Adherence exception codes not filtering correctly in historical reports

Genesys Cloud WFM
1

Before you spend too much time rebuilding the math in Power BI, verify that your agents are actually logging in with the correct system state.

We had a bizarre issue where our IdP (Okta) was passing a malformed SAML assertion during SSO login. It didn’t break the login, but it delayed the presence sync. The agents appeared ‘Offline’ for the first 5 minutes of their shift, which generated a massive amount of historical adherence exceptions. Check your SAML tracer to ensure the auth handshake is completing cleanly.

2

Our executive dashboards rely heavily on accurate adherence metrics, but I’m having trouble matching the UI to the API.

I’m querying the /api/v2/workforcemanagement/managementunits/{id}/historicaladherencequery endpoint to pull last week’s data. In the Genesys Cloud WFM UI, our management team filters out specific exception codes (like ‘System Outage’). However, the JSON payload from the API seems to return the raw adherence percentages including those exceptions.

How do I apply the exception code filtering programmatically in the query body so my Power BI dashboards match the WFM supervisor views?

3

To build on that, if you are feeding this into a data lake via middleware, you have to flatten the nested arrays first.

Here is a quick Java snippet using the Genesys Cloud SDK that we use in our MuleSoft pipeline to extract the actual activity codes before sending it to our database for recalculation:

WfmHistoricalAdherenceResponse response = wfmApi.postWorkforcemanagementManagementunitHistoricaladherencequery(muId, body);
for (HistoricalAdherenceQueryResult result : response.getData()) {
    for (HistoricalAdherenceActuals actual : result.getActuals()) {
        String activityCategory = actual.getActivityCategory();
        String exceptionId = actual.getExceptionId();
        // Stream to Kafka topic for downstream BI filtering
        kafkaTemplate.send("wfm-adherence", buildPayload(activityCategory, exceptionId));
    }
}

It is methodical, but necessary since the API doesn’t do the math for you.

4

While I don’t work with WFM data directly, we face similar data normalization challenges in speech analytics.

When we query the transcripts for topic detection, the raw API payload contains a lot of noise that the native UI filters out. We have to calibrate our precision and recall models by manually scripting out the irrelevant speech segments. It sounds like you have to build a similar calibration layer in your BI tool to handle those WFM exception codes.

5

From a workforce management perspective, this discrepancy is a classic headache.

In the WFM UI, supervisors often use ‘Neutral’ adherence mappings for certain exception codes. If an agent is in an exception state mapped to Neutral, the UI recalculates the adherence percentage by essentially removing that time segment from the denominator.

The API, however, returns the absolute scheduled vs. actual data. You cannot filter the exceptions in the initial API request. You have to extract the full timeline, identify the segments tagged with your specific exception IDs, and recalculate the compliance percentage manually in your BI tool using the business logic.