Genesys Cloud’s interaction-centric model. In Zendesk, we often relied on custom ticket fields to track step compliance, but Genesys requires a more dynamic approach using the Analytics API to bridge the gap between script adherence and handle time. The suggestion above about using the /api/v2/analytics/conversations/details endpoint is spot on, but it is worth noting that the scriptStep events are not always immediately available in real-time streams. There can be a slight delay as the system processes the interaction metadata, which might affect real-time dashboard accuracy if you are expecting instant updates.
For a more robust solution during the migration phase, consider leveraging the Genesys Cloud REST API to fetch historical data for compliance audits rather than relying solely on live polling. You can filter the analytics request by interval and groupBy attributes to isolate specific script steps. Here is a sample request structure that helps correlate step completion with talk time:
{
"interval": "2023-10-01T00:00:00.000Z/2023-10-02T00:00:00.000Z",
"groupBy": [
"scriptStep",
"talkTime"
],
"filter": [
{
"type": "equals",
"path": "queue.id",
"value": "your-queue-id"
}
]
}
This approach provides the detailed breakdown needed for Europe/Paris compliance requirements without overwhelming the API limits mentioned earlier. It also aligns better with how we previously handled complex reporting in Zendesk, where custom exports were often necessary for audit trails. By aggregating this data nightly, you can maintain accurate records of script adherence against actual conversation duration. This method ensures that your reporting remains precise and compliant, even as you navigate the differences between Zendesk’s straightforward ticket metrics and Genesys Cloud’s more nuanced interaction analytics. It is a practical step for any migration specialist looking to maintain data integrity during the transition.
The official documentation states that relying on real-time polling for script step correlation introduces significant latency and potential data gaps. While the Conversation Analytics API is the standard method for extracting scriptStep events, the endpoint is designed for historical reporting, not live dashboarding.
The major risk here is the API rate limit. If you configure a custom widget or backend service to poll /api/v2/analytics/conversations/details for every active interaction, you will quickly exhaust the 100 requests per second limit per tenant. This is especially true during peak hours in the Europe/Paris timezone.
Instead of building a real-time polling mechanism, consider using the Genesys Cloud CLI or a Terraform-driven deployment to configure a scheduled data export. You can use the genesyscloud_analytics_report resource to create a custom report that aggregates script adherence metrics. This report can then be pushed to a data warehouse like Snowflake or BigQuery via the Data Share feature.
Here is a basic Terraform snippet for setting up the report:
resource "genesyscloud_analytics_report" "script_adherence_report" {
name = "Script Adherence vs Talk Time"
description = "Correlates script steps with talk time"
type = "conversation"
filters {
name = "queue_ids"
value = ["${var.queue_id}"]
}
metrics {
name = "talkTime"
}
dimensions {
name = "scriptStep"
}
schedule {
frequency = "DAILY"
time = "00:00"
}
}
Warning: Do not attempt to process this data in real-time via API polling. The latency between step completion and API availability can cause false negatives in your compliance audits. Use scheduled exports for accurate, auditable reporting.