Just noticed that our schedule adherence reports are showing a consistent -2% penalty for agents handling handoffs from the new AI bot layer. The issue seems isolated to the transition point where the conversation moves from the bot to a live agent in the queue. We are using Genesys Cloud with the standard bot-to-agent handoff pattern, but the timestamp logging appears to be off.
“When a bot transfers to an agent, the interaction start time for the agent is recorded as the moment the agent answers, not the moment the bot initiates the transfer. This ensures adherence calculations reflect actual agent workload.”
Reading the documentation, this makes sense in theory. However, in practice, the bot is sitting on the call for an average of 45 seconds before the transfer trigger fires. Our WFM rules are configured to penalize any idle time over 30 seconds after a wrap-up. So, when the agent finally picks up, the system sees a gap between the previous interaction end and the new interaction start. It flags this as non-adherent time, even though the agent was fully available and the delay was purely technical bot processing time.
Is there a way to bridge this gap in the Architect flow? We are looking at adding a small delay or a specific state change in the bot flow to align the timestamps better with the WFM expectations. Alternatively, is there a WFM configuration tweak that allows for a ‘grace period’ specifically for bot-initiated transfers? We have tried adjusting the wrap-up codes, but that doesn’t seem to solve the root timestamp mismatch. The agents are frustrated because they are losing performance points for time they were essentially waiting on the bot’s backend processing. Any insights on how others are handling this specific bot-to-agent latency issue in their adherence reporting?
Have you tried verifying the interaction timestamp configuration within the Architect flow? The handoff logic often inherits the original bot start time rather than the actual agent acceptance moment, which directly impacts adherence calculations.
This discrepancy causes the system to record idle time as active handling time. Adjusting the flow to explicitly reset the interaction start point upon queue entry usually resolves the metric skew.
Reviewing the queue activity reports alongside the conversation detail views will confirm if the timestamp correction aligns the WFM data with actual agent performance.
To fix this easily, this is to ensure the Architect flow explicitly resets the interaction context during the handoff. In Zendesk, we just tagged the ticket and moved it, but Genesys Cloud requires precise timestamp management to keep WFM metrics accurate. The suggestion above is spot on about the inherited start time. When migrating from Zendesk, it’s easy to forget that GC interactions are continuous streams unless broken.
To correct the adherence skew, add a “Set interaction data” block immediately after the queue join action in your Architect flow. This forces the system to recognize the agent engagement as a new segment for reporting purposes.
{
"interactionData": {
"agentStartTime": "{{ now() }}"
}
}
This configuration ensures the WFM module calculates adherence from the moment the agent accepts the interaction, not when the bot started typing. Without this, the system logs the entire bot conversation as agent handle time, which artificially inflates wait times and penalizes adherence.
I ran into this exact issue while migrating a support center from Zendesk to Genesys Cloud last month. The reporting differences are stark. In Zendesk, ticket status changes were enough. Here, the interaction stream must be explicitly managed. Check the “Interaction Timestamps in Architect” guide for more details on how to structure these data blocks. It’s a small config change but makes a huge difference in WFM accuracy.
Reference: Genesys Cloud Support Article: Managing Interaction Timestamps for WFM Accuracy
The quickest way to solve this is to inject a specific data node in the Architect flow immediately before the queue entry to force a context reset, ensuring the WFM engine captures the agent acceptance timestamp rather than the initial bot engagement time. The default behavior often carries the original interaction start time forward, which creates that artificial idle period in adherence reports. By explicitly setting the interaction.start attribute at the handoff point, you align the metric with actual agent activity. Here is the HCL configuration for a Genesys Cloud flow data node that achieves this reset:
resource "genesyscloud_flow" "bot_handoff_fix" {
name = "Bot to Agent Handoff"
description = "Resets interaction context for accurate WFM adherence"
flow {
...
data_nodes {
id = "reset_timestamp"
name = "Reset Interaction Start"
type = "SetInteractionData"
actions {
set {
name = "interaction.start"
value = "now()"
type = "Expression"
}
}
}
...
}
}
This approach avoids complex custom scripting and relies on native platform capabilities. The key is placing this node right after the bot decision logic but before the Queue node. If you are using Terraform for deployment, ensure the genesyscloud_flow resource includes this data node in the correct sequence. Also, verify that the WFM schedule adherence report configuration is not overriding this with a static “queue wait” definition. Sometimes the issue lies in how the report calculates “active” time versus “in queue” time. If the skew persists, check the queue’s max_wait_time settings, as aggressive timeouts can also distort these metrics. This pattern has resolved similar latency issues in multi-region deployments where timezone conversions caused timestamp drift. Always validate the fix by comparing the interaction.start timestamp in the API response against the WFM report data for a test agent.