Quick question about the behavior of the Outbound Dialer when operating in ‘Power’ mode within our Architect flow configurations.
We have observed a significant discrepancy between the configured dialing rate and the actual execution metrics recorded in the Performance dashboard. The campaign is set to a dialing rate of 4.5, yet the ‘Active Calls’ metric rarely exceeds 2.0 during peak business hours (09:00 - 11:00 CET). Concurrently, the ‘Abandoned Calls’ percentage remains below 0.5%, suggesting the platform is not dropping calls due to agent unavailability.
The Architect flow is structured to initiate the outbound interaction via the ‘Make Outbound Call’ block, which routes through a dedicated SIP trunk provisioned for high-volume outbound traffic. The trunk registration status is stable, with no 408 or 407 timeouts logged in the recent 24-hour window. We have verified that the ‘Do Not Call’ list filtering is active but contains fewer than 500 entries, which should not impact the dialing algorithm’s throughput.
Reviewing the Outbound Dialer Configuration Guide, the documentation suggests that ‘Power’ mode should maximize agent utilization while adhering to the abandon rate threshold. However, our experience indicates a conservative throttling behavior that does not align with the specified rate.
Is there a hidden dependency on the ‘Agent Availability’ window defined in WFM that overrides the dialer’s internal pacing logic? We have ensured that the ‘Available for Work’ status is correctly propagated from WFM to the dialer via the standard integration. The environment is deployed in the eu-west-1 region, utilizing Genesys Cloud Platform version 2023.11.
We require clarification on whether this throttling is a platform-imposed safeguard or a misconfiguration in the Architect flow’s error handling branch. Any insights into the interaction between the dialer’s pacing algorithm and the WFM availability feed would be appreciated.
It depends, but generally… the discrepancy stems from how the Architect flow handles nested objects versus flat key-value pairs during the initial ingest. When migrating from Zendesk, the strict JSON schema validation often overrides the configured dialing rate if the campaign metadata isn’t properly flattened. This causes the predictive engine to default to a conservative safety mode, capping active calls regardless of the Power setting.
Check your campaign configuration JSON for any nested arrays under the dialing_strategy object. If you see structures like {"rate": {"value": 4.5}}, flatten them to {"rate": 4.5}. The outbound dialer API expects primitive types for rate calculations. This mismatch is a common culprit when migrating legacy configurations or using custom integrations that inject complex objects into the flow context.
After flattening the JSON, restart the campaign to force a re-evaluation of the dialing parameters. You should see the active calls metric climb toward the configured 4.5 within the first 15 minutes. If the issue persists, verify that no schedule adherence rules are inadvertently throttling outbound capacity based on agent availability windows.
The root cause here is the platform’s internal rate-limiting mechanisms kicking in during high-concurrency scenarios, specifically when the WebSocket connections are saturated.
Hi there. The suggestion about JSON flattening is valid for ingestion errors, but it doesn’t explain why active calls cap at 2.0 when the dialing rate is set to 4.5. This behavior is typical of the Genesys Cloud platform enforcing connection limits per region or tenant tier. When running load tests or high-volume campaigns in ap-southeast-1, the system may throttle outbound dialer attempts if the underlying WebSocket pool reaches its maximum capacity.
To diagnose this, check the X-RateLimit-Remaining header in your API responses during the peak window. If it drops to zero, the platform is rejecting new dial attempts to protect stability. You can verify this by monitoring the /api/v2/analytics/outbound/campaigns endpoint in real-time. Look for a spike in attempted versus connected calls. If attempted is high but connected stays low, it’s a throughput bottleneck, not a configuration error.
Here’s a quick JMeter sampler config to isolate the issue:
Run this with a thread count matching your expected concurrent dials. If you see 429 errors, you’re hitting the rate limit. The fix isn’t just adjusting the dialing rate; you need to ensure your WebSocket keep-alive settings are optimized. Consider increasing the keep-alive interval in your Architect flow to reduce connection churn. Also, verify that your tenant’s concurrent call capacity allows for 4.5x the answered rate. If not, the platform will enforce a hard cap regardless of settings.
The official documentation states that Power mode relies heavily on real-time agent availability and answer rates to adjust the predictive algorithm. If the underlying Agent status updates are delayed or if the Architect flow contains heavy logic branches, the system throttles to prevent abandoned calls. The suggestion above about JSON flattening is useful for ingestion errors, but it doesn’t explain the runtime throttling.
From an integration perspective, check if your custom Data Actions are introducing latency. If a Data Action takes longer than 2 seconds to resolve, the dialer may pause to maintain compliance. Verify the max_concurrent_requests setting in your integration’s OAuth configuration. High latency here forces the predictive engine into a conservative state.
Note: Ensure your AppFoundry integration handles OAuth token refreshes gracefully. Token expiration mid-campaign can cause silent failures in the dialing queue, mimicking throttling behavior.