Context:
Our environment operates within the EU-West BYOC region, utilizing Genesys Cloud as the primary contact center platform. We have recently deployed a set of custom Data Actions via the Genesys Cloud Integrations framework to synchronize agent status changes with an external workforce management system. The integration utilizes the standard REST-based Data Action connector, configured with a 30-second timeout threshold as per enterprise security policies. The Architect flow logic includes a specific block for “Execute Data Action” which triggers upon state transitions.
The issue manifests when the external system experiences latency spikes exceeding the configured timeout. In such scenarios, the Architect flow does not proceed to the subsequent “Success” path, nor does it reliably trigger the configured “Error” path. Instead, the interaction appears to hang or drop silently in the queue metrics, resulting in a discrepancy between the actual agent state and the reported state in the Performance Dashboard. This creates a significant gap in our real-time occupancy calculations, which rely on accurate state synchronization. The logs in the Integration monitoring view indicate a 504 Gateway Timeout response from the target endpoint, yet the flow execution status remains ambiguous in the conversation detail view.
Question:
Is it possible to configure a specific retry mechanism or a fallback path within the Architect flow that explicitly handles Data Action timeouts without requiring a full flow restart? We require a deterministic behavior where a timeout triggers a defined error handling routine, allowing the flow to log the failure and proceed with a degraded state assumption. Currently, the lack of explicit error propagation forces manual reconciliation of agent statuses. Additionally, could you clarify if the 30-second timeout is enforced at the Genesys Cloud edge level or at the application layer, and whether there are any known limitations regarding timeout handling for BYOC instances in the EU-West region? Understanding the precise failure mode is critical for adjusting our dashboard metrics to account for these integration delays.