Context: The handoff from the AI bot to a live agent is taking 12-15 seconds, whereas the SLA is 3 seconds. The flow uses standard predictive routing. This occurs during peak hours in the Europe/Paris timezone. The Performance Dashboard shows the queue is not saturated.
Question: Are there known latency issues with the bot-to-agent handoff logic in Architect? The conversation detail view shows the transition event triggers immediately, but the agent assignment lags.
If I remember correctly…
This latency usually stems from the asynchronous nature of the handoff action when combined with predictive routing’s pre-dial logic. The Architect flow initiates the transfer, but the platform must still resolve the agent selection and check for any active wrap-up timers before connecting. If the agent is in a “busy” state or the skill group has high concurrency, the platform waits for a stable slot.
Check the handoff action configuration in Architect. Ensure you are not using a wait block before the handoff, as this adds dead air. Instead, use the handoff action with immediate mode if available in your version, or verify that the target queue’s max_wait_time is set correctly. Also, review the agent’s wrap_up_timer settings. If agents are wrapping up for 30 seconds, the platform might delay the connection until the next available slot, causing the 12-15 second spike. Consider reducing the wrap-up time or using a dedicated skill for bot handoffs to bypass general queue congestion.
Have you tried isolating the WebSocket connection pool in your load test configuration to see if the latency persists when bot traffic is decoupled from agent signaling? the suggestion above about asynchronous handoff and predictive routing pre-dial logic is spot on, but from a platform API throughput perspective, this delay often masks a hidden bottleneck in the event bus during high concurrency. when you push concurrent sessions above 150 in JMeter, the platform has to serialize the handoff events, and if the WebSocket connections are not properly maintained or are hitting rate limits on the /api/v2/conversations/voice/transfer endpoint, the 12-15 second spike is a classic symptom of the gateway queuing requests. check your JMeter config for the Connection Timeout and Response Timeout settings on the WebSocket sampler. if these are set too low, the client drops the connection before the platform can complete the async handoff, causing a retry loop that looks like latency. also, verify the X-Genesys-Request-Id header is unique for every concurrent request to prevent caching issues at the API gateway level. we saw similar behavior in our stress tests where the bot engine was healthy, but the outbound leg to the agent was throttled by the platform’s internal rate limiter for transfer actions. try adding a small delay between the bot completion and the transfer trigger in the Architect flow, or better yet, use the platform API to poll the conversation state explicitly rather than relying on the default webhook trigger. this gives the system time to resolve the agent selection without blocking the main thread. monitor the Transfer Attempted vs Transfer Completed metrics in real-time to pinpoint exactly where the stall occurs. if it’s consistently at the routing stage, it’s definitely the predictive logic waiting for a stable slot as mentioned, but if it’s at the API level, it’s a throughput issue.
Make sure you validate the webhook payload structure arriving at the ServiceNow Data Action. The suggestion above regarding asynchronous handoff and predictive routing pre-dial logic is valid, but in a production multi-tenant deployment, this delay often masks a hidden bottleneck in the event bus during high concurrency. When pushing concurrent sessions above 150 in JMeter, the platform must serialize the handoff events, which can trigger 429 errors if the SIP INVITE rate limiting is not adjusted. Check if the screen_recording_status field is missing or malformed in the payload, as this often causes the Data Action to hang while retrying. Ensure the Content-Type header is set to application/json and that the IAM policy attached to the S3 bucket explicitly grants s3:PutObject permissions for the Genesys Cloud service principal. If the queue is not saturated, the latency likely stems from the webhook endpoint timing out before the Data Action completes. Try changing the timeout configuration in the Architect flow to allow more time for the external system to respond.