Agent AI - Kore.ai Docs

Intelligent AI assistant for contact center agents to boost productivity.

Overview

Agent AI provides real-time assistance to human agents:

Suggested responses based on conversation context.
Automated call and chat summaries.
Knowledge base search integration.

How It Works

┌─────────────────────────────────────────────────────────────────┐ │ Live Conversation │ │ │ │ Customer: “I received the wrong item in my order │ └─────────────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Agent AI Engine │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Context │ │ Knowledge │ │ Response │ │ │ │ Analysis │ │ Search │ │ Generation │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ └─────────────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Agent Desktop Widget │ │ │ │ Suggested Response: │ │ “I apologize for the inconvenience. I can help you with a │ │ replacement or refund. Let me pull up your order details.” │ │ │ │ Relevant Knowledge: │ │ • Wrong Item Policy (confidence: 95%) │ │ • Return Process Guide (confidence: 87%) │ │ │ │ Recommended Actions: │ │ [Create Return] [Issue Refund] [Escalate to Supervisor] │ └─────────────────────────────────────────────────────────────────┘

Features

Real-Time Suggestions

Contextual response suggestions during conversations:

Feature	Description
Auto-suggest	Suggestions appear as customer speaks
One-click use	Insert suggestion with single click
Edit before send	Modify suggestions as needed
Learn from edits	System improves from agent modifications

Configuration example:

Suggestions:
  trigger: continuous
  display_count: 3
  confidence_threshold: 0.7
  sources:
    - knowledge_base
    - canned_responses
    - conversation_history
  tone: professional

Automated Summaries

Generate conversation summaries automatically:

Summary Type	When Generated
Real-time	Updated as conversation progresses
After-call	Complete summary at interaction end
Disposition	Structured outcome summary

Summary configuration example:

Auto-Summary:
  format: structured
  sections:
    - customer_issue
    - resolution
    - follow_up_required
  length: concise
  auto_save_to_crm: true

Knowledge Assistance

Integrated knowledge search:

Search triggered automatically by conversation context.
Manual search with natural language queries.
Results ranked by relevance.
Source citations included.

Configuration

Enable Agent AI

Navigate to Agent AI → Configuration.
Enable Agent AI for desired queues.
Configure feature settings.
Test with pilot agents.

Feature Settings

Setting	Options
Suggestion mode	Continuous, On-demand, Disabled
Auto-summary	Enabled, Disabled
Knowledge search	Auto, Manual, Both
NBA	Enabled, Disabled

Integration

Connect Agent AI to:

Search AI — For knowledge retrieval.
CRM — For customer context.
Case management — For action execution.
Quality AI — For coaching feedback.

Agent Experience

Agent AI appears as a widget in the agent console:

┌─────────────────────────────────────┐ │ Agent AI [─] [×]│ ├─────────────────────────────────────┤ │ │ │ Suggested Response │ │ ┌─────────────────────────────────┐ │ │ │ I understand your concern about │ │ │ │ the billing charge. Let me │ │ │ │ address them. │ │ │ └─────────────────────────────────┘ │ │ [Use] [Copy] [👍] [👎] │ │ │ │ Knowledge │ │ • Billing FAQ (95%) │ │ • Refund Policy (88%) │ │ [Search manually] │ │ │ │ Quick Actions │ │ [Refund] [Credit] [Escalate] │ │ │ └─────────────────────────────────────┘

Feedback Loop

Agents can rate suggestions:

Thumbs up — Good suggestion.
Thumbs down — Unhelpful suggestion.
Edits — System learns from modifications.

Feedback improves suggestion quality over time.

Analytics

Agent AI Metrics

Metric	Description
Suggestion acceptance	% of suggestions used
Time saved	Handle time reduction
Knowledge utilization	Searches and clicks
NBA conversion	Recommended actions taken

Quality Impact

Track quality improvements:

First contact resolution rate.
Customer satisfaction scores.
Quality evaluation scores.
Handle time trends.

Best Practices

Deployment

Start with a pilot group of agents.
Gather feedback and iterate.
Roll out gradually by queue/team.
Monitor adoption and adjust.

Knowledge Quality

Keep knowledge base current.
Remove outdated content.
Add content for common queries.
Monitor search failures.

Agent Training

Introduce Agent AI in agent training.
Explain feedback mechanism importance.
Show how to use suggestions effectively.
Address concerns about AI assistance.

​Overview

​How It Works

​Features

​Real-Time Suggestions

​Automated Summaries

​Knowledge Assistance

​Configuration

​Enable Agent AI

​Feature Settings

​Integration

​Agent Experience

​Desktop Widget

​Feedback Loop

​Analytics

​Agent AI Metrics

​Quality Impact

​Best Practices

​Deployment

​Knowledge Quality

​Agent Training