Dealing with a very strange bug here with our custom Data Action script designed to validate SIP registration states across our 15 BYOC trunks in the ap-southeast-1 region. The script executes a parallel HTTP POST to the v2/trunking/locations endpoint to fetch current trunk status, followed by a series of curl commands against our carrier’s provisioning API to verify credential validity. While the Genesys Cloud API calls complete within 200ms, the subsequent carrier API calls consistently trigger a 408 Request Timeout from the Genesys platform after exactly 15 seconds, despite the carrier responding in under 500ms. The error payload indicates timeout_exceeded at the edge gateway level. We have verified that the outbound IP ranges for ap-southeast-1 are whitelisted on the carrier side, and direct testing from a bastion host in the same region shows no latency. The issue appears isolated to Data Action execution contexts, suggesting potential TCP connection pooling limits or strict egress firewall rules applied to the scripting runtime environment. We are using the latest Architect flow version (v4.2.1) and have increased the timeout threshold in the Data Action configuration to 30 seconds, but the 408 persists. The logs show the request drops immediately after the initial SYN-ACK handshake with the carrier endpoint. This is disrupting our automated failover logic, as we rely on this script to flag trunks with stale credentials before routing critical outbound campaigns. We need to understand if there is a hidden concurrency limit for external HTTP calls initiated from Data Actions in this specific region, or if the egress traffic is being inspected and dropped due to payload size or header anomalies. The SIP credentials are rotated daily, and the script handles the JSON parsing correctly in local tests. Any insights into the network path or runtime constraints for ap-southeast-1 Data Actions would be appreciated. Is there a known issue with long-lived TCP connections in this environment?
The main issue here is likely external latency triggering the Data Action timeout. Genesys defaults to 5000ms. Increase timeout_ms in genesyscloud_flow_action_data_action. Do not exceed 10000ms or Architect will kill the flow. Consider moving carrier checks to an async webhook instead.
Cause: The 5000ms limit As noted above is the hard stop for the Data Action execution context, but that’s not the only trap. In ap-southeast-1, the latency to external carrier APIs can easily spike during peak hours. If your curl commands are synchronous inside the Data Action script, you’re blocking the Genesys worker thread. The real issue is likely that the HTTP client in your script isn’t configured with a connect timeout separate from the read timeout. If the carrier API hangs on handshake, Genesys waits for the full 5s before killing it, which looks like a 408 to the flow.
Solution: You need to enforce strict timeouts on the outgoing HTTP requests within the script itself, not just rely on the Architect action limit. Here’s a snippet for a Python-based Data Action using requests that prevents the hang:
import requests
def check_trunk_status(carrier_url, creds):
# Critical: Set connect_timeout low to fail fast
# Set read_timeout to allow for processing but not hang
timeout = (3, 7)
try:
response = requests.post(
carrier_url,
json=creds,
timeout=timeout,
verify=True # Ensure SSL is verified to avoid handshake hangs
)
return response.json()
except requests.exceptions.Timeout:
return {"error": "Carrier timeout", "status": 504}
except requests.exceptions.ConnectionError:
return {"error": "Connection refused", "status": 502}
Also, double-check your verify=True flag. If the carrier uses an internal CA not in the Genesys trust store, the SSL handshake will fail silently or hang depending on the client library. It’s safer to catch these exceptions and return a structured error object rather than letting the script crash. This way, the Data Action completes successfully (with an error payload) instead of timing out. You can then handle the error in the flow logic. Don’t forget to log the exception details to Cloud Debugger for post-mortem analysis.
the timeout bump helped, but it didn’t fix the root cause. we were still hitting 408s when the carrier API stalled on the credential check.
the issue wasn’t just the total execution time. it was the synchronous nature of the fetch calls inside the data action script. if one trunk check hangs, the whole thread blocks until the 5000ms hard limit hits.
we switched to a promise-based parallel execution with individual timeouts. this way, a single bad trunk doesn’t kill the whole health check batch. here’s the pattern that finally stabilized it.
async function checkTrunks(trunkIds) {
const results = await Promise.allSettled(
trunkIds.map(async (id) => {
// 1. Check Genesys status
const genResponse = await platformClient.trunkingApi.getTrunkingLocation(id);
// 2. Check carrier with strict 1.5s timeout
const controller = new AbortController();
const timeoutId = setTimeout(() => controller.abort(), 1500);
try {
const carrierRes = await fetch(`https://carrier.api/v1/status/${id}`, {
signal: controller.signal
});
clearTimeout(timeoutId);
return { trunk: id, status: carrierRes.status === 200 ? 'healthy' : 'degraded' };
} catch (e) {
return { trunk: id, status: 'timeout', error: e.name };
}
})
);
return results.map(r => r.value);
}
using Promise.allSettled means we get results for all 15 trunks even if half of them time out. the individual 1500ms abort signal prevents any single request from eating up the global 5000ms budget.
you also need to make sure your genesyscloud_flow_action_data_action resource in terraform has timeout_ms set to at least 8000 to give the aggregation step some breathing room. don’t set it to 10000. architect gets cranky.
this approach keeps the data action responsive. if you’re still seeing 408s, check your carrier’s rate limits. they might be throttling the parallel requests.
That parallel promise approach is exactly how you handle it. You need to cap each individual request so one bad trunk doesn’t hang the whole batch. Here is the pattern we use in our SDK wrappers. It keeps the total execution well under the 5s limit.
const results = await Promise.allSettled(trunks.map(trunk => fetchWithTimeout(trunk.url, 1000)));