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We have the following setup for voice: Queues QA, QK and QW being handled by teams TA and TW. All agents of TA are members of QA via skill group, all agents of TW are members of TW, and all agents from both teams are members of QK. We use "best available agent" routing, no bullseye, no skill matching except language skills. 

Our idea with this setup was that QK will be "fairly" distributed between teams TA and TW, based on the load from their own queues. If team TW receives a lot of calls and has a high waiting time, its agents would get less calls from QK, as the QK interactions would have a lower priority given that they wait less in the queue before an agent from TA would be available and would take the call (as TA has less calls, and less waiting time). 

However we found that not to be the case, and we cannot really understand where our logic fails. Reading the documentation at https://help.genesys.cloud/articles/genesys-cloud-acd-processing/ would support our setup, especially under Scenario 2 (which we assume mostly happens, as the SLA is not met and waiting time is fairly high)

Last week we had QA with an SLA of 75%, QW with 47%, but still TW took 2/3 of the calls from QK, which is proportional to the team size, even though QW SLA was way lower, and avg. waiting time double of QA (and of QK).

Any ideas why this doesn't work as intended ?

Hi Andrei, I think the current design does not really guarantee fairness between TA and TW. It creates a shared QK pool where both teams are eligible, so distribution will mostly follow real-time agent availability, skill/language eligibility, and the size of the available pool... not the SLA or historical load of QA/QW.
That would explain why TW still receives a larger share of QK calls if TW represents a larger portion of the eligible agents.
If QK needs to be distributed based on the current load of QA and QW, that logic needs to be made explicit in the routing design. For example, by splitting QK into QK_TA/QK_TW and routing via Architect logic, using team-specific skills, bullseye routing, or a Data Action to check current load before choosing the target queue.

Hi Andrei,

My understanding is that Best Available Agent routing looks at interaction-level scoring rather than queue-level SLA. That could explain why QK was distributed roughly by team size even though QW had a much lower SLA.