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Agent Copilot (Agent Assist)

Study Notes

Topic Description
Agent Copilot AI-powered real-time guidance system for agents
Also Known As Agent Assist, Copilot Assistant
Purpose Provides real-time recommendations and knowledge during customer interactions
Activation Requires Premium edition and Customer Insights module
Benefit Reduces handle time, improves first-contact resolution, enhances agent confidence

Navigation

Admin → Architect → Agent Copilot OR Admin → Contact Center → Agent Assistance → Copilot Configuration

Agent Copilot Overview

Agent Copilot is an AI-powered assistant that provides real-time guidance and recommendations to agents during customer interactions. It analyzes the conversation in real-time and suggests relevant knowledge articles, scripts, and next steps to improve interaction quality and resolution.

Key Capabilities

  • Real-time recommendations - Suggests actions based on conversation context
  • Knowledge article suggestions - Recommends relevant articles automatically
  • Script guidance - Provides talking points and recommended language
  • Sentiment analysis - Monitors customer emotion and suggests de-escalation
  • Next action recommendations - Predicts optimal next steps
  • Agent learning - Improves over time with agent feedback

How It Works

  1. Agent answers contact
  2. Copilot monitors conversation in real-time
  3. AI analyzes conversation intent and context
  4. System searches knowledge base for relevant information
  5. Recommendations displayed in agent interface
  6. Agent reviews and applies suggestions
  7. Feedback loop improves future recommendations

Edition & Module Requirements

Requirement Details
Minimum Edition Premium Edition required
Module Customer Insights add-on module
License Type Agent licenses with Copilot enabled
Setup Admin configuration in Architect
Integration Knowledge management system required

Study Notes - Copilot Features

Feature Description Use Case
Knowledge Recommendations AI-suggested articles from knowledge base Technical support, FAQs
Script Guidance Real-time conversation scripts and talking points Sales, compliance-heavy calls
Sentiment Monitoring Real-time emotion analysis of customer De-escalation, empathy guidance
Next Action Suggestions Recommended next steps for agent Call routing, transfer decisions
Agent Performance Tips Real-time coaching during interaction Training reinforcement
Historical Context Customer interaction history suggestions Personalization, context
Product Recommendations Sales-specific recommendations Upsell, cross-sell opportunities
Compliance Reminders Real-time compliance guidance Regulatory requirements

Implementation Guide

Step 1: Prerequisites & Planning

  1. Ensure organization has Premium edition
  2. Purchase Customer Insights module
  3. Audit existing knowledge base
  4. Document Copilot use cases by queue
  5. Plan knowledge article optimization
  6. Review agent readiness and training needs

Step 2: Knowledge Base Configuration

  1. Navigate to Admin → Knowledge Management
  2. Create/organize knowledge articles
  3. Tag articles with metadata (category, queue, intent)
  4. Add keywords and synonyms for better matching
  5. Ensure article quality and accuracy
  6. Set article access permissions

Step 3: Enable Agent Copilot

  1. Go to Admin → Architect → Agent Copilot
  2. Enable "Agent Copilot" toggle
  3. Select knowledge base source
  4. Configure recommendation parameters
  5. Set recommendation types to display
  6. Choose recommendation frequency

Step 4: Customize by Queue

  1. Create queue-specific Copilot settings
  2. Configure knowledge sources per queue
  3. Set relevance thresholds
  4. Define script templates
  5. Establish sentiment trigger rules
  6. Test queue-specific configurations

Step 5: Agent Training & Rollout

  1. Train agents on Copilot interface
  2. Explain recommendation types
  3. Practice with sample interactions
  4. Gather initial feedback
  5. Monitor early adoption
  6. Provide ongoing support

Step 6: Monitoring & Optimization

  1. Review Copilot engagement metrics
  2. Monitor agent utilization of recommendations
  3. Track recommendation accuracy
  4. Gather agent feedback
  5. Optimize knowledge articles
  6. Adjust recommendation parameters

How to Implement

Phase Description Timeline
Planning Audit knowledge base and define use cases Week 1-2
Setup Configure Copilot and knowledge sources Week 2-3
Content Create/optimize knowledge articles Week 3-5
Training Educate agents on features and usage Week 5-6
Pilot Deploy to single queue with monitoring Week 6-7
Rollout Enable across all queues Week 7-8
Optimization Monitor and tune performance Ongoing

Agent Copilot Architecture

Incoming Contact
    ↓
Agent Accepts Contact
    ↓
Copilot Monitoring Begins
├── Real-time Conversation Analysis
├── Intent Detection
└── Context Extraction
    ↓
AI-Powered Recommendation Engine
├── Knowledge Base Search
├── Relevance Scoring
├── Sentiment Analysis
└── Prediction Models
    ↓
Recommendation Generation
├── Knowledge Articles
├── Scripts & Talking Points
├── Next Action Suggestions
├── Sentiment De-escalation Tips
└── Product/Service Recommendations
    ↓
Display to Agent Interface
    ↓
Agent Reviews Recommendations
    ↓
Agent Applies (or Dismisses) Suggestions
    ↓
Feedback Loop Updates AI Model

Real-Time Recommendation Flow

Customer Says: "I've been trying to reset my password for hours"

Copilot Analyzes:
├── Intent: Password Reset Help
├── Sentiment: Frustrated/Angry
├── Context: Technical Issue
└── Duration: Extended problem
    ↓
Copilot Recommendations Display:

1. KNOWLEDGE ARTICLE (High Confidence)
   ├── "Password Reset Troubleshooting"
   ├── Relevance: 94%
   └── Steps: 5-7 minute resolution

2. SENTIMENT GUIDANCE (Urgent)
   ├── Suggest: Apologize for inconvenience
   ├── Tone: Empathetic
   └── De-escalation: Acknowledge frustration

3. NEXT ACTION (Suggested)
   ├── Offer: Manual password reset
   ├── Escalation: If still unresolved
   └── Followup: Offer premium support

4. SCRIPT SUGGESTION (Optional)
   ├── "I completely understand how frustrating that is..."
   ├── "Let me walk you through the fastest solution..."
   └── "If this doesn't work, I'll reset it for you personally"
    ↓
Agent Applies Recommendations
    ↓
Customer Issue Resolved
    ↓
System Captures Feedback

Copilot Interface Components

Agent Desktop View:

┌─────────────────────────────────────────┐
│  Current Interaction                     │
│  Customer: John Smith                    │
│  Queue: Technical Support                │
│  Duration: 3:45                          │
├─────────────────────────────────────────┤
│  COPILOT RECOMMENDATIONS                 │
├─────────────────────────────────────────┤
│                                          │
│  📚 KNOWLEDGE ARTICLES                   │
│  ├─ Password Reset Guide (94% match)    │
│  ├─ Two-Factor Auth Setup (87% match)   │
│  └─ Account Recovery (76% match)        │
│                                          │
│  😟 SENTIMENT ALERT                      │
│  ├─ Customer: FRUSTRATED                │
│  └─ Suggestion: De-escalate & empathize│
│                                          │
│  ➡️  NEXT ACTIONS                        │
│  ├─ Offer manual password reset         │
│  ├─ Provide security questions          │
│  └─ Escalate if unsuccessful            │
│                                          │
│  💬 SUGGESTED SCRIPT                     │
│  "I understand how frustrating this is. │
│   Let me walk you through the quickest  │
│   way to get this resolved..."          │
│                                          │
│  [👍 Helpful] [👎 Not Helpful]           │
└─────────────────────────────────────────┘

Real Flow Scenario: Sales Queue with Copilot

Agent: "Hi, thanks for calling. How can I help?"

Customer: "I'm interested in upgrading my plan"

Copilot Recommendations Appear:

1. KNOWLEDGE - Sales Playbook
   ├─ Current Plan Analysis
   ├─ Available Upgrades
   └─ Pricing Information

2. PRODUCT RECOMMENDATIONS
   ├─ Upsell: Premium Plan (+40% revenue potential)
   ├─ Cross-sell: Support Package
   └─ Offer: 2-month discount if upgrading today

3. NEXT ACTION SUGGESTIONS
   ├─ Qualify: Ask about usage patterns
   ├─ Present: Show cost-benefit analysis
   └─ Close: Offer contract details

4. SCRIPT SUGGESTION
   "Based on your usage, the Premium Plan
    would save you money and give you these
    benefits: [list]. Can I set that up?"
    ↓
Agent Applies Script & Recommendations
    ↓
Customer Upgrades (upsell successful)
    ↓
System Logs: Agent applied recommendation
    ↓
Next Sale: System learns and improves recommendations

Real Flow Scenario: Support Queue with Sentiment

Customer Calls (Angry Tone)

Agent Answers

Copilot Immediately Detects:
├─ Sentiment: NEGATIVE (85% confidence)
├─ Emotion: Frustrated/Angry
├─ Risk: Potential churn
└─ Recommended Action: De-escalate NOW
    ↓
Sentiment Alert in Copilot:
"Customer is frustrated. 
 Suggested response:
 'I'm sorry you're experiencing this issue.
  I'm going to personally make sure we get
  this resolved for you right now.'"
    ↓
Copilot Provides Knowledge:
├─ Issue Resolution Articles
├─ Escalation Path (if needed)
└─ Retention Options
    ↓
Agent Applies De-escalation Approach
    ↓
Customer Sentiment Improves
    ↓
System Logs Improvement
    ↓
Issue Resolved Successfully

Omnichannel Copilot Support

Voice Interactions

Real-time Copilot assistance during calls
├── Conversation transcription
├── Intent analysis
├── Real-time knowledge suggestions
└── Sentiment monitoring

Chat Interactions

Suggested responses during chat
├── Pre-written messages
├── Quick knowledge links
├── Canned responses with personalization
└── Sentiment-based guidance

Email Interactions

Copilot assistance with draft responses
├── Knowledge recommendations
├── Tone suggestions
├── Template recommendations
└── Compliance checking

Social Media Interactions

Real-time assistance for public responses
├── Tone and brand consistency
├── Knowledge suggestions
├── De-escalation for negative sentiment
└── Escalation recommendations

Usage Scenarios

Scenario Solution Outcome
High call volume with complex issues Copilot suggests knowledge articles Reduced AHT, faster resolution
New agents lacking experience Real-time script and guidance Improved FCR, faster ramp-up
Compliance-heavy calls Compliance reminders and scripts Reduced risk, better compliance
Frustrated customers Sentiment analysis with de-escalation tips Improved satisfaction, retention
Sales team underperforming Upsell/cross-sell recommendations Increased revenue per interaction
Quality issues with call handling Real-time coaching suggestions Improved quality scores
Agent knowledge gaps Targeted knowledge recommendations Improved FCR, fewer escalations
Multilingual support Language-specific scripts and guidance Consistent quality across languages

Knowledge Base Organization for Copilot

Knowledge Base Structure:

├─ TECHNICAL SUPPORT
│  ├─ Password Reset
│  │  ├─ Steps 1-5
│  │  ├─ Common Issues
│  │  └─ When to Escalate
│  ├─ Two-Factor Auth
│  ├─ Account Recovery
│  └─ Billing Issues
│
├─ SALES
│  ├─ Plan Comparison
│  ├─ Pricing Information
│  ├─ Promotional Offers
│  ├─ Upsell Scenarios
│  └─ Contract Terms
│
├─ CUSTOMER SUCCESS
│  ├─ Onboarding Steps
│  ├─ Best Practices
│  ├─ Feature Usage
│  └─ Integration Guides
│
└─ COMPLIANCE
   ├─ Required Disclosures
   ├─ Privacy Policies
   ├─ Legal Requirements
   └─ Prohibited Actions

Copilot Performance Metrics

Recommendation Quality

Metric Target Purpose
Recommendation Accuracy >85% Agent finds recommendations helpful
Agent Acceptance Rate >60% Agents actively use suggestions
Relevance Score >4/5 Recommendations match context
Time to Resolution -15% Faster with Copilot assistance
Knowledge Article Match Rate >90% Correct article suggested

Agent Performance

Metric Target Purpose
First Contact Resolution +10-25% Copilot guidance improves outcomes
Average Handle Time -10-20% Faster resolution with suggestions
Customer Satisfaction +5-15% Better agent performance
Quality Score +5-10% Improved call quality
Agent Confidence +20-30% Subjective improvement

Best Practices

Knowledge Base Optimization

  • Keep articles current - Update quarterly or when processes change
  • Write for clarity - Use simple language agents can understand quickly
  • Include visuals - Screenshots and diagrams help comprehension
  • Provide examples - Real scenarios help agents apply knowledge
  • Tag thoroughly - Use keywords and metadata for better matching
  • Test accuracy - Verify all knowledge is correct before publishing

Copilot Configuration

  • Start simple - Begin with high-confidence recommendations only
  • Tune relevance - Adjust thresholds based on agent feedback
  • Monitor adoption - Track which recommendations agents use
  • Gather feedback - Ask agents what would help them more
  • Iterate quickly - Update knowledge and rules frequently
  • A/B test - Try different recommendation approaches

Agent Enablement

  • Provide training - Agents need to understand Copilot features
  • Share success stories - Show how other agents use it effectively
  • Encourage experimentation - Let agents find what works for them
  • Make feedback easy - Simple thumbs up/down for recommendations
  • Celebrate improvements - Recognize agents who adopt well
  • Continuous learning - Regular coaching on Copilot usage

Monitoring & Optimization

  • Track recommendations - See which articles agents use most
  • Monitor accuracy - Ensure recommendations are helpful
  • Gather sentiment - Ask agents about Copilot effectiveness
  • Review metrics - Check impact on FCR, AHT, CSAT
  • Optimize content - Update articles agents find unhelpful
  • Plan improvements - Use data to guide enhancements

Common Implementation Scenarios

Scenario 1: Technical Support with Knowledge-Heavy Topics

Configuration:
├── Knowledge base with 200+ articles
├── Intent-based recommendation
├── Sentiment monitoring enabled
├── De-escalation scripts
└── Escalation pathways

Expected Results:
├── FCR improvement: 15-25%
├── AHT reduction: 12-18%
└── Agent confidence: +25%

Scenario 2: Sales Team with Upsell Focus

Configuration:
├── Product recommendation engine
├── Upsell/cross-sell playbooks
├── Customer history integration
├── Pricing recommendations
└── Contract template suggestions

Expected Results:
├── Revenue per call: +15-30%
├── Sales conversion: +10-20%
└── Agent productivity: +20%

Scenario 3: Multilingual Support

Configuration:
├── Knowledge base in multiple languages
├── Language-specific scripts
├── Tone guidance per language
├── Cultural sensitivity prompts
└── Translation recommendations

Expected Results:
├── FCR consistent across languages
├── Quality standardization
└── Customer satisfaction: +10-15%

Troubleshooting Guide

Issue Cause Resolution
No recommendations appearing Knowledge base empty or not indexed Populate knowledge base and index
Irrelevant recommendations Poor knowledge article tagging Review and improve metadata/keywords
Agents ignoring recommendations Not helpful or slow to appear Adjust relevance thresholds and review content
Slow recommendation loading Too many articles to search Add more specific keywords and metadata
Sentiment detection inaccurate Model needs more training data Collect more interactions and retrain
High false positive sentiment Threshold too sensitive Adjust sensitivity settings lower
Copilot not working for all queues Not enabled for specific queues Enable in queue configuration
Knowledge articles outdated No review process Establish content review cycle
Low agent adoption Agents don't understand value Provide additional training
Module not appearing License not purchased or enabled Verify Premium edition and module purchase

Agent Copilot vs. Traditional Knowledge Base

Feature Agent Copilot Traditional Knowledge
Real-time suggestions Yes, automatic Manual search required
Context awareness AI-powered, contextual Search-based only
Sentiment analysis Yes No
Next action prediction Yes No
Script guidance Automatic Manual lookup
Learning capability Improves over time Static
Setup complexity Medium-High Low
Ongoing maintenance High (ML tuning) Medium
Agent productivity +20-30% potential Baseline
Customer satisfaction +5-15% improvement Baseline

Sentiment Analysis in Copilot

Sentiment Detection Levels

Extremely Negative (-2)
├─ Angry, frustrated, hostile
├─ Risk: High churn likelihood
└─ Action: Immediate de-escalation

Negative (-1)
├─ Disappointed, concerned
├─ Risk: Medium churn likelihood
└─ Action: Empathy + quick resolution

Neutral (0)
├─ Standard interaction tone
├─ Risk: Low
└─ Action: Normal service

Positive (+1)
├─ Satisfied, pleased
├─ Risk: None
└─ Action: Reinforce positive experience

Extremely Positive (+2)
├─ Happy, delighted
├─ Opportunity: Upsell/cross-sell
└─ Action: Leverage positive sentiment

Integration Scenarios

With Workforce Optimization

Copilot + WFO
├── Agent assisted by Copilot
├── Interaction recorded
├── Quality scored with AI
├── Coaching recommendations generated
└── Coaching delivered back to agent

With Predictive Routing

Copilot + Predictive Routing
├── Best agent routed to contact
├── Copilot assists during interaction
├── Recommendations improve outcome
└── System learns for future routing

With Analytics

Copilot + Analytics
├── Copilot recommendation usage tracked
├── Impact on metrics measured
├── Dashboards show Copilot effectiveness
└── Data guides optimization

Interview Cheat Sheet

Question Answer
What is Agent Copilot? AI-powered real-time guidance system for agents
Also known as? Agent Assist or Copilot Assistant
What are the requirements? Premium edition + Customer Insights module
What does it recommend? Knowledge articles, scripts, next actions, sentiment guidance
How does sentiment analysis help? Detects customer frustration and suggests de-escalation
Where is it configured? Admin → Architect → Agent Copilot
What channels does it support? Voice, chat, email, social media
How does it improve performance? FCR +10-25%, AHT -10-20%, CSAT +5-15%
What's required for success? Quality knowledge base and agent training
How does machine learning help? System learns from recommendations agents use vs. ignore
Can agents reject recommendations? Yes, agents decide what to apply
How long until ROI? 4-8 weeks to see significant improvement
What if knowledge base is empty? Copilot won't have content to recommend
How does it work with omnichannel? Provides channel-specific guidance (voice, chat, email, etc.)
What's the biggest success factor? Quality, current, well-organized knowledge base

Key Takeaways

  • Real-Time AI Assistance - Copilot provides in-the-moment guidance during interactions
  • Knowledge-Driven - Quality depends on knowledge base content and organization
  • Sentiment Awareness - Monitors emotion and suggests appropriate responses
  • Omnichannel Support - Works across voice, chat, email, and social channels
  • Premium Feature - Requires Premium edition and Customer Insights module
  • Significant Impact - FCR improvements of 10-25% typical
  • Machine Learning - System improves recommendations based on agent actions
  • Agent Adoption Critical - Success depends on agents trusting and using recommendations
  • Ongoing Content Management - Knowledge base requires regular updates
  • Quick ROI - 4-8 weeks to measurable improvements

Migration Path from Manual Knowledge Search

Phase 1: Assessment (Weeks 1-2)

├── Audit current knowledge base
├── Identify content gaps
├── Plan reorganization
└── Set quality standards

Phase 2: Content Preparation (Weeks 2-4)

├── Create/update knowledge articles
├── Add metadata and keywords
├── Organize by intent and queue
└── Quality review all content

Phase 3: Copilot Setup (Weeks 4-5)

├── Enable Agent Copilot
├── Configure recommendations
├── Set relevance thresholds
└── Establish monitoring

Phase 4: Agent Training (Week 5-6)

├── Educate on Copilot features
├── Practice with sample interactions
├── Explain recommendation types
└── Share best practices

Phase 5: Pilot & Optimization (Weeks 6-8)

├── Deploy to single queue
├── Monitor and gather feedback
├── Optimize recommendations
└── Plan full rollout

Phase 6: Full Deployment (Week 8+)

├── Enable across all queues
├── Provide ongoing support
├── Monitor metrics
└── Continuous improvement

Additional Resources

Official Documentation Links

  • Genesys Cloud Agent Copilot Guide: https://help.genesys.com/genesyscloud/current/en-us/AgentCopilot.html
  • Knowledge Management Setup: https://help.genesys.com/genesyscloud/current/en-us/KnowledgeManagement.html
  • Customer Insights Module: https://help.genesys.com/genesyscloud/current/en-us/CustomerInsights.html

Support Contacts

  • Genesys Sales: [email protected]
  • Genesys Support: https://support.genesys.com
  • Community Forums: https://community.genesys.com

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Version: 1.0

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