Stuck on a recurring timeout issue when invoking a custom Data Action designed to sync trunk metadata from our internal CMDB to Genesys Cloud. We are using the Genesys Cloud Platform SDK v3.5.2 within an Architect flow triggered by a scheduled task. The flow executes successfully for approximately 60% of our 15 BYOC trunks, but consistently fails with a 504 Gateway Timeout for the trunks hosted in the Singapore and Tokyo regions.
The Data Action itself is a simple HTTP POST to an internal endpoint, which returns a 200 OK within 200ms according to our internal logs. However, Genesys Cloud reports the failure. I have verified that the SIP credentials and outbound routing rules are intact and that the trunks are registered properly. The issue appears isolated to the Data Action execution context rather than the trunk health itself.
Increasing the timeout limit in the Architect step did not resolve the issue. The error payload indicates a network layer drop rather than an application-level rejection. Given our verbose logging setup, we can see the request leaving the Genesys environment but never receiving the response object back in the flow context.
Is there a known limitation or regional routing issue with Data Actions calling external endpoints from APAC regions, or should we be looking at a specific firewall configuration on our side?
You should probably look at at the concurrent execution limits on the architect flow level. when you trigger a scheduled task for 15 trunks, it’s not just one api call. it’s likely spawning multiple parallel threads to handle the metadata sync. in my jmeter load tests, i’ve seen the platform api hit rate limits when concurrent websocket connections spike above 100 in the ap-southeast-1 region.
the 504 timeout isn’t necessarily a network issue between your cmdb and genesys cloud. it’s often the internal platform queue backing up. try reducing the concurrency in your scheduled task. instead of firing all 15 trunks at once, batch them. process 3 or 4 at a time.
also, check your data action payload size. if the metadata is large, the http post might be timing out before the genesys cloud api can process it. here is a simple config tweak for your jmeter script to simulate this throttling:
add a constant timer of 2000ms between requests. this helps spread out the api throughput. i noticed in my recent tests that pushing concurrent sessions beyond 50 on the /v2/conversations/messaging endpoint caused similar drops. the websocket handshake completes, but the initial payload fails if the server is saturated.
if you are using the platform sdk v3.5.2, ensure you are handling the retry logic correctly. the sdk might not be retrying the 504 automatically. you might need to implement a custom retry mechanism in your architect flow or the data action script. check the api rate limits documentation for your specific region. singapore and tokyo often have different capacity planning thresholds. try lowering the concurrent call volumes in your test environment to see if the timeout persists.
Have you tried wrapping the Data Action in a retry loop with exponential backoff? The 504s on APAC trunks often stem from transient regional latency spikes rather than hard failures.
Since I manage weekly schedule publishing, I see similar timeouts when agent preference updates hit the API during peak hours. The key is not to treat a 504 as a fatal error immediately. Configure the Data Action to catch these specific status codes and retry before failing the flow. Also, check if your CMDB allows increasing the HTTP timeout threshold for outbound requests to Genesys endpoints. Sometimes the platform is just processing the metadata sync in batches, and a simple wait solves the race condition.
Beware that bulk metadata syncs can trigger legal hold conflicts if the trunk metadata is part of an active discovery request. The recording export API locks these records, causing the 504 timeout when the Data Action attempts to overwrite fields. Verify the audit trail for any pending export jobs on those Singapore and Tokyo trunks before increasing retry limits. This prevents data corruption during chain of custody preservation.