What is the standard approach to handle predictive routing capacity limits when simulating high-volume inbound traffic for Genesys Cloud?
I am setting up a load test scenario to validate how the platform handles a sudden spike in inbound calls routed via predictive outbound campaigns. The goal is to push 500 concurrent call attempts through the /api/v2/outbound/calls endpoint using JMeter. The environment is set up in the Asia/Singapore region, and we are using the standard OAuth2 service account for authentication.
The issue arises when the predictive dialer tries to initiate calls faster than the assigned agent capacity can handle. Instead of queuing or dropping calls gracefully, the API starts returning 429 Too Many Requests errors almost immediately after reaching 150 concurrent requests. I have checked the documentation, but it is not clear if there is a specific rate limit per campaign or per organization that applies to predictive routing specifically.
Here is the error response I am seeing:
{
"errors": [
{
"code": "RATE_LIMIT_EXCEEDED",
"message": "Rate limit exceeded for endpoint /api/v2/outbound/calls. Please retry after 5 seconds."
}
]
}
I am using a simple JMeter script with a constant throughput timer set to 100 requests per second. The headers include the necessary Authorization: Bearer <token> and Content-Type: application/json. I have also tried adding a Retry-After delay in the script, but the errors persist.
Is there a specific configuration in the predictive campaign settings that affects API throughput? Or is this a hard limit that cannot be bypassed during load testing? Any insights on how to structure the JMeter script to respect these limits while still testing the routing logic would be appreciated.
It depends, but generally… the issue often stems from treating Genesys Cloud capacity as a static queue like Zendesk ticket groups. In Zendesk, you might just watch the open ticket count, but GC predictive routing relies on dynamic agent availability and campaign-specific limits. When pushing 500 concurrent attempts via JMeter to /api/v2/outbound/calls, you need to ensure your test respects the max_attempts and priority settings defined in the campaign configuration.
A common pitfall during migration is ignoring the capacity field in the campaign settings. If this is set too low, GC will reject calls with 429 Too Many Requests before they even hit the agent pool. Here is a snippet of how the campaign JSON should look to handle high volume:
{
"name": "HighVolTest",
"state": "ACTIVE",
"campaignType": "PREDICTIVE",
"capacity": 500,
"maxAttempts": 1,
"priority": 10,
"contactCenterId": "your-center-id"
}
Also, remember that Zendesk does not have a direct equivalent to predictive outbound dialing rules. You are essentially simulating a dialer now. Ensure your JMeter script includes proper error handling for 429s by implementing an exponential backoff strategy. Without this, the test will flood the API and trigger rate limiting, which skews the results. Since I am coming from a Zendesk background, I initially struggled with this shift from passive ticket intake to active call generation. The key is to treat the API limits as part of the routing logic, not just a network constraint. Check your campaign’s dialerRule settings too; if the answerRatio is too optimistic, the system will throttle calls to protect agent experience. Adjusting these parameters usually resolves the capacity bottleneck during load tests.
{
"method": "POST",
"url": "https://{{tenant}}.mygenesiscallcenter.com/api/v2/outbound/calls",
"headers": {
"Authorization": "Bearer {{access_token}}",
"Content-Type": "application/json"
},
"body": {
"campaign_id": "{{campaign_uuid}}",
"contact_uri": "tel:+1234567890",
"attempt_number": 1,
"priority": 5,
"max_attempts": 1,
"skip_disconnected": true
}
}
Pushing raw POST requests via JMeter bypasses the platform’s internal capacity management engine. The /api/v2/outbound/calls endpoint is designed for manual or scripted ad-hoc dialing, not for simulating the predictive campaign queue. When you force 500 concurrent requests directly to this endpoint, you are effectively creating a thundering herd problem that ignores the max_attempts and priority throttling logic handled by the outbound campaign scheduler.
For accurate load testing, the traffic should be directed through the Campaign API to update the dialer state, or by simulating inbound traffic that triggers the predictive outbound rules. This ensures the capacity_factor and agent_availability metrics are respected. Direct API hammering will likely result in 429 rate limits before the routing engine even evaluates the queue depth, rendering the test data invalid for capacity planning.
Make sure you configure the ApiClient with explicit connection pooling settings before running high-concurrency load tests. The default settings in PureCloudPlatformClientV2 are not optimized for 500 concurrent threads. I followed the documentation here: “The ApiClient uses an internal HTTP client that defaults to a small connection pool size.” This caused immediate ConnectionPoolTimeoutException errors in my Spring Boot integration tests.
Here is how I configured the ApiClient to handle the load:
ApiClient client = new ApiClient();
// Set connection pool size to match JMeter thread count
client.getConnectionPool().setMaxTotal(500);
client.getConnectionPool().setMaxPerRoute(50);
// Increase timeout for slow campaign responses
client.setReadTimeout(30000);
// Initialize the outbound API client
OutboundApi outboundApi = new OutboundApi(client);
I also noticed that the JSON payload in the previous post lacks the contact_uri validation. The API requires a valid E.164 format. If you pass invalid URIs, the platform returns 400 Bad Request instead of processing the capacity logic. Add this validation in your JMeter pre-processor:
// JMeter BeanShell PreProcessor
String uri = vars.get("contact_uri");
if (!uri.matches("^tel:\\+?[0-9]+$")) {
log.warn("Invalid URI: " + uri);
// Fail the sampler or handle gracefully
}
Also, ensure your OAuth token is refreshed automatically. The default token expiry is 30 minutes. In a long-running JMeter test, tokens expire mid-test. Use the AuthorizationCodeGrant flow with a refresh token handler in your Java service to avoid 401 Unauthorized errors. See the fictional support article: GC-DOC-4521: Managing OAuth Tokens in High-Volume Java Clients.