How does the Virtual Agent API actually handle DNC suppression when the contact list updates mid-flow? There’s a Python script pushing PATCH /api/v2/outbound/contacts every hour, but the bot keeps dialing numbers that just got flagged, and the response payload shows dnc_status: "not_active" even after the update hits.
The flow just hangs on the wait step and doesn’t recover before dropping the call after 30 seconds.
Try disabling the default DNC cache inside routing engine. We ran an A/B test splitting traffic between cached lookups and direct DB queries. The ML scoring model dropped 3% accuracy on direct path, but suppression lag fixed itself.
Add a 5-second timeout to that wait step before bot queries the contact. This gives sync worker time to refresh the flag. It’s pretty standard fix.
Check if your script hits the batch endpoint or single-contact patch. The batch route often skips validation webhook, same issue that thread mentioned about S3 exports. Are you pushing individual records or a full dump?
Switch to async propagation toggle in admin settings. The predictive routing beta weights handle DNC overrides differently now. Legacy scoring models just ignore mid-flow patches. You’ll need this change.
Wait step config usually looks like this: {"type": "wait", "timeout_ms": 5000, "action": "proceed"}. Just paste it into your flow builder. The timeout value depends on region latency anyway.
PureCloudPlatformClientV2 forces a hard cache bust when you hit that single-contact endpoint. The batch route just queues it up. You’ll see the dnc_status flip immediately if you drop the offset param. It’s just masking the sync delay. Honestly, the routing engine caches way too aggressively.
Run that right after the patch. The DNC flag routes through a separate sync queue and takes roughly 8 to 12 seconds to hit the Virtual Agent cache. It’s a hard limit. The endpoint just starts dropping requests if you push too hard.
The cache invalidation delay is expected behavior under heavy load. When concurrent call volumes spike, the outbound contact store prioritizes read throughput over write consistency. The 8-second lag mentioned above is the standard propagation window for the sync worker.
If the bot flow hangs, that’s a configuration error on the wait step. The workaround is to force a cache bust on the client side. Setting Cache-Control: no-cache works, but it adds latency to every lookup. A better approach for high-throughput flows is to batch the updates and poll the status endpoint directly.
Ran a JMeter test with 500 TPS on the patch endpoint using the standard thread group. The throughput dropped significantly when forcing hard cache busts across the board. The tail latency jumped from 40ms to 450ms.
Warning: Do not hammer the patch endpoint during peak hours. The rate limiter will throttle the whole tenant.