Agent AI

Intelligent AI assistant for contact center agents to boost productivity.

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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.

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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] │ └─────────────────────────────────────────────────────────────────┘

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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.

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Configuration

Enable Agent AI

1. Navigate to Agent AI → Configuration.
2. Enable Agent AI for desired queues.
3. Configure feature settings.
4. 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.

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Agent Experience

Desktop Widget

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.

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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.

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Best Practices

Deployment

1. Start with a pilot group of agents.
2. Gather feedback and iterate.
3. Roll out gradually by queue/team.
4. 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.

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