stuck on predictive routing not respecting skill weights when volume spikes. we publish schedules weekly for our chicago team and usually things run smooth but this week the routing engine seems to be ignoring the proficiency levels we set in architect. agents with high skill ratings for technical support are getting general inquiries while low rated agents are stuck handling complex cases. checked the configuration in the routing strategy and everything looks correct. no error codes popping up just bad distribution. is there a delay in the wfm to routing sync that causes this lag? our publish window is friday 02:00 am ct and by monday morning the queue is already messed up. tried clearing cache and republishing but same result. any ideas on how to force a refresh of the agent capabilities in the predictive model without doing a full restart? feeling like we are missing a setting in the skill group configuration or maybe the time zone offset is messing up the availability windows. really need this fixed before next week’s publish.
It depends, but generally… predictive routing prioritizes queue stability over individual agent proficiency when the system detects potential service level breaches. During peak volume, the algorithm shifts to a “first available” logic to prevent abandoned calls, which effectively bypasses the skill proficiency weights you configured. This is a known behavior in high-concurrency scenarios where the queue depth exceeds the agent capacity threshold. The routing engine calculates the probability of a wait exceeding the service level agreement and opts for the fastest possible connection, regardless of the agent’s technical rating.
To mitigate this during load tests or actual spikes, you need to adjust the routing strategy settings to prioritize skill match over wait time. Specifically, check the Queue Settings in the Admin panel and ensure that Skill-based Routing is enabled and set to “Strict” or “Best Match” rather than “Any Available”. Additionally, review the Agent Availability settings to ensure agents are not being marked as available for skills they are not proficient in. If you are running JMeter tests, simulate the actual skill distribution of your agents to see how the predictive algorithm behaves under load. You can also use the Analytics API to pull real-time queue metrics and verify if the abandon rate is triggering the fallback logic. Here is a sample JMeter config snippet to test different routing weights:
<hashTree>
<HTTPSamplerProxy>
<stringProp name="Endpoint">/api/v2/routing/queues/{queueId}</stringProp>
<stringProp name="Method">GET</stringProp>
</HTTPSamplerProxy>
</hashTree>
Adjust the concurrency in your JMeter thread group to match your peak expected volume and monitor the routing decisions in the Agent Desktop logs. This will help identify if the issue is purely algorithmic or if there is a configuration mismatch in the skill assignments.
TL;DR: Zendesk lacks this predictive complexity, so GC’s behavior can be jarring.
You might want to look at the “Longest Available Agent” rule priority. In GC, skill proficiency often yields to availability during spikes. Try lowering the “Max Time in Queue” threshold to force stricter adherence to weights before the system defaults to first-available logic.
How I usually solve this is by adjusting the routing strategy to prioritize skill proficiency over availability, even during peak loads. This requires a deliberate configuration change in the routing rules to prevent the system from defaulting to first-available logic.
The performance dashboard will reflect the shift, allowing you to monitor if the increased wait times are acceptable for maintaining service quality. Balancing these metrics is essential for long-term stability.