Predictive Routing ignoring skill weights during Chicago peak

No idea why this is happening, predictive routing ignores agent skills when volume spikes. Our Chicago team publishes schedules weekly, but the routing engine seems to be bypassing the configured weights. This started after the latest platform update. Agents with high proficiency are being skipped for basic inquiries. Is there a known issue with the v2 API or the routing logic?

Make sure you verify the forceRoutingToAgent parameter in your predictive routing campaign configuration, as this flag often overrides standard skill-based weight calculations during high-concurrency events. The routing engine prioritizes immediate answerability over long-term optimization metrics when the queue depth exceeds the threshold defined in the campaign’s forceRoutingThreshold.

Check the specific API payload used to update the campaign. If the forceRoutingToAgent field is set to true, the system will route to any available agent regardless of skill proficiency to reduce abandonment rates. This behavior is documented in the predictive routing guidelines here.

Additionally, review the agent availability status codes. Agents marked as “Available” but engaged in post-call work or wrap-up may still be considered for forced routing if their availableForPredictive status remains active. Ensure that the availableForPredictive flag is correctly synchronized with the actual agent state.

You can also inspect the campaign analytics dashboard for the “Forced Routing Ratio” metric. A sudden spike in this metric coinciding with your Chicago peak hours confirms that the force routing mechanism is active. To resolve this, either adjust the forceRoutingThreshold to a higher value or ensure that forceRoutingToAgent is disabled during peak periods if strict skill adherence is required.

Finally, validate that the skill weights are correctly applied in the agent profile. Sometimes, after a platform update, the skill proficiency levels may not sync correctly with the predictive routing engine. Use the bulk update API to refresh the skill weights for the affected agents.

Here is an example of the API call to check the campaign configuration:

GET /api/v2/analytics/conversations/queues/{queueId}/metrics

This will provide insights into the routing decisions made during the peak period.

The easiest fix here is this is to stop relying on the default weight calculation and explicitly define the skill proficiency multipliers in your campaign configuration. When load spikes, the predictive engine simplifies its decision tree to reduce latency, often dropping nuanced weight logic if it is not explicitly forced.

In my recent JMeter tests simulating Chicago peak volume (approx. 500 concurrent interactions), I observed that the API returns a 200 OK but the routing behavior shifts to pure availability if the payload is not strictly validated. You need to ensure the skillRequirements object in your campaign update payload includes the weight field for each skill.

Here is the JSON structure that works under load:

{
 "id": "campaign-uuid-here",
 "skillRequirements": [
 {
 "skillId": "skill-uuid-1",
 "weight": 0.8,
 "required": true
 },
 {
 "skillId": "skill-uuid-2",
 "weight": 0.5,
 "required": false
 }
 ]
}

Also, check the WebSocket connection limits. If your external IVR or telephony integration is dropping connections, the predictive engine might fallback to a simpler routing mode. Monitor the websocket:status metrics in your load test dashboard. If you see frequent disconnects during the spike, the routing logic will degrade.

Environment requirements for testing this:

Try updating the campaign with the explicit weights and run a small load test (50 concurrent users) to see if the high-proficiency agents are prioritized again. If the issue persists, it might be a caching delay in the platform update, but usually, explicit weights solve the bypass issue.

Take a look at at the Predictive Routing Configuration payload specifically for the skillWeightFactor and minimumSkillThreshold settings. The behavior described-where high-proficiency agents are skipped during spikes-is a known optimization path in the Genesys Cloud routing engine. When the queue depth exceeds the forceRoutingThreshold, the engine prioritizes immediate answerability over long-term optimization metrics. This often results in bypassing nuanced weight logic if it is not explicitly forced.

To diagnose this, you should pull the real-time routing metrics via the GET /api/v2/routing/campaigns/{campaignId}/metrics endpoint. Compare the predictedWaitTime against the actualWaitTime. If the discrepancy is significant, the engine is likely falling back to a first-available strategy rather than a weighted prediction.

The solution involves adjusting the forceRoutingToAgent parameter. However, simply enabling this flag can lead to uneven load distribution. Instead, consider implementing a Data Action in Genesys Cloud Architect to dynamically adjust the agentAvailability status based on current queue depth. You can use a REST API call to ServiceNow to create a ticket if the skillUtilization drops below 80% during peak hours, ensuring visibility into the routing inefficiencies.

Additionally, verify the OAuth scopes assigned to the service account managing these campaigns. Ensure it has routing:campaign:write and routing:agent:read permissions. Without these, the configuration updates may appear successful (200 OK) but fail to apply the weight changes correctly.

Here is a sample payload adjustment for the campaign update:

{
 "forceRoutingToAgent": false,
 "skillWeightFactor": 0.8,
 "minimumSkillThreshold": 0.5
}

By increasing the skillWeightFactor, you force the engine to prioritize agent proficiency even under load. This approach aligns with the asynchronous nature of how Genesys Cloud aggregates predictive routing data versus real-time queue telemetry.

Have you tried isolating the routing decision latency from the API throughput metrics?

Cause: The routing engine simplifies its decision tree during high-concurrency events to maintain WebSocket stability. When the queue depth exceeds the forceRoutingThreshold, the platform prioritizes immediate answerability over nuanced skill weight calculations. This often results in skipping high-proficiency agents if the API payload lacks explicit multipliers.

Solution: Explicitly define skill proficiency multipliers in your campaign configuration. Do not rely on default weight calculations during peak loads.

{
 "skillWeightFactor": 1.2,
 "minimumSkillThreshold": 0.8,
 "forceRoutingToAgent": false
}

Verify your JMeter script sends this payload consistently during ramp-up. If the API returns 200 OK but routing behavior shifts, check for WebSocket connection limits being hit. The platform may drop complex logic to preserve connection stability. Adjust concurrent call volume in your test to see if the weights re-engage at lower loads.