Does anyone understand why the Genesys Cloud Architect bot API returns HTTP 503 Service Unavailable errors when concurrent message throughput exceeds 20 requests per second? Running a JMeter load test from Asia/Singapore to simulate peak chat volume for a retail customer service queue. The test plan uses a thread group with 50 users ramping up over 30 seconds, holding for 60 seconds, then ramping down. Each thread sends a POST request to /api/v2/communications/conversations with a valid OAuth2 token obtained via /oauth/token. The payload includes a simple text message and the bot’s application ID. Under low load (1-5 rps), the bot responds instantly with a 200 OK and the expected JSON response from the bot flow. However, as the load increases to around 15-20 rps, the error rate spikes to nearly 100% with 503 errors. The JMeter logs show no timeouts on the client side, just immediate 503 responses from the Genesys Cloud servers. The bot flow in Architect is a simple decision tree with no external API calls or complex integrations, just pure logic and static text responses. The environment is the Genesys Cloud US1 region. I’ve checked the API rate limits documentation and don’t see a specific limit for bot conversation endpoints that would trigger 503s at this volume, only 429s for general API abuse. The bot’s application ID is correct and the bot is published. The issue persists across multiple test runs. I suspect there might be a hidden concurrency limit for bot interactions or a bottleneck in the Architect runtime engine. Is there a way to increase the bot’s capacity or adjust the load test parameters to avoid hitting this limit? The current setup prevents us from validating the bot’s performance under realistic peak conditions. Any insights into the backend handling of bot requests or known limitations would be appreciated.
Check your rate limiting configuration first. This is a common hurdle when moving from Zendesk’s simpler API model to Genesys Cloud.
Genesys enforces strict per-tenant limits. The 503 error usually means you hit the ceiling for that specific endpoint.
Adjust the JMeter ramp-up time. Slow down the thread group to stay within safe bounds during migration testing.