AI Studio & AI Guides Genesys PureCloud AI Studio & AI Guides Documentation Study Notes Topic Description AI Studio Centralized workbench for building, managing, and deploying AI-powered experiences AI Guides No-code feature that converts business instructions into Virtual Agents using natural language Purpose Enable business users to create intelligent virtual agents without coding expertise Licensing Requires Genesys Cloud AI Experience tokens (metered pricing) Innovation Level Level 4 agentic AI - semi-autonomous with defined boundaries and guardrails Navigation Admin → AI Studio OR Admin → Contact Center → Automation → AI Studio AI Studio Overview Genesys Cloud AI Studio provides a centralized workbench to build, manage and deploy AI experiences like Virtual Agents and Agent Copilots. It serves as the command center for organizations to create next-generation AI-powered customer experiences with governance, control, and scalability. Key Capabilities Natural language editor with no-code and low-code tools for users without technical know-how Unified environment for designing AI agents across all channels Built-in governance and compliance controls AI Guides that allow business teams to create Virtual Agents that respond intelligently to customer context and adapt their behavior dynamically within conversations Customizable summaries for interactions Integration with existing Architect Virtual Agent flows Version control and deployment management Performance analytics and monitoring Strategic Positioning AI Studio represents a leap from simple execution to intelligent problem-solving - Level 4 where semi-autonomous systems are configured around specific objectives using reasoning, planning and memory to figure out how best to accomplish goals while still operating within clearly defined boundaries to meet compliance and policy requirements Edition & Module Requirements Requirement Details Minimum Edition Genesys Cloud CX 1, CX 2, CX 3, or CX 4 license Licensing Model AI Experience tokens (metered, usage-based pricing) Permissions Role-based AI Studio permissions required Additional Access to Virtual Agent module for full functionality Study Notes - AI Studio Components Component Purpose User Type AI Guides Create Virtual Agents from natural language prompts Business users, CX teams Guide Editor Build and refine guide instructions and logic All user levels Virtual Agent Integration Deploy guides to Virtual Agent flows Technical users Customizable Summaries Shape interaction summaries to business needs Admins, supervisors Governance Controls Ensure compliance and policy adherence Admins, compliance Performance Dashboard Monitor guide usage and effectiveness Managers, analysts API Access Programmatic guide and agent management Developers AI Guides Overview Genesys Cloud AI Guides enables business users to create AI-powered Virtual Agents through natural language instructions without coding, facilitating conversational flows that mirror customer journeys, adapt dynamically to customer context, and combine structured logic with AI capabilities How AI Guides Work Business user describes goal in plain language or uploads process documentation AI Guides use large language models (LLMs)-based natural language processing to interpret user prompts and documents, generating complete agentic flows including intents, slots and dialog logic System generates draft virtual agent with conversation flow User reviews, edits, and customizes the generated guide AI executable instructions produced by AI Guides are fully editable within Genesys Cloud, allowing users to update messages, logic and backend integrations before publishing Guide is published and connected to Virtual Agent Virtual Agent uses guide to handle customer interactions Key Features Natural Language, No Code Required - easily build or refine virtual agents using plain language or existing documentation with no coding skills needed Build Once, Deploy Anywhere - design experiences once and deploy them across Genesys Cloud Virtual Agent and Copilots, and more to maintain consistency and reduce duplication of effort Enterprise-Grade Collaboration - seamlessly connect front, middle and back-office systems to execute tasks, automate workflows and deliver measurable business outcomes Guardrails Built In - implement configurable, testable safety controls to help enable increased accuracy, appropriate tone and policy compliance to support responsible adoption of agentic AI Knowledge integration - guides can use either knowledge Workbench v2 knowledge bases or knowledge fabric configurations to answer customer questions at any point in a conversation Implementation Guide Step 1: Assessment & Planning Identify suitable use cases for AI Guides Document business processes and customer journeys Gather process documentation or playbooks Define success metrics for each guide Plan escalation scenarios Assess team readiness for AI automation Plan change management approach Step 2: Licensing & Setup Ensure organization has Genesys Cloud CX license (CX 1, CX 2, CX 3, or CX 4) Purchase Genesys Cloud AI Experience tokens Add necessary AI Studio permissions Set up role-based access controls Configure Virtual Agent if not already enabled Test integration with backend systems Establish governance policies Step 3: Guide Creation Navigate to AI Studio Create a guide using an AI prompt, convert a process document into a guide, or create from scratch by starting with a blank guide Describe goal in natural language or upload documentation Review AI-generated guide structure Edit guide instructions and customize flow Add variables and data integrations Configure escalation paths Step 4: Testing & Refinement Preview guide behavior in test environment Author preview before publish - preview real knowledge responses during Guide configuration to confirm accuracy and behavior Test with sample customer scenarios Verify escalation triggers work correctly Validate data integrations Test across all supported channels Gather feedback from SMEs Step 5: Publishing & Deployment Publish guide to Virtual Agent Connect the guide to Virtual Agent flows in Architect Assign to production queues Monitor initial interactions closely Validate customer experience Adjust guide parameters based on feedback Scale to additional queues as needed Step 6: Monitoring & Optimization Monitor guide usage metrics daily Track customer satisfaction and resolution rates Review escalation patterns Analyze customer feedback Refine guide instructions based on data A/B test different guide variations Update regularly based on learnings How to Implement Phase Description Timeline Planning Identify use cases, document processes, assess readiness Week 1-2 Setup Activate licenses, configure permissions, enable integrations Week 2-3 Creation Build guides from prompts or documents, test Week 3-5 Testing Validate behavior, test scenarios, refine Week 5-6 Pilot Deploy to select queues, monitor closely Week 6-7 Rollout Expand to production, scale across organization Week 7-8 Optimization Monitor, analyze, improve continuously Ongoing AI Studio & AI Guides Architecture AI Studio Centralized Workbench ┌─────────────────────────────────────────┐ │ AI Studio Command Center │ ├─────────────────────────────────────────┤ │ │ │ Guide Creation Interface │ │ ├─ Natural Language Prompt Input │ │ ├─ Document Upload & Conversion │ │ └─ Blank Guide Builder │ │ │ │ AI Generation Engine │ │ ├─ LLM Processing │ │ ├─ Intent Detection │ │ ├─ Slot Identification │ │ └─ Dialog Logic Generation │ │ │ │ Guide Editor & Customization │ │ ├─ Instruction Editing │ │ ├─ Variable Management │ │ ├─ Logic Configuration │ │ └─ Integration Setup │ │ │ │ Governance & Controls │ │ ├─ Built-in Guardrails │ │ ├─ Compliance Checking │ │ ├─ Tone Configuration │ │ └─ Brand Alignment Rules │ │ │ │ Publishing & Deployment │ │ ├─ Version Control │ │ ├─ Publish to Virtual Agent │ │ ├─ Queue Assignment │ │ └─ Channel Distribution │ │ │ │ Analytics & Monitoring │ │ ├─ Usage Dashboards │ │ ├─ Performance Metrics │ │ ├─ Escalation Tracking │ │ └─ Feedback Collection │ │ │ │ System Integration │ │ ├─ Architect Virtual Agent │ │ ├─ Knowledge Management │ │ ├─ Backend Data Systems │ │ └─ Customer Data Platforms │ │ │ └─────────────────────────────────────────┘ ↓ Virtual Agent Deployment ↓ Customer Interactions AI Guides Use Cases & Examples Use Case 1: Order Status & Tracking Business Process Documentation: 1. Collect order number from customer 2. Look up order in system 3. Provide tracking information 4. Offer additional help options 5. Close or escalate as needed AI Guide Generated: Utterances: ├─ "Where's my order?" ├─ "Track my package" ├─ "Order status" └─ "When will my order arrive?" Intents: ├─ Track Order ├─ Delivery Status └─ Tracking Number Dialog Flow: ├─ Ask for order number ├─ Query order database ├─ Provide shipping status ├─ Offer further assistance └─ Escalate if needed Resolution Rate: 85-90% (self-service) Use Case 2: Password Reset Process Uploaded Process Document: "Follow these steps for password reset: 1. Verify customer identity with security questions 2. Confirm email address on file 3. Send password reset link 4. Confirm reset completed 5. Offer additional support" AI Guide Generated: Intents: ├─ Password Reset Request ├─ Account Access Issue └─ Security Verification Dialog: ├─ "I'll help you reset your password" ├─ "Let me verify who you are" ├─ Customer answers security questions ├─ System confirms identity ├─ "Check your email for reset link" ├─ "Did you successfully reset?" └─ Provide follow-up support Resolution Rate: 92-95% (self-service) Use Case 3: Appointment Scheduling Prompt Input: "Create a guide for customers to book service appointments. Must: - Ask for service type - Show available dates/times - Confirm appointment - Send confirmation" AI Guide Generated: Intents: ├─ Schedule Appointment ├─ Change Appointment └─ Cancel Appointment Dialog: ├─ "What service do you need?" ├─ "When works best for you?" ├─ Show available slots ├─ "Let me confirm..." ├─ Send calendar confirmation ├─ Offer reschedule option Resolution Rate: 88-92% (self-service) Use Case 4: Billing Question & Payment Process Flow: "Customers with billing questions: 1. Verify account 2. Explain charges 3. Offer payment options 4. Process if customer agrees 5. Send receipt/confirmation" AI Guide Generated: Intents: ├─ Check Bill Amount ├─ Explain Charges ├─ Make Payment └─ Dispute Charge Dialog: ├─ Confirm customer identity ├─ "Let me look up your account" ├─ Display current balance ├─ "Would you like to pay?" ├─ Secure payment processing ├─ Send receipt and confirmation ├─ Escalate disputes Resolution Rate: 80-85% (self-service) Modular Guides Within Virtual Agents AI Guides are designed to create modular bot flows that can be combined within a single virtual agent - each guide typically addresses a specific use case like order tracking, password reset or appointment booking, and these flows can be added to your virtual agent's overall configuration Multi-Guide Architecture Example Virtual Agent: Customer Service Bot ├─ Guide 1: Order Tracking │ ├─ Intents: Track order, delivery status │ ├─ Resolution: Order lookup + tracking info │ └─ Escalation: Complex shipment issues │ ├─ Guide 2: Account Management │ ├─ Intents: Password reset, update info │ ├─ Resolution: Self-service account changes │ └─ Escalation: Security concerns │ ├─ Guide 3: Billing & Payments │ ├─ Intents: Check bill, make payment │ ├─ Resolution: Payment processing │ └─ Escalation: Billing disputes │ └─ Guide 4: Appointment Booking ├─ Intents: Schedule, reschedule, cancel ├─ Resolution: Appointment management └─ Escalation: Complex scheduling needs Router (AI determines which guide needed): Customer: "Where's my order?" → Route to Guide 1: Order Tracking → Resolve: Self-service tracking info Customer: "I want to schedule a service" → Route to Guide 4: Appointment Booking → Resolve: Appointment confirmation Customer: "I have a billing question" → Route to Guide 3: Billing & Payments → Resolve or escalate accordingly Knowledge Integration with AI Guides In a future release, Genesys Cloud will allow AI Guides to use knowledge articles to answer customer questions while continuing through a defined process - guides can answer customer questions using approved knowledge content at any point in a conversation and then continue the task without losing context Knowledge-Aware Guide Benefits Before Knowledge Integration: Customer: "What's your return policy?" Guide: "Let me connect you with an agent who can answer policy questions" → Escalation (unnecessary) After Knowledge Integration: Customer: "What's your return policy?" Guide: [Accesses knowledge base] "Here's our return policy... Now, back to your order tracking, I see your item was delivered..." → Continues conversation naturally → No escalation needed Configuration Options Guide authors can choose to inherit knowledge from the connected Virtual Agent or select a Guide-specific Knowledge source Flexible knowledge sourcing: Use Virtual Agent's knowledge base Configure guide-specific knowledge source Support for knowledge Workbench v2 Support for knowledge fabric configurations Real Flow Scenario: AI Guide in Action Customer Interaction Example: Customer Calls: "I need to reset my password" ↓ Virtual Agent Answers (AI Guide: Account Management) "Hi! I'm here to help. To reset your password, I'll need to verify your identity. What's your email address on file?" ↓ Customer: "john.smith@email.com" ↓ Guide Verifies: Customer email matches account ↓ Guide: "Thanks. What was your first pet's name?" ↓ Customer: "Fluffy" ↓ Guide Confirms: Security answer matches ↓ Guide: "Perfect! I've sent a password reset link to your email. Please check your inbox and click the link to set a new password." ↓ Customer: "Got it, thanks!" ↓ Guide: "You're welcome. Is there anything else I can help with today?" ↓ Customer: "No, that's all" ↓ Guide: "Have a great day!" ↓ Interaction Complete: ├─ Resolution: Self-service password reset ├─ Tokens Used: 1-2 per interaction ├─ Customer Satisfaction: High (immediate help) └─ Cost: ~$0.05-0.15 per interaction Licensing & Token Pricing AI Experience Token Model Organizations need Genesys Cloud AI Experience tokens to use AI Studio, with tokens based pricing model to monitor feature usage and consumption Token Consumption AI Guides consume tokens for each interaction session and for each guide created Pricing Considerations Per-interaction tokens - Consumed when customers interact with guides Per-guide tokens - Consumed when creating new guides Metered model - Pay for what you use Flexibility - Tokens can scale up or down based on demand Multiple LLM support - AI Studio is compatible with proprietary, open source and Amazon Bedrock large language models (LLMs) and advanced frontier models from companies such as OpenAI, Anthropic and Google Token Consumption Example Small Organization: ├─ 3 AI Guides created (3 guides × tokens) ├─ 500 customer interactions/month (500 × tokens) ├─ Monthly token usage: ~750 tokens └─ Estimated cost: $XX-XXX/month Mid-Market Organization: ├─ 10 AI Guides created (10 guides × tokens) ├─ 5,000 customer interactions/month (5,000 × tokens) ├─ Monthly token usage: ~5,100 tokens └─ Estimated cost: $XXX-XXXX/month Enterprise Organization: ├─ 50 AI Guides created (50 guides × tokens) ├─ 50,000 customer interactions/month (50,000 × tokens) ├─ Monthly token usage: ~50,100 tokens └─ Estimated cost: $XXXX-XXXXX/month Best Practices Guide Design Clear intent - Define specific goal for each guide Natural language - Write instructions as you would for an employee Process documentation - Use existing playbooks and procedures Modular approach - Create focused guides for specific tasks Escalation paths - Define clear handoff scenarios Testing - Always test with real scenarios before production Iteration - Guides improve with updates and refinement Guardrails & Governance Implement configurable, testable safety controls to help enable increased accuracy, appropriate tone and policy compliance to support responsible adoption of agentic AI Set boundaries for agent autonomy Define tone and brand voice Establish compliance requirements Monitor sensitive operations Implement audit trails Regular compliance reviews Knowledge Management Author preview before publish - preview real knowledge responses during Guide configuration to confirm accuracy and behavior Keep knowledge articles current Ensure accurate information Update for policy changes Test knowledge accuracy Monitor article usage Gather feedback from guides using knowledge Performance Optimization Monitor token usage closely Track guide effectiveness metrics Analyze escalation reasons Gather customer feedback Refine guides based on data A/B test different approaches Regular guide audits and updates Common Implementation Scenarios Scenario 1: Quick Start (Small Team) Timeline: 2-3 weeks Setup: ├─ 2-3 simple guides ├─ Basic integration (order lookup, FAQ) ├─ Limited escalation paths └─ Single channel deployment Expected Results: ├─ 60-70% automation of routine inquiries ├─ Quick implementation, minimal training ├─ Fast ROI (4-6 weeks) └─ Monthly token usage: ~300-500 Scenario 2: Comprehensive (Mid-Market) Timeline: 6-8 weeks Setup: ├─ 8-12 specialized guides ├─ Multiple backend integrations ├─ Complex escalation logic ├─ Omnichannel deployment └─ Knowledge base integration Expected Results: ├─ 50-65% automation of customer volume ├─ Significant cost savings ├─ 8-12 week ROI └─ Monthly token usage: ~3,000-5,000 Scenario 3: Enterprise (Full Scale) Timeline: 12-16 weeks Setup: ├─ 30-50+ specialized guides ├─ Full system integration ├─ Advanced routing and logic ├─ Multi-language support ├─ Comprehensive knowledge integration └─ Global channel deployment Expected Results: ├─ 40-60% automation of global volume ├─ Major cost reduction ├─ Continuous optimization ├─ 6-10 week ROI └─ Monthly token usage: ~20,000-50,000+ Troubleshooting Guide Issue Cause Resolution Guide doesn't understand customer intent Insufficient training variations Add more example utterances to training data Escalations happening too frequently Over-strict guardrails or incomplete logic Expand guide capabilities and relax thresholds Inaccurate information from knowledge Stale knowledge articles Update knowledge base and preview before publish Slow response time Token consumption or system latency Optimize guide logic and integrations Integration failures Backend system connectivity issues Verify API connections and data endpoints Guides not deployed to queues Missing Virtual Agent configuration Check Architect flow configuration and deployment Customer dissatisfaction Poor conversation quality Refine guide instructions and tone Token overages Guides consuming more than expected Analyze usage patterns and optimize interactions Editing feels clunky Unfamiliar with new guide model Review updated guide model documentation Knowledge not accessible in guides Knowledge not properly configured Configure knowledge source and test access AI Guides vs. Traditional Bot Flows Feature AI Guides Traditional Flows Creation Method Natural language prompts Manual design Skill Required Business knowledge Technical/coding Speed to Deploy Days to weeks Weeks to months Iteration Time Minutes to hours Hours to days Intent Recognition AI-powered, flexible Rule-based, rigid Conversation Quality Natural, contextual Scripted, menu-driven Maintenance AI learns from data Manual updates Integration Automatic via AI Manual configuration Cost to Build Low (no developers) High (developer time) Scaling Easy, rapid deployment Time-consuming Real-World Timeline Example Week 1-2: Planning & Assessment Day 1-3: Kickoff and planning ├─ Identify 3-5 use cases ├─ Gather process documentation └─ Assess team readiness Day 4-10: Documentation review ├─ Refine process flows ├─ Identify integration needs ├─ Plan escalation scenarios Day 11-14: Setup and licensing ├─ Purchase AI Experience tokens ├─ Configure permissions └─ Set up integrations Week 3-4: Guide Creation Day 15-20: First guide creation ├─ Upload process documentation ├─ Review AI-generated guide ├─ Customize and refine └─ Connect to Virtual Agent Day 21-28: Additional guides ├─ Create guides 2-4 ├─ Refine based on feedback ├─ Configure escalation logic └─ Integration testing Week 5-6: Testing & Refinement Day 29-35: Comprehensive testing ├─ Test all guide scenarios ├─ Validate integrations ├─ Test escalation paths └─ Gather feedback Day 36-42: Optimization ├─ Refine guide instructions ├─ Adjust confidence thresholds ├─ Optimize knowledge integration └─ Performance tuning Week 7-8: Deployment Day 43-49: Pilot deployment ├─ Deploy to pilot queue ├─ Monitor closely ├─ Gather customer feedback └─ Make adjustments Day 50-56: Full rollout ├─ Expand to all queues ├─ Monitor metrics ├─ Provide agent support └─ Celebrate success Interview Cheat Sheet Question Answer What is AI Studio? Centralized workbench for building, managing, and deploying AI experiences What is an AI Guide? No-code feature that converts business instructions into Virtual Agents using AI What licensing is required? Genesys Cloud CX license + AI Experience tokens How do AI Guides work? Users describe goals in plain language, AI generates guide structure, user customizes Can non-technical users create guides? Yes, AI Guides are designed for business users without coding skills What level of agentic AI is this? Level 4 - semi-autonomous with defined boundaries and guardrails How long to create a guide? Minutes to hours depending on complexity Can guides be combined? Yes, multiple guides can be modular components in one Virtual Agent How are guides customized? Fully editable - users can change messages, logic, and integrations What about knowledge integration? Guides can access knowledge articles to answer questions within conversation What's the pricing model? Token-based - consumption tracked per interaction and per guide created Can guides handle escalation? Yes, with configurable escalation paths to human agents What channels do guides support? All Genesys channels - voice, chat, email, messaging, etc. How do guides improve over time? AI learns from interactions; user refines guides based on metrics What's the expected ROI timeline? 4-12 weeks depending on implementation scope Key Takeaways No-Code Creation - Natural language, no code required - easily build or refine virtual agents using plain language or existing documentation with no coding skills needed Intelligent Automation - AI-powered guides intelligently handle customer conversations with reasoning and planning Enterprise Control - Guardrails built in - implement configurable, testable safety controls to help enable increased accuracy, appropriate tone and policy compliance Rapid Deployment - Create and deploy guides in days, not months Modular Design - Build once, deploy anywhere across virtual agents, copilots and more to maintain consistency and reduce duplication of effort Token-Based Pricing - Pay only for what you use with flexible scaling Knowledge Integration - Guides can answer customer questions using approved knowledge content during any step of a process Business User Friendly - Designed for CX teams, not IT/developers Continuous Learning - Guides improve as they handle more interactions Compliance Ready - Built-in governance and audit capabilities Customizable Summaries (Additional AI Studio Feature) Customizable Summaries allows admins to shape interaction summaries to fit their business needs, enhancing consistency, compliance, and operation efficiency across human and AI agent interactions Use Cases Tailor summaries to specific business requirements Ensure compliance with regulatory standards Align summaries with operational priorities Support multiple languages and regions Customize for different teams and use cases Getting Started Checklist Pre-Implementation Assess current contact center operations Identify 3-5 suitable use cases Gather process documentation and playbooks Determine success metrics for each guide Assess team readiness for AI automation Plan change management approach Licensing & Setup Purchase Genesys Cloud AI Experience tokens Assign AI Studio permissions to team Configure role-based access controls Enable Virtual Agent module Set up backend system integrations Test knowledge base connectivity Guide Development Create first AI Guide from process document Test guide functionality thoroughly Customize instructions and logic Configure escalation paths Connect to Virtual Agent Deploy to pilot queue Monitoring & Optimization Monitor guide performance daily Track customer satisfaction metrics Analyze escalation patterns Refine guides based on data Scale to additional queues Plan continuous improvements Additional Resources Official Documentation Links AI Studio Overview: help.genesys.cloud/articles/about-ai-studio/ AI Guides Overview: help.genesys.cloud/articles/ai-guides-overview/ Knowledge Integration: help.genesys.cloud/announcements/knowledge-integration-for-ai-guides/ AI Studio Permissions: help.genesys.cloud/articles/ai-studio-permissions/ Support Contacts Genesys Sales: sales@genesys.com Genesys Support: https://support.genesys.com AI Services Team: ai-services@genesys.com Community Forums: https://community.genesys.com Document Version Info Last Updated: March 2026 Source: Genesys PureCloud Official Documentation (help.genesys.cloud) Validated: Current with January-March 2026 releases Version: 1.0