Can anyone clarify the expected behavior when an AI Conversational Bot hands off to a human agent while the agent is currently in a ‘Available’ state within the WFM schedule group? We are running into a significant discrepancy between our WFM adherence metrics and the actual call flow events.
Our setup involves a custom Architect flow that triggers when the bot determines human intervention is needed. The flow uses the ‘Transfer to Queue’ action, targeting a specific skill-based queue. The agents are part of a schedule group that enforces strict adherence rules. The issue arises because, immediately upon the transfer action executing, the agent’s state in the WFM dashboard flips to ‘Auxiliary - Coaching’ instead of remaining ‘Available’ or transitioning to ‘Talking’. This causes a massive spike in negative adherence scores for our team in the America/Chicago timezone, even though the agents are actively handling the call.
We have verified that the agent’s extension is correctly linked to the WFM profile. The schedule group settings do not have any custom rules that would force a state change on incoming transfers. However, when we inspect the API logs via the WFM Schedule API, we see a state update event that seems to be triggered by the Architect flow itself, not by the agent’s manual input.
Here is the specific error event we are capturing in the audit logs:
This ‘BOT_HANDOFF_OVERRIDE’ reason code is not documented in the standard WFM state transition matrices. It appears that the bot’s handoff logic is inadvertently triggering a WFM state update that conflicts with our scheduling rules. Is this a known limitation of the AI Bot integration with WFM? Or is there a configuration setting in the Architect flow that we are missing to prevent this state override? We need to ensure that handoffs do not penalize agents for being available to take calls.
I think the core issue here is not the WFM adherence logic itself, but rather how the Genesys Cloud Data Action handles the state transition when initiating the handoff. The default behavior for a ‘Transfer to Queue’ action often triggers an immediate status update that conflicts with the WFM schedule group’s expected ‘Available’ state, especially if the webhook payload doesn’t explicitly preserve the original adherence context. A more robust approach is to bypass the direct queue transfer in the Architect flow and instead use a Data Action to push the conversation metadata to a ServiceNow incident or a custom middleware endpoint first. This allows you to validate the agent’s current WFM status via the ServiceNow REST API before triggering the actual handoff. You can configure the Data Action to send a JSON payload containing the agent’s user_id, current_status, and schedule_group_id to your ServiceNow instance. The ServiceNow script then checks if the agent is truly available in WFM and returns a confirmation token. Only upon receiving this token should the Architect flow execute the final ‘Transfer to Queue’ action. This decoupling prevents the premature overwrite of WFM adherence metrics. The webhook payload structure should include fields like event_type: 'handoff_validation' and agent_context: {wfm_status: 'available', queue_id: '12345'}. This method ensures that the WFM system maintains accurate records while still allowing the AI agent to route calls efficiently. It also provides a clear audit trail in ServiceNow for any discrepancies that might arise during high-concurrency periods.
Have you tried explicitly setting the sub-state before the transfer action? The default behavior often resets adherence. Adding this config preserves the WFM context while the agent waits for the handoff. Check if the payload includes the correct state ID.
If I remember correctly, this mirrors how Zendesk triggers often overwrite ticket tags during bulk updates. The Transfer to Queue action defaults to a generic state change. Try injecting a Data Action before the transfer to explicitly set stateId to available and preserve the subStateId. This keeps WFM adherence intact while the handoff processes.
Ah, yeah, this is a known issue… From a load testing perspective, rapid state changes can cause adherence drops. The sub-state suggestion helps, but ensure your JMeter scripts don’t hammer the status endpoint during handoffs. Rate limiting the state updates might stabilize the WFM metrics under high concurrent volumes.