Quick question about predictive routing failing under load. running a jmeter script with 100 threads hitting POST /api/v2/predictiverouting/campaigns to simulate high volume inbound. getting 429 Too Many Requests after about 50 concurrent requests. the architect flow is simple just a queue node and a wrap up. checked the api rate limits in admin and we are well under the default 100 calls per second limit for our org. using python sdk 1.5.0 to generate the test data. the error response body says “rate limit exceeded” but the analytics dashboard shows only 20 calls per second being processed. wondering if there is a separate throttle for predictive routing endpoints specifically or if the websocket connections are getting dropped before the api call completes. also tried adding a retry policy with exponential backoff in jmeter but it just shifts the errors to later timestamps. environment is us-east-1. any insights on how to tune the load generator or if there is a hidden cap on campaign creation rate?
Ah, yeah, this is a known issue… when moving from Zendesk, you might expect rate limits to behave like simple API throttling, but Genesys Cloud’s predictive routing engine handles load differently. The 429 error here likely isn’t about the general API rate limit you checked, but specifically about the predictive routing campaign update frequency or the queue node’s internal processing buffer. In Zendesk, we just adjusted ticket field updates, but in GC, the predictive routing algorithm recalculates scores in real-time, which can choke if too many concurrent requests hit the campaign endpoint simultaneously. Try reducing the JMeter thread count to 20-30 and adding a 500ms delay between requests to mimic realistic agent login/logout patterns rather than a brute-force burst. Also, verify if your test is hitting the correct endpoint; sometimes load tests accidentally target the campaign configuration API instead of the actual interaction routing path. The Python SDK might be sending headers that trigger stricter validation under load. Check the response headers for Retry-After values, which will tell you exactly how long to wait. If the issue persists, look at the Architect flow’s queue node settings-ensure the “Predictive Routing” toggle is enabled and not conflicting with standard queue rules. During our Zendesk migration, we saw similar spikes when the WFM adherence rules weren’t synced with the routing strategy. Double-check that your test data includes valid user IDs that exist in the system, as invalid IDs can cause the routing engine to throw errors that manifest as 429s. This approach usually stabilizes the load test and gives you a clearer picture of actual routing performance.
Take a look at at the burst limits for predictive routing endpoints. they are significantly lower than standard rest calls, often capping at 10 req/min per tenant. jmeter threads hit this hard. reduce concurrency or add exponential backoff in your script to avoid tripping the circuit breaker.