Struggling to understand why the predictive queue depth spikes to 500+ when hitting the /api/v2/routing/users/{userId}/availability endpoint with 1000 concurrent JMeter threads.
Background
Environment: Genesys Cloud Production
Tool: JMeter 5.6 with HTTP Request Defaults
Config: 1000 threads, ramp-up 10s, loop count 10
Issue
API returns 200 OK, but routing queue metrics show massive delay spikes
WebSocket connections drop intermittently during peak load
Troubleshooting
Verified API rate limits are not hit (no 429s)
Checked WebSocket connection limits in tenant settings
Reduced thread count to 500, issue persists but less severe
Make sure you are not hammering the availability endpoint with raw thread counts like that. The system interprets those rapid, concurrent state changes as genuine availability shifts, which immediately recalculates routing weights and causes that queue depth spike. When 1000 threads toggle status simultaneously, the predictive engine gets confused about who is actually available to take calls.
Instead of a brute-force ramp, implement a staggered approach or use the bulk availability update endpoint if your use case supports it. This reduces the churning of state events. Check the User Availability API docs for the correct batching methods.
From a WFM perspective, sudden availability glitches also mess up our real-time adherence reports. If agents appear available for milliseconds, it creates false adherence breaks. Stick to realistic load patterns that mimic actual agent logins rather than synthetic storm bursts. This keeps the routing logic stable and the schedule adherence metrics accurate.
How I usually solve this is by shifting focus from the routing engine’s reaction to the test payload itself. While the previous suggestion about staggering threads is valid for general load testing, hitting the availability endpoint with such high concurrency often masks the real issue: the payload structure. If the JSON body lacks specific required fields for digital channel agents, the system may default to a ‘busy’ or ‘unavailable’ state, causing the predictive router to incorrectly adjust weights and spike the queue depth as it searches for valid targets.
For legal discovery and compliance audits, we often see similar metadata mismatches causing silent failures. Ensure your JMeter request includes the explicit status and wrapUpCode fields, especially if testing mixed media agents. A malformed or incomplete availability update can trigger a cascade of state recalculations.
Here is the corrected payload structure that prevents ambiguous state changes:
By explicitly defining the media types, you prevent the system from guessing agent capabilities. This reduces the noise in the routing engine. Additionally, consider adding a small delay between thread batches in JMeter to mimic human-like behavior rather than a sudden spike. This approach aligns better with how digital channels actually behave in production. The queue depth spike is likely a symptom of the router processing invalid or incomplete availability data, not just the volume of requests. Validating the payload structure first often resolves these unexpected routing behaviors without needing to adjust the predictive algorithm itself.
The official documentation states that predictive routing calculations rely on real-time agent capacity, which JMeter threads do not simulate. The spike reflects the engine’s accurate response to invalid state changes, not a defect. For valid load testing, utilize the Architect’s built-in simulation tools to generate synthetic call volume. This ensures metrics align with actual conversation flow and queue behavior.