Hi Gabriel,
This is a very common challenge in large Copilot deployments.
Even with separated KBs, semantic similarity can still cause cross-brand retrieval if:
- terminology overlaps
- agents share queues/workspaces
- articles have similar operational language
The best results I've seen come from combining:
- queue segmentation
- strict KB scoping
- metadata/tag standardization
- minimizing duplicated operational wording
Another important factor is reducing "generic" article titles/content that semantically match multiple brands.
Some teams also isolate:
- intents
- routing
- and Copilot context
As early as possible in Architect before the interaction reaches the agent.
Pure KB separation alone is often not enough in shared-agent environments.
Regards!
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Lilian Lira
Services and Developer Manager
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Original Message:
Sent: 05-07-2026 21:15
From: Gabriel Garcia
Subject: Improving Agent Copilot Knowledge Isolation in Multi-Brand Environments
Hi everyone,
We are testing a multi-brand Agent Assist deployment in Genesys Cloud where agents support different business units inside the same org.
The challenge we are facing is knowledge grounding contamination between brands. Even after separating knowledge bases, Copilot recommendations sometimes bring content that is operationally correct but belongs to another brand/process.
Current setup:
- Separate knowledge bases
- Queue segmentation
- Different intents/use cases
- Shared agent population
We are evaluating:
- queue-based KB association
- flow-level contextual grounding
- dynamic knowledge exposure strategies
Curious to know how others are handling multi-brand or multi-business-unit Copilot deployments without creating duplicated environments.
Has anyone found an effective production strategy to improve KB isolation and recommendation precision?
#AICopilot(Agent,Supervisor,Admin)
#AICopilot(Agent,Supervisor,Admin)
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Gabriel Garcia
NA
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