Discrepancy in Service Level calculation using v2/analytics/conversations/aggregate interval data

Quick question about calculating Service Level percentage using raw Analytics API interval data. I am building a Django backend with Celery workers to poll POST /api/v2/analytics/conversations/aggregates/query for queue performance metrics. The goal is to compute the Service Level percentage (calls answered within target time / total offered calls) based on the returned interval objects. The JSON payload returns count.answered and count.wait but lacks a direct count.target field. I am currently summing count.answered across intervals and comparing it against a calculated threshold, but the resulting percentage deviates significantly from the Genesys Cloud dashboard. Here is the relevant snippet:

# Pseudo-code logic
for interval in response['interval']:
 answered += interval['count']['answered']
 # Missing logic for target time filtering
sl = (answered / offered) * 100

The API returns a 200 OK, so the query is valid, but the data granularity seems to aggregate across the entire time bucket rather than filtering by wait time. How do I correctly extract the count of calls answered within the specific target time from the aggregate interval response? The standard REST docs are ambiguous on this specific calculation.

Have you tried querying the serviceLevel metric group directly instead of trying to derive it from raw counts? The aggregate endpoint supports metricGroups: ["serviceLevel"] which returns count.answeredWithinTarget and count.offered pre-calculated, saving you the headache of handling interval boundaries.

The JSON payload returns count.answered and count.wait but lacks a…

Stop manually deriving service levels from raw counts. Use the serviceLevel metric group as suggested.

"metricGroups": ["serviceLevel"],
"interval": "PT1H"

This returns count.answeredWithinTarget and count.offered directly. My Postman collection validates this structure, avoiding boundary errors.