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

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

Agent answers contact

Copilot monitors conversation in real-time

AI analyzes conversation intent and context

System searches knowledge base for relevant information

Recommendations displayed in agent interface

Agent reviews and applies suggestions

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

Ensure organization has Premium edition

Purchase Customer Insights module

Audit existing knowledge base

Document Copilot use cases by queue

Plan knowledge article optimization

Review agent readiness and training needs

Step 2: Knowledge Base Configuration

Navigate to Admin → Knowledge Management

Create/organize knowledge articles

Tag articles with metadata (category, queue, intent)

Add keywords and synonyms for better matching

Ensure article quality and accuracy

Set article access permissions

Step 3: Enable Agent Copilot

Go to Admin → Architect → Agent Copilot

Enable "Agent Copilot" toggle

Select knowledge base source

Configure recommendation parameters

Set recommendation types to display

Choose recommendation frequency

Step 4: Customize by Queue

Create queue-specific Copilot settings

Configure knowledge sources per queue

Set relevance thresholds

Define script templates

Establish sentiment trigger rules

Test queue-specific configurations

Step 5: Agent Training & Rollout

Train agents on Copilot interface

Explain recommendation types

Practice with sample interactions

Gather initial feedback

Monitor early adoption

Provide ongoing support

Step 6: Monitoring & Optimization

Review Copilot engagement metrics

Monitor agent utilization of recommendations

Track recommendation accuracy

Gather agent feedback

Optimize knowledge articles

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

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