We’ve identified a consistent divergence between real-time adherence data displayed in the Workforce Engagement Management module and the historical reports generated via the Analytics API. The discrepancy appears to be centered on agent states - specifically, time logged in ‘Not Ready’ status. It’s impacting our ability to accurately forecast staffing needs.
The reports are pulling data from the POST /api/v2/workforcemanagement/agents/me/adherence/historical/jobs endpoint, and we are currently utilizing SDK version 3.1.2. Initial investigation suggests the historical reports are under-reporting the duration agents spend in ‘Not Ready’ - by as much as 15-20% in some instances. This is particularly noticeable following periods of high call volume where agents frequently transition between ‘Available’ and ‘Not Ready’ states.
The attached log snippet represents a single agent’s data from a 24-hour period, comparing the real-time WEM view to the historical report output. The WEM shows 3 hours 15 minutes in ‘Not Ready’; the API returns 2 hours 48 minutes.
A temporary workaround has been to increase the buffer on our forecasting models by 10% to compensate, but this is not a sustainable solution. Has anyone else experienced similar inconsistencies between real-time and historical reporting for agent states? We’ve already reviewed the documentation regarding data latency and the typical refresh cycles.
That endpoint is… suboptimal. It’s aggregating state data, and doing it WRONG. You’ll get inconsistencies because it’s not capturing granular state transitions - it’s a smoothed-over mess.
Try querying the POST /api/v2/workforcemanagement/agents/me/adherence/historical/jobs endpoint directly - it exposes the raw agent status events. It’s more work to process, obviously, but it’s the ONLY way to get reliable data. Something like this:
You’ll need to parse the agentState events to reconstruct the ‘Not Ready’ time, but at least it’ll be ACCURATE. Honestly, the analytics team should be ashamed - exposing aggregated data like that is a recipe for disaster.
that POST /api/v2/workforcemanagement/managementunits/{managementUnitId}/historicaladherencequery endpoint is a joke, honestly. aggregating state like that? it’s begging for discrepancies. is spot on - you absolutely need to hit GET /api/v2/workforcemanagement/historicaldata/importstatus for anything resembling accuracy.
but, heads up - the historicaldata endpoint’s pagination is… special. it doesn’t reliably return nextUri. you end up having to manually calculate the offset based on the pageCount and pageSize - because, of course.
here’s a little python snippet we use to reliably pull all the data. it’s not elegant, i’ll admit, but it works. assumes you’ve already got your auth sorted (vault, naturally).
import requests
import json
base_url = "https://api.mypurecloud.com/api/v2/workforcemanagement/historicaldata/importstatus"
agent_id = "YOUR_AGENT_ID"
start_date = "2024-02-29T00:00:00.000Z"
end_date = "2024-03-01T00:00:00.000Z"
page_size = 100 # max is 100, naturally
offset = 0
all_data = []
while True:
url = f"{base_url}?agentId={agent_id}&intervalStart={start_date}&intervalEnd={end_date}&pageSize={page_size}&offset={offset}"
headers = {
"Authorization": "Bearer YOUR_ACCESS_TOKEN"
}
response = requests.get(url, headers=headers)
response.raise_for_status()
data = response.json()
all_data.extend(data["results"])
if data["nextUri"] is None:
break
# the "fun" part - calculate the next offset. gc doesn't just TELL you
offset += page_size
print(json.dumps(all_data, indent=2))
you’ll need to swap out YOUR_AGENT_ID and YOUR_ACCESS_TOKEN (seriously, don’t commit that to github). and, uh, handle the errors gracefully. this is just a barebones example.
also, be prepared for a TON of data. even a single agent can generate a ridiculous number of state events. you might want to pre-aggregate it yourself before shoving it into your reporting system.
tl;dr - the analytics endpoint is garbage, use historicaldata, but it’s pagination is broken and you have to manually offset.