Agent Copilot
Genesys PureCloud Agent Copilot Documentation
Study Notes
| Topic | Description |
|---|---|
| Agent Copilot | Real-time AI assistance for agents during customer interactions |
| Core Function | Determines customer intent and provides next best actions in real-time |
| After-Call Work | Automates summaries, wrap-up codes, and resolution tracking |
| AI Skills | Modular AI capabilities that can be combined and customized |
| On-Queue Behavior | Operates continuously during interaction, adapting to conversation flow |
| Key Results | 5-min AHT reduction, 1.5-min hold time reduction, 2-min ACW reduction |
Navigation
Admin → Contact Center → Agent Assistance → Agent Copilot Configuration OR Admin → AI Studio → Agent Copilot Settings
Agent Copilot Overview
Genesys Agent Copilot enhances communication between the agent and the customer by determining customer intent and providing relevant next best actions to the agent. It offers assistance in after-call work and provides a summary of the conversation, reason for contact, resolution, and suggests wrap-up codes.
Genesys Agent Copilot integrates AI and Large Language Models to deliver intelligent assistance capabilities that transform agent productivity and customer service delivery. The platform automates after-call work through intelligent wrap-up code suggestions, conversation summaries, and resolution tracking, while providing real-time next-best-action recommendations and customer intent determination during live interactions.
Key Capabilities
- Real-time agent assist during customer interactions on voice and digital channels
- Intelligent next-best-action recommendations based on conversation context
- Automatic conversation summaries and wrap-up code suggestions
- Customer intent determination and understanding
- Multi-language support (13+ languages including English, Spanish, French, German, Japanese, Korean, Arabic, Hindi, Portuguese, Swedish, Dutch, Italian)
- Custom AI Guides built through AI Studio integration
- Knowledge article integration and answer highlighting
- Queue-specific configuration and management
- Performance analytics and monitoring dashboards
Performance Metrics
Agent Copilot users have seen results including:
- 5-minute decrease of average handle time
- 1.5-minute decrease in average hold time
- 2-minute decrease in after-call work time
Edition & Module Requirements
| Requirement | Details |
|---|---|
| Minimum Edition | Premium Edition (Genesys Cloud CX 1-4) |
| Module | Included or Agent Copilot add-on depending on tier |
| License Type | Per-user licensing |
| AI Tokens | Requires tokens: 40-60 per user for Agent Copilot |
| Knowledge Base | Genesys Knowledge Workbench or Knowledge Fabric recommended |
Study Notes - Agent Copilot Features
| Feature | Description | Benefit |
|---|---|---|
| Intent Recognition | AI understands customer goals from conversation | Faster issue identification |
| Next-Best-Action | Recommends optimal next step for agent | Guides resolution path |
| Auto-Summarization | Generates conversation summary automatically | Reduces after-call work |
| Wrap-Up Code Suggestions | AI suggests appropriate completion codes | Faster code selection |
| Knowledge Integration | Surfaces relevant knowledge articles automatically | Faster access to information |
| Answer Highlighting | Highlights key parts of knowledge articles | Easier information scanning |
| Sentiment Monitoring | Detects customer emotion in real-time | Enables de-escalation |
| Multi-Language Support | Operates in 13+ languages | Global team support |
| Custom Guides | Build AI agents using AI Studio | Automation flexibility |
| Performance Analytics | Dashboard showing copilot usage and impact | Measurement and optimization |
How Agent Copilot Works
During Interaction (Real-Time Assistance)
Agent Answers Contact
↓
Copilot Begins Monitoring
├── Real-time transcription
├── Intent analysis
└── Context understanding
↓
Customer Explains Issue
"I'm having trouble logging into my account"
↓
Copilot Analyzes
├── Intent: Account Access Issue
├── Context: Login problem
├── Emotion: Slightly frustrated
└── Knowledge needed: Account recovery
↓
Copilot Provides Assistance
├── Knowledge Articles
│ ├─ "Password Reset Steps" (94% match)
│ ├─ "Two-Factor Auth Help" (81% match)
│ └─ "Account Locked Recovery" (76% match)
│
├── Next-Best-Action
│ └─ "Guide customer through password reset"
│
└── Script Suggestion
└─ "I can help you regain access to your account..."
↓
Agent Reviews & Applies
├── Reads suggested article
├── Guides customer through steps
├── Issue resolved
└── Interaction continues
After Interaction (After-Call Work)
Contact Completes
↓
Copilot Generates Summary
├── Conversation Analysis
├── Key Points Extraction
├── Issue Resolution Status
└── Customer Sentiment Analysis
↓
Summary Displayed to Agent
"Customer called about password reset.
Issue resolved through email link sent.
Customer satisfied."
↓
Wrap-Up Code Suggestions
├─ Suggested Codes (in priority order)
│ ├─ Password Reset (89% confidence)
│ ├─ Account Access (71% confidence)
│ └─ Technical Support (34% confidence)
└─ Agent selects from suggestions
↓
Agent Reviews & Saves
├── Reviews summary (can edit)
├── Selects wrap-up code
├── Adds notes if needed
└── Saves interaction
AI Skills Architecture
AI Skills are modular, customizable AI capabilities that can be combined and deployed through Agent Copilot. Each skill targets a specific function or use case.
Types of AI Skills
Knowledge Skills
- Surfaces relevant knowledge articles automatically
- Answers customer questions from knowledge base
- Answer highlighting for quick scanning
- Knowledge caching for speed
Next-Best-Action Skills
- Recommends optimal next step in interaction
- Guides agent through proper procedure
- Suggests escalation or transfer when needed
- Predicts customer outcomes
Wrap-Up Code Skills
- Analyzes interaction to suggest wrap-up codes
- Reduces time agents spend on code selection
- Improves code accuracy through AI
- Learning improves accuracy over time
Sentiment & De-Escalation Skills
- Real-time sentiment detection
- De-escalation recommendations
- Tone guidance for responses
- Emotional intelligence coaching
Compliance & Risk Skills
- Monitors for compliance violations
- Alerts on risky language or actions
- Ensures required disclosures made
- Flags suspicious patterns
Custom Business Skills
- Created via AI Studio
- Built using AI Guides
- Business-specific logic
- Adaptive to business processes
On-Queue Behavior
How Agent Copilot Operates During Customer Interaction
Queue: Customer Support
Status: Agent Sarah actively handling call
Real-Time Copilot Behavior:
MOMENT 1 (0:15 into call)
├─ Copilot listening to conversation
├─ Transcribing in real-time
├─ Analyzing customer statements
└─ Building context understanding
MOMENT 2 (0:45 into call)
Customer: "I need to change my billing address"
├─ Intent Detected: Billing Update
├─ Copilot triggers knowledge search
├─ Results: 2 relevant articles found
└─ Displayed to Agent Sarah
MOMENT 3 (1:15 into call)
├─ Customer emotion: Neutral → Positive
├─ Issue complexity: Routine
├─ Next action: Process address change
└─ Copilot suggests: "Enter new address in system"
MOMENT 4 (1:45 into call)
Customer: "Can I also update my phone number?"
├─ New intent added to context
├─ Copilot expands knowledge articles
├─ Related articles surfaced
└─ Agent handles both requests together
MOMENT 5 (3:00 - Call ends)
├─ Both issues resolved
├─ Customer satisfied
├─ Copilot prepares summary
└─ Ready for after-call work
Agent Copilot Interface Components
Agent Desktop View
┌──────────────────────────────────────┐
│ ACTIVE CALL - Sarah Martinez │
│ Customer: John Smith │
│ Queue: Billing Support │
│ Duration: 3:42 │
└──────────────────────────────────────┘
┌──────────────────────────────────────┐
│ 📚 SUGGESTED KNOWLEDGE │
├──────────────────────────────────────┤
│ ✓ Update Billing Address (94%) │
│ ✓ Change Phone Number (87%) │
│ ○ Payment Methods (61%) │
│ │
│ [Show Full Articles] [Minimize] │
└──────────────────────────────────────┘
┌──────────────────────────────────────┐
│ ➡️ NEXT-BEST-ACTION │
├──────────────────────────────────────┤
│ Recommended: Process address update │
│ in customer record │
│ │
│ Steps: │
│ 1. Confirm new address │
│ 2. Verify zip code │
│ 3. Update system │
│ 4. Send confirmation │
└──────────────────────────────────────┘
┌──────────────────────────────────────┐
│ 😊 CUSTOMER SENTIMENT │
├──────────────────────────────────────┤
│ Current: POSITIVE │
│ Emotion: Satisfied │
│ Recommendation: Offer additional help│
└──────────────────────────────────────┘
[Notes Box]
[Hold/Transfer/Close Buttons]
Behavior by Interaction Type
Inbound Call
- Continuous listening and transcription
- Real-time intent and sentiment analysis
- Knowledge surfacing as topics emerge
- Next-best-action recommendations throughout
- Escalation alerts if conversation flags
- Summary generation at call end
Chat/Email
- Message-by-message analysis
- Delayed but thorough knowledge search
- Context building across multiple messages
- Suggested responses for agent review
- Quick-reply templates with personalization
- Handoff summaries for escalation
Outbound Call
- Prepopulates customer context
- Suggests opening statements
- Guides conversation flow
- Handles objections with recommendations
- Closing suggestions
- Outcome tracking
Implementation Guide
Step 1: Prerequisites & Planning
Step 2: Knowledge Base Setup
- Review/create knowledge articles in Knowledge Workbench
- Ensure articles are accurate and current
- Add metadata and keywords for searchability
- Organize by topic and queue
- Set article access permissions
- Test knowledge base connectivity
Step 3: Enable Agent Copilot
Step 4: Configure by Queue
- Create queue-specific Copilot settings
- Define which AI Skills are active per queue
- Configure knowledge sources
- Set wrap-up code matching rules
- Establish sentiment thresholds
- Test queue-specific behavior
Step 5: Agent Training
- Conduct overview training on Copilot interface
- Demonstrate real-time knowledge suggestions
- Practice reviewing and applying recommendations
- Explain wrap-up code suggestions
- Cover sentiment alerts and de-escalation
- Q&A and feedback
Step 6: Phased Rollout
- Deploy to pilot queue first
- Monitor closely for 1-2 weeks
- Gather agent and supervisor feedback
- Optimize based on learnings
- Expand to additional queues
- Scale to full deployment
Step 7: Ongoing Monitoring
- Review Agent Copilot dashboard daily
- Track key metrics (AHT reduction, token usage)
- Monitor agent adoption rates
- Gather continuous feedback
- Refine knowledge articles based on usage
- Optimize AI recommendations
Real-Flow Scenarios
Scenario 1: Technical Support with Knowledge Integration
Agent: "Thanks for calling technical support, how can I help?"
Customer: "My printer isn't connecting to WiFi"
Copilot immediately surfaces:
├─ 3 relevant knowledge articles
├─ Troubleshooting flowchart visual
├─ Video walkthrough link
└─ Quick-resolution steps
Agent: "I can help. Let me walk you through the WiFi
connection steps which usually resolve this..."
[Uses Copilot-suggested steps to guide customer]
Result: Issue resolved in 4 minutes
Copilot Summary:
"Customer called about printer WiFi connectivity issue.
Guided through troubleshooting steps. Issue resolved
after resetting router. Customer satisfied."
Wrap-up Code Suggestions:
├─ Printer Setup (92% confidence) ← Selected
├─ Technical Support (67% confidence)
└─ Network Issues (45% confidence)
Time Saved: 2 minutes (no manual summary or code lookup)
Scenario 2: Billing with Sentiment De-escalation
Customer (frustrated): "Why was I charged twice?!"
Copilot detects:
├─ Emotion: FRUSTRATED
├─ Sentiment score: -2.1/5
├─ Recommended action: Empathize & resolve immediately
└─ De-escalation tip: "Acknowledge frustration sincerely"
Agent (applying suggestion): "I completely understand
your frustration. Let me look into that right away
and fix this for you."
Copilot provides:
├─ Knowledge: "Duplicate Charge Resolution"
├─ Next action: "Issue refund immediately"
└─ Script: "Here's what happened and how I'm fixing it..."
[Agent processes refund while explaining]
Customer (relieved): "Oh wow, thanks for handling that so fast!"
Copilot updates sentiment: +1.5/5 (positive)
Summary generated:
"Customer called upset about duplicate charge. Explained
system error, issued refund immediately. Customer very
satisfied with quick resolution and empathetic service."
Result: De-escalation successful, issue resolved, positive outcome
Scenario 3: Sales with Cross-Sell Recommendations
Customer: "I'd like to upgrade my plan"
Copilot analyzes:
├─ Customer history: 2-year subscriber
├─ Current plan: Basic
├─ Usage patterns: Heavy data user
├─ Recommended action: Suggest upgraded plan + add-ons
└─ Next-best-action: "Present Premium with extras"
Agent: "Great! Based on your usage, I have a perfect
recommendation..."
Copilot displays:
├─ Customer's usage metrics
├─ Recommended plan details
├─ Comparison of savings
└─ Available promotions (10% if upgrade today)
[Agent presents recommendations]
Customer: "The Premium plan looks good, and 10% off
sounds great!"
Copilot suggests:
├─ Add-on: Premium Support (93% value match)
└─ Upsell: Extended warranty (67% match)
Result: Upgraded plan + 1 add-on sale
Copilot Summary:
"Customer upgraded from Basic to Premium plan and added
Premium Support. Mentioned promotional discount influenced
decision. Total value: $XXX. Customer satisfied."
Business Impact: Increased revenue, improved satisfaction
Best Practices
Knowledge Base Quality
- Accuracy First - All information must be current and correct
- Comprehensive Coverage - Address common issues and scenarios
- Clear Language - Write for quick scanning, not detailed reading
- Proper Organization - Use tags and metadata effectively
- Regular Updates - Review and update quarterly minimum
- Validation Process - Test before publishing
Agent Copilot Configuration
- Start Simple - Enable basic features first, add complexity gradually
- Queue-Specific - Tailor for each queue's unique needs
- Knowledge Curation - Surface only relevant articles
- Test Thoroughly - Pilot before full deployment
- Monitor Adoption - Track agent usage and feedback
- Continuous Refinement - Improve based on data
Agent Enablement
- Comprehensive Training - Thorough explanation of features
- Live Demonstrations - Show real examples and use cases
- Practice Sessions - Let agents use Copilot before production
- Clear Benefits - Help agents understand time-saving value
- Easy Access to Help - Support for questions and issues
- Celebrate Successes - Share agent stories and improvements
Performance Optimization
- Track Metrics Daily - Monitor AHT, ACW, knowledge usage
- Gather Agent Feedback - Regular surveys and check-ins
- Analyze Usage Patterns - Identify which features are most helpful
- Refine Knowledge - Update articles based on usage data
- Optimize for Your Business - Tailor features to your priorities
- Benchmark Performance - Compare pre/post Copilot metrics
Token Consumption
Agent Copilot requires AI Experience tokens for operation:
- Consumption: 40-60 tokens per user per month
- Factors: Interaction volume, knowledge article access, AI feature usage
- Optimization: Monitor usage and optimize knowledge articles
- Budgeting: Plan tokens based on agent count and deployment scope
Interview Cheat Sheet
| Question | Answer |
|---|---|
| What is Agent Copilot? | Real-time AI assistance for agents during customer interactions |
| What does it do during calls? | Determines intent, suggests next actions, surfaces knowledge, monitors sentiment |
| What about after-call work? | Auto-generates summaries, suggests wrap-up codes, tracks resolution |
| What are AI Skills? | Modular AI capabilities (knowledge, next actions, sentiment, compliance) |
| How does it behave on-queue? | Continuous monitoring and assistance throughout interaction |
| What languages does it support? | 13+ including English, Spanish, French, German, Japanese, Korean, Arabic, Hindi |
| How much time does it save? | ~9 minutes per interaction (5 AHT + 2 ACW + 1.5 hold time) |
| What's the expected improvement? | AHT reduction 5-10%, ACW reduction 2-3 minutes, CSAT improvement 5-15% |
| How is it licensed? | Per-user with 40-60 tokens per user per month |
| Does it work omnichannel? | Yes - voice, chat, email all supported |
| Can you customize it? | Yes via AI Studio to create custom Guides and Skills |
| Where is it configured? | Admin → Contact Center → Agent Assistance → Agent Copilot |
| What knowledge does it need? | Quality knowledge base (Workbench or Fabric) |
| How long until ROI? | 2-4 weeks to see AHT improvements |
| What's most important? | Quality knowledge base and agent training |
Key Takeaways
- Real-Time Assistance - Continuously monitors interactions providing guidance as they happen
- Intelligent Understanding - Determines customer intent and emotional state
- Automatic Documentation - Generates summaries and suggests wrap-up codes
- Knowledge Integration - Surfaces relevant articles without agent search
- Multi-Channel - Works on voice, chat, email, and messaging
- Modular Skills - Combine capabilities for your specific needs
- Proven Results - 5-minute AHT reduction, 1.5-minute hold time reduction
- Adaptive Behavior - Responds to conversation flow and customer emotion
- Global Support - Operates in 13+ languages
- Continuous Learning - Improves recommendations based on outcomes
Additional Resources
Official Documentation
- About Agent Copilot: help.genesys.cloud/articles/about-genesys-agent-copilot/
- Agent Copilot Configuration: help.genesys.cloud/articles/configure-agent-copilot/
- Agent Copilot Deep Dive: genesys.com/blog/post/genesys-cloud-agent-copilot-deep-dive
- Agent Copilot FAQs: help.genesys.cloud/faqs/category/agent-copilot/
Support & Training
- Genesys University: genesys.com/training
- Community Forums: https://community.genesys.com
- Technical Support: https://support.genesys.com
Document Version Info
Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Validated: Current with January-March 2026 releases
Version: 1.0