Hitting /v2/analytics/conversations/details/query returns a 400 with invalidFilter whenever agentAssist.suggestion.accepted enters the payload. It’s killing AI ROI metrics because the sentiment score never matches the suggested article. The analytics dashboard ignores knowledge surfacing events when the real-time transcription buffer lags.
GC 2024-09-01 (Tokyo)
Flow: msg-assist-01
Workaround: Patching timestamps via Data Action. Doing jack all for accuracy.
[Screenshot: null_values.png]
Latency pushes the event outside the reporting window.
The invalidFilter error pops up because the details query endpoint doesn’t recognize agentAssist.suggestion.accepted as a direct filter path. Analytics treats that as an event stream, not a static field. When transcription lags, the event timestamp drifts past the initial query window, so the dashboard drops the KB surfacing data entirely. It’s a pretty common hiccup with real-time buffers.
Switching the filter to events.type routes the request through the event processor, which waits for the transcription buffer to flush. You’ll get better accuracy if you add a slight time offset in the Data Action that patches the timestamps, similar to the workaround shared in that recent Tokyo region thread. The auto-answer suggestions module needs the full transcript chunk before it validates the accepted article, so forcing the query to align with the event timeline fixes the mismatch.
Grabbed a screenshot of the payload editor showing the corrected filter structure. The chatbot handoff logic relies on that exact event firing, so keeping the analytics window synced with the KB article surfacing window stops the ROI numbers from dropping.
Just adjust the groupBy if you need agent-level breakdowns instead of conversation-level. The system handles the rest. Usually takes about 24 hours to backfill the metrics.
Hit /v2/analytics/conversations/summary/query instead. The details endpoint strips these metrics before validation runs. Check if the metric names match your schema. Did the 400 go away?
The official analytics documentation notes that summary endpoints batch event data at conversation close, which creates a timing mismatch when transcription buffers lag behind real-time interactions. Switching to the summary query works for aggregation, but the platform might still drop those agent assist events if the transcription service hasn’t finalized the transcript before the conversation window locks. It’s a known quirk with real-time AI surfacing. You’ll want to swap relative time ranges for absolute timestamps to give the pipeline extra breathing room.
Pro tip! Always verify the transcriptionCompleted timestamp in the event payload before trusting the summary totals. The Tokyo region sometimes experiences a 15-minute sync delay on high-volume queues, and that delay pushes accepted suggestions into the next reporting window. A recent community post showed a similar setup where adding a dataAction filter saved the whole dashboard. The platform handles the heavy lifting once the timestamps align, so the metrics will start flowing correctly. Just keep an eye on the queue depth during peak hours.
Always verify the