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 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 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: sales@genesys.com 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