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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.
  • Next-best-action recommendations.

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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

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Real-Time Suggestions

Contextual response suggestions during conversations:
FeatureDescription
Auto-suggestSuggestions appear as customer speaks
One-click useInsert suggestion with single click
Edit before sendModify suggestions as needed
Learn from editsSystem 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

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Automated Summaries

Generate conversation summaries automatically:
Summary TypeWhen Generated
Real-timeUpdated as conversation progresses
After-callComplete summary at interaction end
DispositionStructured outcome summary
Summary configuration example: 
Auto-Summary:
  format: structured
  sections:
    - customer_issue
    - resolution
    - follow_up_required
  length: concise
  auto_save_to_crm: true

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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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Next-Best-Action

Recommended actions based on context, example: 
NBA Rules:
  - condition: sentiment == "frustrated" AND issue_unresolved
    action: offer_supervisor_escalation
    priority: high

  - condition: customer_tier == "premium" AND wait_time > 5min
    action: offer_compensation
    priority: medium

  - condition: issue_type == "billing"
    action: show_billing_tools
    priority: normal

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Configuration

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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.

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Feature Settings

SettingOptions
Suggestion modeContinuous, On-demand, Disabled
Auto-summaryEnabled, Disabled
Knowledge searchAuto, Manual, Both
NBAEnabled, Disabled

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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

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

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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

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Agent AI Metrics

MetricDescription
Suggestion acceptance% of suggestions used
Time savedHandle time reduction
Knowledge utilizationSearches and clicks
NBA conversionRecommended actions taken

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Quality Impact

Track quality improvements:
  • First contact resolution rate.
  • Customer satisfaction scores.
  • Quality evaluation scores.
  • Handle time trends.

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Best Practices

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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.

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Knowledge Quality

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

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Agent Training

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