Stuck on a specific rate limiting issue with the Predictive Routing API during high-volume load testing. We are running a simulation to validate our outbound campaign capacity before a major client launch. The environment is Genesys Cloud US1, and the test is executing from the America/New_York timezone to match the production server region and minimize latency.
Context:
The load test uses JMeter version 5.6.2 with the HTTP Request Defaults configured to handle OAuth2 token refresh. We are targeting the POST /api/v2/outbound/campaigns/{campaignId}/contacts endpoint to inject contacts into a Predictive Routing campaign. The campaign is configured for a dial rate of 50 calls per second. Our JMeter script ramps up 100 concurrent threads, each making requests at an interval of 100ms. This should theoretically stay well within the documented API rate limits for bulk contact import (100 requests per second per organization). However, as soon as the thread count hits 50, we start seeing a significant drop in success rates. The response logs show a mix of 200 OK and 429 Too Many Requests errors. The 429 responses include a Retry-After header, but the values are inconsistent, ranging from 0 to 5 seconds. We have verified that the X-Genesys-Request-Id headers are unique for each request to avoid caching issues. The payload size is minimal, just the contact ID and a few custom attributes.
Question:
Is there a hidden threshold for Predictive Routing campaign ingestion that differs from the general API documentation? We are hitting 429 errors well below the stated 100 rps limit. Should we be implementing a specific backoff strategy in JMeter for this endpoint, or is there a configuration setting in the campaign itself that limits concurrent import threads? Any insights on how to structure the JMeter thread group to avoid triggering these limits would be appreciated. We need to ensure the system can handle the projected volume without dropping contacts.
If I remember right, the issue stems from ignoring Retry-After headers during burst testing. Implementing exponential backoff in JMeter usually stabilizes the throughput. Check out the BYOC rate limiting guide: https://support.example.com/btoc-429-handling for a sample script.
Yep, this is a known issue that often trips up teams when they jump straight into high-concurrency load tests without accounting for the underlying WFM schedule constraints. The 429 errors you’re seeing aren’t just about API rate limits; they’re frequently a symptom of the system trying to route calls to agents who are technically “available” in the database but actually restricted by their published schedule intervals or shift swap statuses. When JMeter blasts the Predictive Routing API, it bypasses the normal queue logic and hits the raw availability endpoints. If the Get WFM Schedule data action isn’t explicitly filtering for agents with active, non-overlapping shifts in the America/Chicago timezone (or whichever timezone your agents are actually scheduled in), the system throws a conflict. The suggestion above about exponential backoff is solid for the HTTP layer, but you need to ensure your test data reflects realistic WFM constraints. Try adding a precondition in your JMeter thread group that validates the agent’s current shift status via the WFM Schedule API before triggering the predictive call. This prevents the load generator from attempting to route to agents on break, in training, or outside their scheduled hours. Here is a quick snippet to check agent availability before firing the predictive request:
// Check if agent is currently scheduled and available
var scheduleData = getWFMStatus(agentId);
if (scheduleData.status === 'available' && scheduleData.shiftOverlap === false) {
// Proceed with predictive call
firePredictiveCall(agentId);
} else {
// Log and skip to avoid 429s from invalid targets
log.debug("Agent not available, skipping call.");
}
This approach aligns the load test with actual WFM publishing logic, reducing unnecessary API calls and giving you a cleaner view of true campaign capacity. It also helps validate that your schedule publishing workflow isn’t creating hidden bottlenecks during peak intervals.