Looking for advice on handling rate limiting when load testing the Conversational AI bot endpoints via the platform API. We are simulating a high-concurrency scenario using JMeter to validate bot response times under stress. The test plan initializes 100 concurrent WebSocket connections to simulate inbound voice calls routing to the default bot flow. Each thread executes a POST to /api/v2/analytics/conversations/details/query to fetch real-time engagement metrics while the bot processes intents.
The issue manifests immediately after the 45-second mark. The JMeter dashboard shows a sharp spike in 429 Too Many Requests errors specifically from the analytics query endpoint, not the WebSocket stream itself. The WebSocket connections remain stable, but the polling loop for conversation details gets throttled aggressively. The error response body includes a retry-after header, but the value is inconsistent, ranging from 1 to 5 seconds. This inconsistency makes it difficult to configure a reliable retry logic in the JMeter JSR223 PostProcessor.
“Clients should implement exponential backoff strategies when encountering 429 status codes. The retry-after header indicates the minimum wait time in seconds before the next request is permitted.”
We are using the standard Genesys Cloud OAuth2 client credentials flow for authentication. The token is refreshed every 3600 seconds, so token expiration is not the cause. The environment is a production tenant in the us-east-1 region. We have checked the API rate limit dashboard in the admin console, and the bot analytics queries are hitting the per-minute limit for that specific scope.
Is there a recommended pattern for decoupling the analytics polling from the real-time WebSocket stream in a load test? Should we be batching the analytics requests instead of polling per conversation ID? We are currently polling every 2 seconds per active call. Any insights on adjusting the JMeter thread group configuration or using a different API endpoint for batch analytics retrieval would be helpful. We want to ensure the bot’s intent recognition latency is measured accurately without the noise of rate-limit retries skewing the results.