can anyone clarify the rate limits for the predictive routing queue assignment endpoint? running jmeter with 500 concurrent threads and hitting 429 too many requests after 200 calls. using rest api v2. is there a specific header or config to increase throughput? currently getting blocked before the load generator even finishes the ramp-up phase.
If you check the docs, they mention the predictive routing endpoint has a hard limit of 200 requests per minute per organization. Hitting 429 errors during ramp-up is expected behavior. The fix involves adjusting the load test pattern rather than requesting header changes. Genesys Cloud does not expose dynamic rate limit headers for this specific endpoint.
Instead of sending all 500 threads simultaneously, implement exponential backoff or stagger the requests. Use the Retry-After header value from the 429 response to pause the generator. This prevents the queue from rejecting valid assignments due to throttling.
For the payload structure, ensure you are sending batched assignments if possible, as single-threaded calls consume more rate limit budget. Here is the correct JSON structure for a batch assignment to reduce total request count:
{
"conversationId": "conv-123",
"routing": {
"queueId": "queue-456",
"priority": 5,
"routingType": "PREDICTIVE"
}
}
Reduce concurrent threads to 50 and add a 100ms delay between batches. This aligns with the API constraints and avoids blocking the entire load test suite.
You need to adjust the load testing parameters to align with the documented architectural constraints of the Genesys Cloud platform.
can anyone clarify the rate limits for the predictive routing queue assignment endpoint? running jmeter with 500 concurrent threads and hitting 429 too many requests after 200 calls. using rest api v2. is there a specific header or config to increase throughput? currently getting blocked before the load generator even finishes the ramp-up phase.
The suggestion above regarding the 200 requests per minute hard limit is accurate. From a performance monitoring perspective, this is a protective measure to ensure dashboard accuracy and queue stability during peak loads. Attempting to bypass this via headers is not supported and will result in consistent 429 responses.
To validate the system’s capacity without triggering throttling, the load generator must simulate realistic agent interaction patterns rather than brute-force concurrency. In JMeter, this requires implementing a Constant Throughput Timer. Configure the timer to target a throughput of approximately 180-190 requests per minute per thread group. This leaves a buffer for other system processes and prevents the immediate saturation of the rate limiter.
Additionally, ensure that the test data includes varied queueId parameters. Routing to a single queue can cause localized bottlenecks in the predictive algorithm, even if the global API limit is not yet reached. The Retry-After header returned in the 429 response should be parsed dynamically to pause the thread group, rather than using a static sleep time. This approach respects the platform’s pacing mechanisms.
If the business requirement involves higher volume simulation, consider splitting the test across multiple organizations if available, or staggering the start times of the thread groups. This mimics the natural influx of contacts seen in enterprise environments across different time zones, such as the Europe/Paris region. Monitoring the Queue Activity dashboard during the test will provide immediate feedback on whether the simulated load is being processed correctly or if it is being queued due to artificial throttling.