I want to correlate historical adherence data with our voice biometrics analysis.
If an agent was logged in but their audio stream shows zero speech activity for 20 minutes, the adherence API should flag them as non-adherent. But the API reports them as 100% adherent because their status was ‘On Queue’. Is there a way to query a more granular adherence signal?
In CIC (PureConnect), we had the IC Adherence module that showed second-by-second status changes in a grid view.
The Genesys Cloud WFM adherence API returns data in much coarser intervals. You get status snapshots rather than continuous telemetry. The granularity is sufficient for daily reports but terrible for real-time anomaly detection.
The endpoint you want is POST /api/v2/workforcemanagement/adherence/historical/query.
Fair warning: this endpoint is heavily rate-limited. If you query more than 100 users at a time, the response time degrades exponentially. I batch my requests at 50 users per call with a 2-second delay between batches.
for chunk in chunks(user_ids, 50):
body = {'userIds': chunk, 'startDate': start, 'endDate': end}
result = wfm_api.post_wfm_adherence_historical(body)
time.sleep(2) # Respect rate limits
If you are cross-referencing adherence with outbound dialer activity, be aware of a data gap.
When an agent is in a preview dialing campaign, the WFM adherence API may report them as ‘Idle’ during the preview period (when they are reviewing the contact record before dialing). This creates a false non-adherence flag. You need to filter out OUTBOUND_PREVIEW activity statuses from your adherence calculations.
Does the adherence query hit the Edge appliance or the cloud API?
I ask because our remote site Edge has limited connectivity, and I want to make sure the adherence polling script isn’t adding unnecessary traffic to an already constrained WAN link.