Just noticed that the metadata payload in the final S3 bucket does not match the initial request parameters when pulling digital channel recordings. We are running a bulk export job for legal discovery purposes, specifically targeting WhatsApp interactions from last week. The environment is Genesys Cloud v2.104, using the Recording API to trigger the job.
The issue appears when the session spans across two different business days in GMT+0. The start_time and end_time fields in the exported CSV manifest show the correct UTC timestamps. However, the agent_id field is null for roughly 15% of the records. These are not abandoned chats; agents were actively engaged. The chain of custody requirement for our legal team demands that every message be linked to a specific user ID for audit trails.
We have verified that the agents involved have the correct ‘Recording:Export’ permissions. The RBAC roles seem fine. The problem seems isolated to the digital channel metadata mapping. When we look at the real-time streaming webhooks, the data is present. It is only in the asynchronous bulk export that it drops. We suspect this might be related to how the system handles session segmentation for messaging platforms versus voice calls.
Is there a known limitation with the digital_channel_recording type in the bulk export API? We need to ensure the data integrity before submitting this to the discovery team. How can we force the API to include the agent_id in the metadata for digital channel sessions that span multiple calendar days?
The discrepancy likely stems from how the Performance dashboard aggregates data versus the raw timestamp handling in the export job. When sessions cross midnight in GMT+0, the system may split the record or attribute the end_time to the next business day’s batch. This creates a mismatch if the export job filters strictly by the initial start_time without accounting for the rolled-over date.
A more reliable approach for legal discovery is to bypass the standard bulk export job for this specific query and instead utilize the Conversation Detail view with a custom report configuration. Define a report with the following filters:
- Channel Type:
WhatsApp
- Date Range: Set to “Custom” and ensure the timezone is explicitly set to
GMT+0 to avoid local tenant timezone conversions.
- Metric: Select
Session Duration and Start Time.
Then, export this report directly. This method ensures that the metadata aligns with the dashboard’s internal aggregation logic, which is often more consistent for cross-day sessions than the raw API export, which can be sensitive to microsecond-level timestamp shifts.
Additionally, verify the metadata payload structure in the S3 bucket. Look for the cross_day_boundary flag. If present, it indicates the system has flagged the session for potential split accounting. In such cases, the end_time in the export might reflect the final interaction timestamp, which could be in the subsequent day, causing the perceived inconsistency.
For future bulk exports, consider adding a buffer to the end_time filter in the API request to capture any late-arriving metadata updates. This can be done by adjusting the filter parameter in the POST request:
{
"filters": [
{
"field": "start_time",
"operator": "gte",
"value": "2023-10-01T00:00:00.000Z"
},
{
"field": "end_time",
"operator": "lte",
"value": "2023-10-07T23:59:59.999Z"
}
]
}
This ensures that all parts of a cross-day session are included in the same export batch, reducing the chance of metadata fragmentation.
have you tried using the recording id instead of time ranges for the bulk export? the api documentation notes that time-based filters can be unreliable for sessions crossing midnight boundaries.