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

Performance Metrics

Agent Copilot users have seen results including:

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

Next-Best-Action Skills

Wrap-Up Code Skills

Sentiment & De-Escalation Skills

Compliance & Risk Skills

Custom Business Skills

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

Chat/Email

Outbound Call

Implementation Guide

Step 1: Prerequisites & Planning

  1. Ensure Premium Edition activated
  2. Confirm sufficient AI tokens available (40-60 per user)
  3. Audit knowledge base quality and coverage
  4. Identify queues for initial deployment
  5. Assess agent readiness and training needs
  6. Plan change management approach

Step 2: Knowledge Base Setup

  1. Review/create knowledge articles in Knowledge Workbench
  2. Ensure articles are accurate and current
  3. Add metadata and keywords for searchability
  4. Organize by topic and queue
  5. Set article access permissions
  6. Test knowledge base connectivity

Step 3: Enable Agent Copilot

  1. Navigate to Admin → Contact Center → Agent Assistance
  2. Select "Agent Copilot" → Enable
  3. Choose knowledge source (Knowledge Workbench v2 or Fabric)
  4. Configure recommendation parameters
  5. Set recommendation types to display
  6. Choose display behavior (auto-popup vs. agent-triggered)

Step 4: Configure by Queue

  1. Create queue-specific Copilot settings
  2. Define which AI Skills are active per queue
  3. Configure knowledge sources
  4. Set wrap-up code matching rules
  5. Establish sentiment thresholds
  6. Test queue-specific behavior

Step 5: Agent Training

  1. Conduct overview training on Copilot interface
  2. Demonstrate real-time knowledge suggestions
  3. Practice reviewing and applying recommendations
  4. Explain wrap-up code suggestions
  5. Cover sentiment alerts and de-escalation
  6. Q&A and feedback

Step 6: Phased Rollout

  1. Deploy to pilot queue first
  2. Monitor closely for 1-2 weeks
  3. Gather agent and supervisor feedback
  4. Optimize based on learnings
  5. Expand to additional queues
  6. Scale to full deployment

Step 7: Ongoing Monitoring

  1. Review Agent Copilot dashboard daily
  2. Track key metrics (AHT reduction, token usage)
  3. Monitor agent adoption rates
  4. Gather continuous feedback
  5. Refine knowledge articles based on usage
  6. 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

Agent Copilot Configuration

Agent Enablement

Performance Optimization

Token Consumption

Agent Copilot requires AI Experience tokens for operation:

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

Additional Resources

Official Documentation

Support & Training

Document Version Info

Last Updated: March 2026
Source: Genesys PureCloud Official Documentation
Validated: Current with January-March 2026 releases
Version: 1.0

Revision #1
Created 13 March 2026 19:30:03 by Cesar Gzz
Updated 14 March 2026 19:35:03 by Cesar Gzz