Pulling raw interval data from /api/v2/analytics/queues/queueid/summary for a custom SLA dashboard. The goal is to calculate the Service Level percentage (e.g., 80% within 20s) manually rather than relying on the pre-aggregated serviceLevelPercent field, which sometimes feels opaque for custom thresholding.
The logic seems straightforward: sum handled calls where waitTime < threshold and divide by total handled. Here’s the snippet I’m using to process the response:
const calculateSL = (data, thresholdSeconds) => {
const totalHandled = data.intervals.reduce((acc, interval) =>
acc + interval.handleStats.handled, 0);
const withinSL = data.intervals.reduce((acc, interval) => {
// Assuming waitTime is in milliseconds in the raw payload
const waitMs = interval.handleStats.avgWaitTime;
// This logic feels wrong because avgWaitTime isn't a count
return acc + (interval.handleStats.handled);
}, 0);
return (withinSL / totalHandled) * 100;
};
The issue is the payload structure. handleStats gives me handled, abandoned, and avgWaitTime, but it doesn’t give me a count of calls that were specifically under the threshold. The avgWaitTime is an average, so I can’t use it to filter individual calls. I’ve looked at the queue/summary endpoint docs and don’t see a breakdown of handled calls by wait time buckets in the interval object itself.
Is there a specific query parameter or a different endpoint (like /api/v2/analytics/queues/queueid/details) that returns a histogram or bucketed data for wait times? Or am I missing a field in the handleStats object that indicates the count of calls meeting the SLA criteria? The current approach requires guessing based on averages, which is inaccurate for precise reporting. Need to know if this data is available via the standard Analytics v2 API or if I need to fall back to the older reporting APIs.
def calculate_service_level(interval_data, threshold_seconds):
total_handled = 0
within_threshold = 0
for bucket in interval_data:
# Ensure we are looking at voice/queue data
if 'handled' not in bucket:
continue
total_handled += bucket['handled']
# The waitTime in the interval data is usually an integer representing the bucket center or max
# Check if the bucket's wait time is less than or equal to the threshold
if bucket['waitTime'] <= threshold_seconds:
within_threshold += bucket['handled']
if total_handled == 0:
return 0.0
return (within_threshold / total_handled) * 100
The issue you’re running into is likely how the waitTime field is structured in the interval response versus what you’re expecting. The API doesn’t return individual call records. It returns aggregated buckets. If you’re summing handled calls where waitTime < threshold, you need to be careful about which side of the inequality you use and how the buckets are defined.
In Genesys Cloud, the waitTime in the interval data represents the upper bound of that specific time bucket. So if you have a bucket with waitTime: 20, it includes all calls that waited up to 20 seconds. If your SLA is “80% within 20 seconds”, you should include that bucket. Using strict less-than (<) might exclude the exact threshold bucket depending on how the rounding is handled, leading to a lower percentage than expected.
Also, make sure you’re filtering for the correct metricId. The summary endpoint can return multiple metrics if not scoped correctly. You want to ensure you’re looking at metricId: "handled" and that you’re aggregating across the correct groupings (usually by interval). If you’re pulling data for a long period, the intervals might be coarser (e.g., 5-minute buckets), which can skew the precision. For a custom dashboard, I’d recommend sticking to the serviceLevelPercent field if the discrepancy is small, as it’s calculated server-side with higher precision. But if you need to do custom thresholding, the logic above should get you closer to the reported number. Just double-check the waitTime values in the raw JSON response to see how they align with your threshold.