we’re seeing a mismatch between the real-time predictive routing scores and the final transcript topics returned by the analytics api. the flow uses the predictive_routing data action to tag conversations based on initial intent, but the analytics/conversations/details endpoint returns different topics 15 mins later. this breaks our post-call quality evals because the routing decision was based on wrong data.
using python sdk genesys-cloud-python-sdk 2.1.4. the service account has analytics:conversation:view and predictive_routing:manage scopes. logs show the routing engine gets a 200 from the intent classifier, but the transcription service is still processing the audio chunks. is there a way to force the routing engine to wait for the transcription_status to be completed before calculating the final score? or am i stuck polling the transcript endpoint in a loop until topics populates? feels like a race condition in the platform. tried adding a 10s delay in architect but that just kills wait times. any config tweaks in the predictive routing strategy to handle this latency?
you’re hitting a known latency gap. the predictive routing engine scores in real-time, but the analytics API aggregates data asynchronously. by the time you poll /api/v2/analytics/conversations/details, the intent model might have re-evaluated the transcript or the routing score updated based on later dialogue.
don’t rely on the analytics endpoint for immediate feedback. instead, use the WebSocket Notification API to catch the routing:conversation:score event. it pushes the exact score used at the moment of routing.
if you need the final topic for QA, store that snapshot in your own DB when the routing:conversation:score webhook fires. comparing it against the final analytics topic later is fine for trend analysis, but don’t let the analytics lag break your live routing logic. the data actions are synchronous, so the tag should be correct at that exact millisecond.
switching to the websocket notifications fixed the timing issue immediately. the routing:conversation:score event fires exactly when the score changes, so there’s no more stale data in our post-call evals.
for anyone else dealing with this in python, here’s the quick setup using the sdk:
fwiv, make sure you’re filtering by specific queue or skill if your org is noisy. otherwise you’ll get flooded with events. ymmv with the payload structure depending on your platform version, but the core event name stays the same. this approach is way cleaner than polling every few seconds.
watch out for the websocket connection limits if you scale this out. the notification api is great for real-time feeds, but it’s not built for high-volume polling or long-term storage. you’ll hit connection caps fast if every agent session or eval tool opens a persistent socket.
also, those score events don’t always stick. the final transcript topics in analytics are the source of truth for reporting, so your quality evals might still show a mismatch if you only capture the initial predictive score. you’re essentially chasing a moving target.
better to stick with the analytics API but add a delay. wait for the conversation to close and then poll after the async aggregation finishes. here’s a simple python snippet using the sdk to check status before grabbing details:
from genesyscloud.analytics import AnalyticsApi
import time
analytics = AnalyticsApi(platform_client)
# Wait for conversation to reach a closed state
while True:
convs = analytics.get_analytics_conversations_details(
query=query_string,
expand=['routing']
)
if convs.entities and convs.entities[0].state == 'closed':
break
time.sleep(5)
# Now fetch the stable topics
details = analytics.get_analytics_conversations_details(
query=final_query,
expand=['analytics', 'routing']
)
it’s slower, but you’ll actually get the data that matches your reports. trying to sync real-time scores with final analytics is a headache.