Hey everyone, jsonjester90 here. I wanted to walk through how we’re structuring our analytics pipeline step by step, since the aggregation logic can get pretty tangled if you don’t map out the reasoning carefully.
genesys-cloud-node-sdk handles the initial fetch, but the interval aggregation is where things get messy. To break this down, step one involves pulling directly from /api/v2/analytics/summaries/aggregates/query while passing interval: "PT15M" to grab our handling time and wrap-up durations. Once that payload lands, we aggregate per agent and queue first to establish a clean baseline before any transformations occur.
genesys-cloud-node-sdk then feeds into our filtering stage, where we strip out non-productive time using a quick conditional like record.wrapUpTime > 300 ? 0 : record.wrapUpTime. From there, the next step calculates efficiency ratios against a target threshold of 75%. This gives us a normalized metric that accurately reflects productive agent behavior before we move into anomaly detection.
genesys-cloud-node-sdk outputs the raw metrics, which we then pass into a basic z-score calculation written in TypeScript. The reasoning here is straightforward: we compute zScore = (val - mean) / stdDev for each data point to measure statistical deviation. If that value crosses 1.5, we automatically flag it as an outlier for downstream review.
genesys-cloud-node-sdk also provides the time-series data we need for generating utilization trends over configurable windows. Because real-world telephony data is rarely perfect, we have to handle missing data points with interpolation. The function just checks if (!bucket.value) bucket.value = (prev + next) / 2 to fill the gaps smoothly and maintain a continuous trend line.
genesys-cloud-node-sdk ties into our internal routing, and we’ve got a REST endpoint wrapping all this at /internal/metrics/utilization. However, we’re hitting a weird edge case where the response payload keeps dropping the trend array when the window shifts past midnight. It’s definitely a boundary condition we’re still debugging on the infrastructure side.
genesys-cloud-node-sdk streams the records into our processing pipeline, and here’s the aggregation loop we’re running: data.records.reduce((acc, rec) => { acc[rec.agentId] = (acc[rec.agentId] || 0) + (rec.handlingTime - Math.min(rec.wrapUpTime, 300)); return acc; }, {}). This ensures we’re only counting productive handling time per agent while maintaining referential integrity across the dataset.
genesys-cloud-node-sdk returns sparse arrays on low-volume queues, which introduces a mathematical edge case. We’re getting NaN in the efficiency ratio when the denominator drops to zero, so we’ll need to add a guard clause there. The dashboard integration expects a flat JSON object with agentId, utilizationPct, and trendLine, but the interpolation keeps dropping values anyway when the buckets are too empty to average.
{
"agentId": "12345",
"utilizationPct": null,
"trendLine": [],
"error": "Missing data points in bucket range"
}