Encountering immediate 429 Too Many Requests errors when hitting the /api/v2/bots endpoint during a concurrency spike. The goal is to validate the capacity of the AI Bot service under load, specifically when triggering multiple simultaneous conversations through a custom Architect flow.
The test setup uses JMeter 5.6 with a thread group configured for 100 concurrent users, ramping up over 10 seconds. Each thread sends a POST request to initiate a chat session via the bot’s webhook URL. The requests fail consistently after the 50th concurrent connection.
HTTP/1.1 429 Too Many Requests
Retry-After: 1
Content-Type: application/json
{
"errors": [
{
"code": "429",
"message": "Rate limit exceeded. Please retry after the specified time."
}
]
}
The standard API rate limit documentation suggests a higher threshold for this endpoint. Is there a specific limit for bot conversation initiations that differs from general API calls? Also, does the Architect flow execution add overhead that triggers these limits faster than expected? Looking for advice on adjusting the JMeter pacing or checking if there is a hidden quota for bot interactions in the Genesys Cloud environment.
Check the rate limiting headers in the response, specifically X-RateLimit-Remaining and X-RateLimit-Reset, to understand the exact ceiling for your tenant’s tier. The /api/v2/bots endpoint often has stricter concurrency limits than standard voice channels, which causes immediate 429 errors when JMeter threads ramp too quickly.
{
"headers": {
"X-RateLimit-Remaining": 0,
"X-RateLimit-Reset": 1715623400,
"Retry-After": 60
}
}
Adjust the JMeter test plan to include a Constant Throughput Timer or a custom JSR223 PostProcessor that reads the Retry-After header and sleeps the thread accordingly. For legal discovery or bulk export scenarios, we usually implement exponential backoff, but for bot initialization, a simple delay between requests is often sufficient to stay under the threshold.
Also, ensure the Architect flow is not creating redundant bot instances for each concurrent user. If the flow logic triggers a new bot session for every message instead of reusing an existing context, you will hit the API limit much faster. Review the flow’s Bot element configuration to confirm it is using the correct Bot Version and Language settings without unnecessary re-initialization.
The documentation suggests that high-concurrency tests should mimic real-world user behavior, which includes natural pauses between actions. A ramp-up of 100 users in 10 seconds is aggressive for API endpoints. Try reducing the concurrent threads to 20 and increasing the loop count to achieve the same total request volume over a longer period. This approach provides more accurate capacity data without triggering rate limits.
Monitor the X-RateLimit-Reset timestamp to align your test cycles with the rate limit window. This method helps in identifying the true capacity of the AI Bot service under sustained load rather than just peak burst capacity.
Have you tried implementing exponential backoff in your JMeter script to respect the rate limit headers? See KB-9921 for the exact retry logic configuration.
The problem is likely that the burst capacity of the POST /api/v2/bots endpoint. While exponential backoff helps, the initial ramp-up is too aggressive. Throttle the JMeter thread group to match the X-RateLimit-Limit header value. Consider staggering the start times or using a constant throughput timer to prevent immediate saturation during the load test.