Genesys Cloud - Main

Hi all,

We are testing a hybrid Virtual Agent architecture combining:

• traditional bot intents

• generative AI responses

• agent escalation

One challenge we are facing is conversation context quality after escalation to the live agent.

In some cases:

• the bot resolves most of the intent correctly

• but the transferred summary/context is too generic

• or misses critical customer decisions collected during the conversation

We are currently evaluating:

• custom conversation summaries

• structured slot persistence

• external orchestration/memory layers

For teams already using generative AI + live escalation in production:
How are you ensuring high-quality context transfer to agents without overwhelming them with the full transcript?

Interested in practical approaches that improved:

• agent experience

• reduced repetition

• lower handle time

#ConversationalAI(Bots,VirtualAgent,etc.)

#ConversationalAI(Bots,VirtualAgent,etc.)

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Gabriel Garcia
NA
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Hi Gabriel, how are you?

One thing I've learned in production is that "full transcript transfer" rarely works well for agents.

The best results usually come from:

• structured summaries
• extracted entities/slots
• explicit customer intent/state
• action-oriented context

Instead of:

"Here is the entire conversation"

focus on:

• what the customer wants
• what was already validated
• what failed
• next recommended action

A hybrid pattern works best:

• deterministic slot/entity persistence
• AI-generated summarization

Relying only on LLM summarization can sometimes miss operationally critical information collected earlier in the flow.

Best Regards!

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Lilian Lira
Services and Developer Manager
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