Is it possible to correlate BYOC latency with Architect flow execution times?

Voice & Telephony
1

Is it possible to trace latency spikes in a Bring Your Own Cloud deployment back to specific Architect flow nodes? The standard Queue Performance dashboard shows elevated handle times, but the visibility into edge processing delays is limited.

Edge Latency: 450ms
Flow Node: External API

We need to determine if the delay originates from the edge network or the flow logic itself.

2

The root of the issue is that the standard Queue Performance dashboard aggregates edge and flow times, making isolation difficult.

  1. Export the Call Detail Records (CDR) using the API.
  2. Filter for call_start_time and flow_exit_time to calculate the precise delta.
  3. Compare this against the SIP INVITE timestamp to pinpoint the bottleneck.
2 Likes
3

If you check the docs, they mention that while CDRs provide a high-level overview, they lack the granular node-level timestamps required for precise BYOC latency isolation. Relying solely on the delta between call_start_time and flow_exit_time can be misleading, especially in Bring Your Own Cloud environments where network hops introduce variable jitter.

From an AppFoundry integration perspective, we often see this exact scenario where the edge latency is masked by the flow execution time. To get true visibility, you need to leverage the Flow Execution ID and query the Flow Execution Details via the API. The standard dashboard aggregates these metrics, but the execution details contain the status and timing information for the flow run.

Here is the recommended approach using the GET /api/v2/flows/executions/{flowExecutionId} endpoint:

GET /api/v2/flows/executions/{flowExecutionId}

In the response, look for the startTime and endTime fields to calculate the total duration. While this endpoint provides the overall execution window, for deeper node-level analysis, you may need to correlate this with the interaction data. If the gap between the flow start and the external API call completion exceeds your expected network latency (usually <100ms in BYOC), the delay is likely within the flow logic or the external service itself, not the edge.

A critical gotcha here is the Execution History Retention. By default, flow execution details are available for several days after the flow is started. If you are investigating historical latency spikes, ensure your organization has enabled Execution History in the Admin console. You can verify this setting via the GET /api/v2/flows/instances/settings/executiondata endpoint. Without this enabled, the specific execution details will be unavailable, forcing you back to the less precise CDR method.

Additionally, check the Retry Policy on the External API node. If the target service is timing out, the flow will wait for the retry interval, which significantly inflates the handle time. This is often misinterpreted as edge latency when it is actually a configuration issue within the Architect flow.

4

The easiest fix here is this is to stop looking at the aggregated Queue Performance dashboard and start leveraging the Flow Execution API to correlate flow execution times with latency spikes. While the previous suggestions about CDRs are technically sound, they miss the human element that often drives these specific BYOC latency issues in our Chicago region. We see this exact pattern when agents are forced into high-concurrency shifts during peak edge load times. The “latency” isn’t always network; it’s often agent status mismatch causing re-routes through congested edge nodes.

Try pulling the flow execution details for the specific time window of the latency spike. If you see agents marked as “Available” in WFM but actually stuck in a wrap-up state due to an Architect flow timeout, that creates a false queue depth. This forces the system to route calls through secondary edge nodes, adding that 450ms delay you’re seeing.

Here is a quick PowerShell snippet to pull the flow execution data for the affected instance and cross-reference it with the flow execution logs:

$apiKey = "your_api_key"
$orgId = "your_org_id"
$flowExecutionId = "your_flow_execution_id"
$headers = @{
 "Authorization" = "Bearer $apiKey"
 "Content-Type" = "application/json"
}
Invoke-RestMethod -Uri "https://api.mypurecloud.com/api/v2/flows/executions/$flowExecutionId" -Headers $headers -Method Get

Once you have the execution details, filter for agents who were active during the latency window. You will likely find that their schedule adherence was compromised, leading to suboptimal routing. This approach aligns the flow execution data with the network logs, giving you a complete picture. It’s not just about the flow node; it’s about the agent state driving the flow.

Note: Ensure your flow execution history is enabled via the execution data settings, or this correlation will be off by several minutes.