Dealing with a very strange bug here with our Predictive Outbound campaign setup in the us-east-1 region while attempting to validate max throughput for a new client. The environment is Genesys Cloud 2024-10.0, and we are using the Python SDK v3.3.1 for initial configuration pushes before switching to manual trigger tests via JMeter. The campaign is configured with a dial ratio of 1.5 and a target answer rate of 60%. When we push concurrent API calls to the /api/v2/outbound/campaigns/{campaignId}/trigger endpoint using a JMeter thread group of 50 users, the system accepts requests initially. However, once the active queue depth hits roughly 85% of the defined agent capacity (which is set to 100 agents in the associated routing queue), the API responses degrade significantly. Instead of standard 202 Accepted responses, we start seeing a mix of 503 Service Unavailable and 429 Too Many Requests errors. The error payload for the 429s includes a Retry-After header of 5 seconds, but even spacing the requests to one every 6 seconds does not resolve the backlog; the queue simply stops processing new attempts. Interestingly, the WebSocket connection to the /v2/outbound/previewmonitor remains stable, indicating the issue is isolated to the predictive dialing logic rather than the underlying connectivity. We have verified that the IP allow-listing is correct and that the rate limits for our organization tier are not being exceeded globally, only for this specific campaign context. The Architect flow attached to the campaign has a simple IVR with no complex script blocks that would cause latency. Is there a hidden throttling mechanism in the predictive engine that activates based on queue saturation percentage, or is this a known issue with the trigger endpoint under high concurrency? We need to understand if we should adjust the dial ratio dynamically or if this is a platform limitation we need to work around in our load testing scripts.
The way I solve this is by ensuring the trigger payload explicitly defines the batch size to prevent the platform from attempting to process the entire queue in a single synchronous operation.
{
"batchSize": 100,
"force": true
}The best way to fix this is to align your campaign trigger configuration with the standard WFM scheduling principles. When predictive outbound campaigns hit capacity limits, it often mirrors how agent schedules get blocked if adherence rules are too strict. The suggestion above regarding batch size is solid, but you also need to ensure the dial ratio isn’t conflicting with the real-time availability of agents.
Try adjusting the campaign settings to include a explicit maxConcurrentCalls parameter that matches your available agent count multiplied by the desired utilization rate.
{
"dialRatio": 1.5,
"maxConcurrentCalls": 150,
"batchSize": 100
}
This prevents the system from overcommiting resources. We usually see 429 errors when the platform tries to push more calls than the current schedule can handle. Check your WFM schedule adherence for the agents assigned to this campaign. If they are on break or in a post-call work state, the available capacity drops, causing the queue to stall.