Reporting v2 is returning mismatched totals for agent occupancy. The summary data for a 24 hour period doesn’t align with the sum of the 30 minute intervals. This is happening in production across multiple ACME tenants.
The DISCREPANCY is roughly 4-7% across the board. Data is being pulled via the Reporting v2 API using a custom Python wrapper.
Steps to reproduce:
Query /api/v2/reporting/intervals for a specific agent.
Sum the occupancy values for all 48 intervals in a day.
Compare against the daily total from the summary endpoint.
The summary endpoint reports 82% while the interval sum totals 76%. This is weird. Caching was ruled out by using different timestamps for the requests.
payload = {
"interval": "2023-10-24T00:00:00Z",
"duration": "PT30M",
"metrics": ["occupancy", "handleTime"],
"agentIds": ["12345"]
}
# Response returns 200 OK but values are inconsistent
The request is sent with a valid client_credentials token. It was noted that the latency on the response is higher than usual today. No 429s are being triggered.
This looks like a classic case of how the system handles interval boundaries versus a flat daily summary. When you sum up 30 minute buckets, you’re often seeing a rounding effect or a misalignment in how the system attributes activity that spans across the top of the hour. In my experience with WFM reporting on Genesys Cloud, occupancy calculations can get tricky if there’s a lot of agent state switching right at the interval mark. The daily summary usually looks at the total duration of the state over the whole period, which is why it doesn’t always match the sum of the parts.
Are you seeing this discrepancy more heavily in queues with high call volume or is it consistent across all agent groups? It’d be helpful to see a screenshot of the specific intervals where the gap is widest. That would help determine if it’s a boundary issue or a general calculation difference.
# Example request to check current reporting settings
import PureCloudPlatformClientV2
from PureCloudPlatformClientV2.rest import ApiException
api_instance = PureCloudPlatformClientV2.AnalyticsApi()
try:
settings = api_instance.get_analytics_reporting_settings()
print(settings)
except ApiException as e:
print(f"Exception when calling AnalyticsApi->get_analytics_reporting_settings: {e}")
First, what are the data retention policies for these reporting metrics in your org? If you’re purging or archiving data too aggressively, you’ll see gaps that manifest as these percentages.
The discrepancy is likely architectural. When you sum 30-minute intervals, you’re essentially performing a manual aggregation of pre-calculated snapshots. The daily summary, however, is often a different calculation logic that handles “straddling” events-activities that start in one interval and end in another-differently than a simple sum. It’s a common pitfall where the summary accounts for the total duration of the event, while interval sums might split or truncate that time.
Check your settings via /api/v2/analytics/reporting/settings to see if there’s a misalignment in how the org handles these aggregations. If you’re using this data for quality audits, be careful with evaluation form versioning. Changing a form version mid-stream can skew how these occupancy metrics are tied to performance scores.
It’s probably not a data loss issue like the earlier reply suggested. The mismatch usually happens because AGGREGATION_INTERVALS handle state changes differently than a flat daily summary. If an agent is in a specific status that straddles two intervals, the system might attribute that time differently in the summary versus the 30 minute buckets. It’s a classic rounding quirk with OCCUPANCY calculations.
Solution:
Instead of summing the intervals manually in Python, try pulling the data via an export to see if the numbers align better. You can trigger a request using the reporting exports endpoint:
POST /api/v2/analytics/reporting/exports
{
"exportType": "AGGREGATED_METRICS",
"interval": "2023-10-01T00:00:00.000Z/2023-10-02T00:00:00.000Z"
}
Compare the results from the EXPORT_METADATA to the manual sum. If they still don’t match, the issue is definitely in how the OCCUPANCY_PERCENTAGE is being weighted across those interval boundaries.
Maybe check the timezone handling in that Python wrapper. If the intervals aren’t perfectly aligned to UTC, you’ll get those weird offsets.
The docs for reporting settings mention that “Timezone offsets must be specified in UTC”. We hit a similar drift issue with our summaries and I spun up a quick Lambda to patch this by forcing everything to UTC before the aggregation.