Virtual Agent Flows (Agentic)

Study Notes 
 
 
 
 Topic 
 Description 
 
 
 
 
 Virtual Agent Flows 
 AI-powered autonomous agent workflows for handling customer interactions 
 
 
 Also Known As 
 Agentic Flows, AI Agent Automation, Autonomous Agents 
 
 
 Purpose 
 Automate routine interactions without human agent intervention 
 
 
 Activation 
 Requires Premium edition and Genesys Cloud CX module 
 
 
 Benefit 
 24/7 availability, reduced operational costs, faster resolution for routine issues 
 
 
 
 
 Navigation 
 Admin → Architect → Flows → Virtual Agent Flows
OR
Admin → Contact Center → Automation → Virtual Agent Configuration 
 
 Virtual Agent Flows Overview 
 Virtual Agent Flows are AI-powered autonomous agents that handle customer interactions without human agent involvement. They use natural language processing, machine learning, and conversation design to understand customer intent and resolve issues independently or escalate when necessary. 
 Key Capabilities 
 
 Natural language understanding - Comprehends customer intent without rigid menu systems 
 Conversational AI - Engages in natural dialogue with customers 
 Intent recognition - Identifies customer goals and required actions 
 Multi-turn conversations - Maintains context across conversation turns 
 Seamless escalation - Routes to human agents when needed 
 Learning capability - Improves responses based on interactions 
 Omnichannel support - Operates across voice, chat, email, messaging 
 Integration with systems - Accesses backend systems for real-time data 
 
 How It Works 
 
 Customer initiates contact (voice, chat, email, etc.) 
 Virtual agent receives interaction 
 NLP analyzes customer intent 
 Agent determines if it can handle the request 
 Agent engages in conversation to gather information 
 Agent performs action or retrieves information 
 Agent provides resolution or escalates to human 
 System learns from interaction for improvement 
 
 
 Edition & Module Requirements 
 
 
 
 Requirement 
 Details 
 
 
 
 
 Minimum Edition 
 Premium Edition required 
 
 
 Module 
 Genesys Cloud CX Automation or Virtual Agent add-on 
 
 
 License Type 
 Virtual agent license (separate from agent seats) 
 
 
 Setup 
 Configuration via Architect flows 
 
 
 Integration 
 Backend system APIs for transactions 
 
 
 
 
 Study Notes - Virtual Agent Capabilities 
 
 
 
 Capability 
 Description 
 Use Case 
 
 
 
 
 Self-Service Resolution 
 Handle routine requests independently 
 Password resets, account balance checks 
 
 
 Information Retrieval 
 Access and provide customer data 
 Account status, order tracking 
 
 
 Transaction Processing 
 Execute system actions safely 
 Payment processing, appointment booking 
 
 
 Sentiment Analysis 
 Monitor customer emotion during interaction 
 De-escalate, escalate when frustrated 
 
 
 Context Preservation 
 Maintain conversation history 
 Multi-turn dialogues, follow-ups 
 
 
 Proactive Outreach 
 Initiate contacts with customers 
 Payment reminders, service updates 
 
 
 Knowledge Integration 
 Access knowledge base articles 
 Answer FAQs, provide guidance 
 
 
 Escalation Logic 
 Intelligent routing to human agents 
 Complex issues, preference-based 
 
 
 Multi-language Support 
 Communicate in multiple languages 
 Global customer base support 
 
 
 Compliance Monitoring 
 Ensure interactions meet regulations 
 Legal requirements, disclosures 
 
 
 
 
 Implementation Guide 
 Step 1: Assessment & Strategy 
 
 Identify suitable use cases (routine interactions) 
 Map customer journeys to automate 
 Determine escalation scenarios 
 Audit backend system integrations needed 
 Estimate volume and ROI 
 Plan change management approach 
 
 Step 2: Virtual Agent Design 
 
 Navigate to Admin → Architect → Flows 
 Create new Virtual Agent Flow 
 Define entry points (voice, chat, email) 
 Design conversation scenarios 
 Configure intent recognition 
 Set up context variables 
 Define escalation paths 
 
 Step 3: Conversation Design 
 
 Create dialogue trees for common scenarios 
 Write natural conversation language 
 Define follow-up questions for clarity 
 Build error handling for misunderstandings 
 Create fallback responses 
 Add personality/brand voice guidelines 
 Test conversation flows 
 
 Step 4: System Integration 
 
 Connect to knowledge base 
 Integrate with CRM/customer systems 
 Set up payment processing (if needed) 
 Configure appointment systems 
 Enable account system access 
 Set up notification systems 
 Test all integrations 
 
 Step 5: Testing & Validation 
 
 Conduct conversation scenario testing 
 Test integration endpoints 
 Validate escalation triggers 
 Test error handling 
 Performance and load testing 
 Security validation 
 Compliance review 
 
 Step 6: Deployment & Monitoring 
 
 Deploy to production queues 
 Monitor interaction success rates 
 Track escalation patterns 
 Gather customer feedback 
 Optimize based on metrics 
 Refine conversations 
 Continuous improvement 
 
 
 How to Implement 
 
 
 
 Phase 
 Description 
 Timeline 
 
 
 
 
 Planning 
 Identify use cases and design approach 
 Week 1-2 
 
 
 Design 
 Create conversation flows and intents 
 Week 2-4 
 
 
 Development 
 Build flows and integrations 
 Week 4-6 
 
 
 Testing 
 Validate all scenarios and systems 
 Week 6-7 
 
 
 Pilot 
 Deploy to subset of traffic 
 Week 7-8 
 
 
 Rollout 
 Full deployment to production 
 Week 8-9 
 
 
 Optimization 
 Monitor and improve performance 
 Ongoing 
 
 
 
 
 Virtual Agent Flow Architecture 
 Customer Contact
 ↓
Virtual Agent Entry Point
├── Voice (IVR-to-Agent transition)
├── Chat Bot Interface
├── Email Automation
└── Messaging Platform
 ↓
NLP & Intent Recognition Engine
├── Language Understanding
├── Entity Extraction
├── Intent Classification
└── Confidence Scoring
 ↓
Conversation Management
├── Context Preservation
├── State Management
├── History Tracking
└── Multi-turn Handling
 ↓
Action Determination
├── Self-Service Resolution Check
├── Required Information Gathering
├── System Access Requirement
└── Escalation Threshold Assessment
 ↓
Execution Path
├── Self-Service Path
│ ├── Database Query
│ ├── Transaction Processing
│ └── Information Retrieval
├── Guided Path
│ ├── Ask Clarifying Questions
│ ├── Provide Information
│ └── Collect Input
└── Escalation Path
 ├── Human Agent Queue
 ├── Context Transfer
 └── Warm Handoff
 ↓
Response Generation
├── Natural Language Generation
├── Personality/Brand Voice
└── Accessibility Compliance
 ↓
Customer Receives Response
 ↓
Interaction Logged & Analyzed
 
 
 Common Virtual Agent Use Cases 
 Use Case 1: Self-Service Password Reset 
 Customer: "I forgot my password"
 ↓
Virtual Agent:
├── Understands: Password reset request
├── Verification: "Let me verify your identity
│ with security questions"
├── Questions: "What's your account email?
│ What was your first pet's name?"
├── Action: Reset password, send new one
└── Confirmation: "Your password has been reset.
 Check your email. Need anything else?"
 ↓
Resolution Rate: 95%+ (self-service)
 
 Use Case 2: Order Status Inquiry 
 Customer: "Where's my order?"
 ↓
Virtual Agent:
├── Retrieves: Customer account
├── Questions: "What's your order number?"
├── Lookup: Accesses order system
├── Tracking: "Your order is out for delivery
 today. You can track it here: [link]"
└── Offer: "Can I help with anything else?"
 ↓
Resolution Rate: 90%+ (self-service)
 
 Use Case 3: Appointment Scheduling 
 Customer: "I need to schedule a service call"
 ↓
Virtual Agent:
├── Understands: Appointment request
├── Gathers: "What service do you need?
 What days work for you?"
├── Checks: Availability in scheduling system
├── Confirms: "Got you down for March 15
 at 10 AM. You'll get a reminder."
└── Summary: Sends appointment confirmation
 ↓
Resolution Rate: 85%+ (self-service)
 
 Use Case 4: Payment Processing 
 Customer: "I want to pay my bill"
 ↓
Virtual Agent:
├── Verification: Confirms identity
├── Info: "Your balance is $150.00.
 How much would you like to pay?"
├── Collection: Securely gathers payment info
├── Processing: Processes payment
├── Confirmation: "Payment of $100 processed.
 New balance: $50. Receipt sent."
 ↓
Resolution Rate: 92%+ (self-service)
 
 Use Case 5: Escalation to Human Agent 
 Customer: "This is too complicated"
 ↓
Virtual Agent:
├── Sentiment: Detects frustration
├── Assessment: Issue beyond agent capability
├── Escalation: "I understand this is complex.
 Let me connect you with a specialist
 who can help better."
├── Transfer: Warm handoff with context
└── Human Agent Receives: Full interaction history
 ↓
Human Agent: "Hi Sarah, I see you were
 working on a billing dispute. Let me
 take it from here..."
 
 
 Virtual Agent Conversation Flow Design 
 Simple Conversation Example 
 INTENT: Account Balance Check

Virtual Agent: "Hi! What can I help with?"
User: "I need my account balance"
Virtual Agent: [Detects intent]
 "I can help with that. Let me verify
 who you are. What's your account email?"
User: "john.smith@email.com"
Virtual Agent: [Verifies identity]
 "Thanks. What's your account PIN?"
User: "5432"
Virtual Agent: [Confirms identity]
 "Perfect. Your current balance is $2,150.
 Would you like to make a payment?"
User: "No, that's all I needed"
Virtual Agent: "Great! Is there anything
 else I can help with today?"
User: "No thanks"
Virtual Agent: "Thanks for contacting us.
 Have a great day!"
[Interaction Complete - Self-Service Resolution]
 
 Complex Conversation with Escalation 
 INTENT: Billing Dispute

Virtual Agent: "Hi! What's going on today?"
User: "I was charged twice for the same order"
Virtual Agent: [Detects: Billing dispute]
 "I'm sorry that happened. I can help
 investigate. What's your order number?"
User: "ORD-123456"
Virtual Agent: [Looks up order]
 "I see the issue. You're right - there
 are two charges. Let me credit one back."
 [Processes refund]
 "The refund of $99.99 has been processed.
 You should see it in 2-3 business days."
User: "But I need it today - I'm short on funds"
Virtual Agent: [Sentiment: Frustrated]
 "I understand this is urgent. While I
 can't speed up the refund, I can connect
 you with our billing specialist who might
 have other options. One moment..."
 [Initiates escalation]
Virtual Agent: "Sarah from billing is ready
 to help. I'm connecting you now..."
 ↓
[Warm Handoff to Human Agent]
Human Agent: "Hi, I see the double charge
 issue. Let's explore what we can do..."
 
 
 Multi-Channel Virtual Agent Flows 
 Voice Channel (IVR-like) 
 Customer Calls
 ↓
Virtual Agent Answers (Voice Synthesized)
 ↓
Conversation via Speech Recognition/TTS
├── "Welcome. Say what you need help with."
├── Customer speaks: "I want my balance"
├── Agent understands: Account inquiry
└── Agent responds with balance information
 ↓
[Natural voice conversation, not menu-based]
 
 Chat Channel 
 Customer Opens Chat
 ↓
Virtual Agent Responds (Text-based)
 ↓
Conversation via Text
├── "Hi there! How can I help?"
├── Customer: "Where's my order?"
├── Agent: [Provides tracking info with links]
└── Conversation continues naturally
 
 Email Channel 
 Customer Sends Email
 ↓
Virtual Agent Processes (Async)
 ↓
Email Response Generated
├── Understands customer question
├── Retrieves relevant information
├── Drafts personalized response
└── Sends reply with solution/escalation
 
 Messaging Apps (SMS, WhatsApp) 
 Customer Sends SMS/WhatsApp
 ↓
Virtual Agent Receives
 ↓
Concise Text-based Conversation
├── "Hi! What do you need?"
├── Customer: "Bill amount?"
├── Agent: "Your bill is $150"
└── Natural conversational flow
 
 
 Escalation Scenarios & Logic 
 Escalation Decision Tree:

Virtual Agent Receives Request
 ↓
Can handle self-service?
├─ YES: Process request
│ └─ Return result to customer
│ ├─ Success → End interaction
│ └─ Failure → Escalate
│
└─ NO: Determine escalation type
 ├─ Capability-based
 │ └─ "I'm not able to handle that.
 │ Let me connect you with someone
 │ who can help."
 │
 ├─ Complexity-based
 │ └─ "This needs expert analysis.
 │ Connecting you with a specialist..."
 │
 ├─ Sentiment-based
 │ └─ Customer frustrated
 │ "I understand your frustration.
 │ Let me get you a specialist..."
 │
 ├─ Preference-based
 │ └─ Customer requests human
 │ "Of course, I'll connect you
 │ with an agent right now."
 │
 └─ Business-based
 └─ High-value customer
 "This deserves personalized
 attention. One moment..."
 ↓
 Queue to Appropriate Agent Group
 ↓
 Warm Handoff with Full Context
 ↓
 Human Agent Assists Customer
 
 
 Virtual Agent Performance Metrics 
 Interaction Success 
 
 
 
 Metric 
 Target 
 Purpose 
 
 
 
 
 Self-Service Resolution Rate 
 >75% 
 Measure autonomous handling 
 
 
 Successful Escalations 
 >95% 
 Ensure proper handoffs 
 
 
 Escalation Rate 
 <25% 
 Control human agent workload 
 
 
 First-Contact Resolution 
 >80% 
 Avoid repeat contacts 
 
 
 Customer Satisfaction 
 >80% 
 Measure customer experience 
 
 
 
 Operational Efficiency 
 
 
 
 Metric 
 Target 
 Purpose 
 
 
 
 
 Automated Interactions 
 >60% of volume 
 Measure automation impact 
 
 
 Avg Interaction Time 
 Baseline -30% 
 Track efficiency gains 
 
 
 Cost Per Interaction 
 -40-60% vs agent 
 Measure ROI 
 
 
 24/7 Availability 
 100% 
 Measure uptime 
 
 
 Peak Hour Handling 
 100% capacity 
 Measure scalability 
 
 
 
 Quality Metrics 
 
 
 
 Metric 
 Target 
 Purpose 
 
 
 
 
 Intent Recognition Accuracy 
 >90% 
 Verify understanding 
 
 
 Conversation Completion 
 >85% 
 Measure flow success 
 
 
 Compliance Adherence 
 100% 
 Ensure regulatory met 
 
 
 Sentiment Stability 
 No escalation due to tone 
 Measure conversational quality 
 
 
 Knowledge Article Accuracy 
 100% 
 Ensure correct information 
 
 
 
 
 Real Flow Scenario: 24/7 Self-Service 
 Scenario: Customer calls at 2 AM with password reset

Timeline:
2:00 AM - Customer Calls
 ↓
Virtual Agent Answers (No wait!)
 "Welcome to ABC Company. I'm your
 virtual assistant. How can I help?"
 
Customer: "I can't log in"
 ↓
Virtual Agent - Intent Recognition
 Identified: "Password Reset Request"
 Confidence: 98%
 ↓
Virtual Agent - Verification
 "I can help you reset your password.
 Let me verify your identity first.
 What email is on your account?"
 
Customer: "john@email.com"
 ↓
Virtual Agent - System Access
 [Queries customer database]
 [Retrieves security questions]
 ↓
Virtual Agent - Authentication
 "What's your mother's maiden name?"
 
Customer: "Smith"
 ↓
Virtual Agent - Verification Complete
 [Identity confirmed]
 [Generates temporary password]
 [Sends via SMS]
 ↓
Virtual Agent - Confirmation
 "Perfect! I've sent a temporary
 password to your phone. Use that to
 log in, then set a new password.
 Need anything else?"
 
Customer: "No, thanks"
 ↓
Virtual Agent - Closing
 "Great! You're all set. Have a
 good night!"
 ↓
2:03 AM - Issue Resolved
Resolution: Self-service (Zero human involvement)
Cost: ~$0.15 per interaction
Customer Satisfaction: Immediate resolution
 
 
 Best Practices 
 Conversation Design 
 
 Natural language - Avoid robotic responses, use conversational tone 
 Context awareness - Remember previous statements in multi-turn dialogue 
 Graceful degradation - Handle misunderstandings without frustration 
 Clear escalation - Explain why escalating to human when needed 
 Personality - Match brand voice (professional, friendly, etc.) 
 Conciseness - Keep responses brief and to the point 
 
 Intent Recognition 
 
 Comprehensive training - Train model with many user utterance variations 
 Entity extraction - Accurately identify account numbers, dates, etc. 
 Confidence thresholds - Only process high-confidence intents 
 Fallback handling - Ask clarifying questions for low confidence 
 Regular updates - Continuously improve model with real interactions 
 Error capture - Log misunderstandings for model improvement 
 
 Escalation Strategy 
 
 Clear criteria - Define when escalation is triggered 
 Warm handoffs - Provide full context to human agent 
 Preference respect - Allow customer to request human anytime 
 Sentiment triggers - Escalate frustrated customers automatically 
 Complexity assessment - Escalate issues beyond agent capability 
 Smooth transition - Make escalation seamless and professional 
 
 System Integration 
 
 Secure access - Validate all backend system connections 
 Transaction safety - Implement verification for financial transactions 
 Data protection - Encrypt sensitive customer information 
 Audit trails - Log all agent actions for compliance 
 Error handling - Gracefully handle system unavailability 
 Real-time sync - Keep agent data current with systems 
 
 Continuous Improvement 
 
 Monitor metrics - Track resolution rates and satisfaction daily 
 Gather feedback - Survey customers about virtual agent experience 
 Analyze conversations - Review failed interactions for improvement 
 Update conversations - Refine dialogue based on real interactions 
 A/B testing - Test different conversation approaches 
 Quarterly reviews - Assess overall performance and ROI 
 
 
 Common Implementation Scenarios 
 Scenario 1: Small Business (Simple Use Case) 
 Configuration:
├── Single virtual agent
├── 2-3 use cases (balance, order status, hours)
├── Basic escalation to agent queue
└── Email & chat channels

Setup Time: 4-6 weeks
Expected Automation: 60-70% of routine inquiries
ROI Timeline: 3-4 months
Cost Savings: $2,000-5,000/month
 
 Scenario 2: Enterprise (Multi-Use Case) 
 Configuration:
├── Multiple specialized virtual agents
├── 10+ use cases (billing, support, sales, etc.)
├── Intelligent routing based on intent
├── All channels (voice, chat, email, messaging)
├── Advanced escalation logic
└── Integration with CRM, billing, ticketing systems

Setup Time: 12-16 weeks
Expected Automation: 50-70% of volume
ROI Timeline: 2-3 months
Cost Savings: $50,000-100,000+/month
 
 Scenario 3: 24/7 Global Support 
 Configuration:
├── Multi-language virtual agents (5+ languages)
├── Timezone-aware routing
├── Global escalation queues
├── Multiple backend system integrations
├── Compliance per region

Setup Time: 16-20 weeks
Expected Automation: 40-60% of global volume
ROI Timeline: 4-6 months
Cost Savings: $100,000-250,000+/month
 
 
 Troubleshooting Guide 
 
 
 
 Issue 
 Cause 
 Resolution 
 
 
 
 
 Low resolution rate 
 Poor intent recognition 
 Improve NLP training with more examples 
 
 
 High escalation rate 
 Agent not handling enough cases 
 Expand conversation design and capabilities 
 
 
 Customer frustration 
 Misunderstood requests 
 Add better error handling and fallbacks 
 
 
 Slow response time 
 System latency 
 Optimize integrations and database queries 
 
 
 Integration failures 
 Backend system down 
 Implement fallback flows and escalation 
 
 
 Incorrect information 
 Stale data in systems 
 Ensure real-time system synchronization 
 
 
 Compliance issues 
 Missing required language 
 Add required disclosures and messaging 
 
 
 Security concerns 
 Data exposed 
 Review access controls and encryption 
 
 
 Poor escalation 
 Missing context 
 Ensure full interaction history transfer 
 
 
 Low adoption 
 Customers prefer agents 
 Improve virtual agent experience and marketing 
 
 
 
 
 Virtual Agent vs. Traditional IVR 
 
 
 
 Feature 
 Virtual Agent 
 Traditional IVR 
 
 
 
 
 User Experience 
 Natural conversation 
 Menu-driven 
 
 
 Understanding 
 NLP-based intent 
 Keyword matching 
 
 
 Conversation Type 
 Multi-turn dialogue 
 Sequential selections 
 
 
 Error Handling 
 Contextual recovery 
 Repeat menu 
 
 
 Personalization 
 Personalized responses 
 Generic options 
 
 
 Flexibility 
 Handles variations 
 Follows fixed paths 
 
 
 Resolution Rate 
 70-80%+ 
 40-60% 
 
 
 Learning 
 Improves with data 
 Static 
 
 
 Cost 
 Medium-High 
 Low 
 
 
 Customer Satisfaction 
 High 
 Medium 
 
 
 Setup Time 
 8-16 weeks 
 2-4 weeks 
 
 
 
 
 Interview Cheat Sheet 
 
 
 
 Question 
 Answer 
 
 
 
 
 What are virtual agent flows? 
 AI-powered autonomous agents that handle customer interactions without humans 
 
 
 What are they also called? 
 Agentic flows, autonomous agents, AI automation 
 
 
 What edition is required? 
 Premium edition with Genesys Cloud CX Automation module 
 
 
 What technology do they use? 
 Natural language processing, machine learning, conversation management 
 
 
 What channels do they support? 
 Voice, chat, email, SMS, WhatsApp, messaging apps 
 
 
 What can they handle? 
 Routine requests: password resets, order tracking, payments, scheduling 
 
 
 When do they escalate? 
 Complex issues, customer preference, sentiment triggers, capability limits 
 
 
 What's the expected automation rate? 
 50-70% of routine interactions 
 
 
 What's the ROI timeline? 
 2-4 months to see significant cost savings 
 
 
 What's most important for success? 
 Quality conversation design and NLP training 
 
 
 How do they improve over time? 
 Machine learning from interactions, feedback, and updates 
 
 
 What about security? 
 Secure authentication, encrypted data, audit trails 
 
 
 Can customers always reach humans? 
 Yes, escalation available anytime on demand 
 
 
 What's the biggest benefit? 
 24/7 availability at 60-80% cost reduction 
 
 
 What's the biggest challenge? 
 Complex conversation design and integration setup 
 
 
 
 
 Key Takeaways 
 
 Autonomous Operation - Virtual agents handle interactions without human involvement 
 AI-Powered - Uses NLP and machine learning for natural conversations 
 Cost Reduction - 60-80% lower cost than human agents for routine interactions 
 24/7 Availability - Provide support outside business hours automatically 
 Omnichannel - Work across voice, chat, email, and messaging channels 
 Intelligent Escalation - Routes to humans when needed seamlessly 
 Continuous Learning - Improves recommendations and handling over time 
 Significant Automation - Can handle 50-70% of routine inquiries 
 Customer Preference - Allows escalation to human anytime 
 Quick ROI - 2-4 months to see measurable cost and efficiency improvements 
 
 
 Migration Path from Traditional IVR 
 Phase 1: Planning (Weeks 1-2) 
 ├── Audit current IVR flows
├── Identify suitable use cases
├── Design new virtual agent conversations
├── Plan escalation logic
└── Estimate volume and ROI
 
 Phase 2: Development (Weeks 3-6) 
 ├── Build virtual agent flows
├── Create conversation scenarios
├── Configure NLP and intents
├── Integrate backend systems
└── Set up escalation routing
 
 Phase 3: Testing (Weeks 6-8) 
 ├── Conduct conversation testing
├── Validate integrations
├── Test escalation paths
├── Performance load testing
└── Security validation
 
 Phase 4: Pilot (Weeks 8-10) 
 ├── Deploy to test traffic
├── Monitor success rates
├── Gather customer feedback
├── Collect metric baseline
└── Optimize based on learnings
 
 Phase 5: Rollout (Weeks 10-12) 
 ├── Redirect production traffic
├── Disable old IVR
├── Monitor closely
├── Provide agent support
└── Scale up gradually
 
 Phase 6: Optimization (Ongoing) 
 ├── Monitor performance daily
├── Improve conversation design
├── Update intents based on data
├── Gather ongoing feedback
└── Continuous improvement
 
 
 Advanced: Custom Agent Configurations 
 Multi-Agent Orchestration 
 Contact Arrives
 ↓
Main Virtual Agent
├── Understands intent
├── Routes to specialized agent
│ ├── Billing Agent
│ ├── Support Agent
│ ├── Sales Agent
│ └── Technical Agent
├── Specialized agent handles interaction
└── Escalates if needed
 
 Hybrid Agent Approach 
 Virtual Agent + Human
├── Virtual agent starts interaction
├── Gathers information
├── Handles simple part
├── Transfers to human for complex part
└── Virtual agent confirms resolution
 
 Proactive Agent 
 System Trigger
├── "Bill due in 3 days"
├── Virtual agent proactively contacts customer
├── "Your payment is due March 15. Pay now?"
├── Processes payment if requested
└── Sends confirmation
 
 
 Additional Resources 
 Official Documentation Links 
 
 Genesys Cloud Virtual Agent Guide: https://help.genesys.com/genesyscloud/current/en-us/VirtualAgent.html 
 Architect Flows: https://help.genesys.com/genesyscloud/current/en-us/ArchitectFlows.html 
 NLP & Intent Configuration: https://help.genesys.com/genesyscloud/current/en-us/NLPConfiguration.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