Customer Experiences with Contact Center AI

Customer Experiences with Contact Center AI - Dialogflow ESCCAIDESGOGoogleGO-CCAIDES2.0<ul> <li>Define what Contact Center AI (CCAI) is and what it can do for contact centers.</li><li>Explain how Dialogflow can be used in contact center applications.</li><li>Describe how natural language understanding (NLU) is used to enable Dialogflow conversations.</li><li>Implement a chat virtual agent using Dialogflow ES.</li><li>Describe how natural language understanding (NLU) is used to enable Dialogflow conversations.</li><li>Describe options for storing parameters and fulfilling user requests.</li><li>Describe how to deploy virtual agents to production.</li><li>Identify best practices for development of virtual agents in Dialogflow ES.</li><li>Identify key aspects, such as security and compliance, in the context of contact centers.</li></ul>Completed GCP Fundamentals or have equivalent experienceThis is a beginner to intermediate course, intended for learners with the following types of roles: <ul> <li>Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows.</li><li>Citizen developers: Creates new business applications for consumption by others using high level development and runtime environments.</li><li>Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.</li></ul>The course includes presentations, demonstrations, and hands-on labs. Module 1: Overview of Contact Center AI <ul> <li>Define what Contact Center AI (CCAI) is and what it can do for contact centers.</li><li>Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI.</li><li>Describe the role each component plays in a CCAI solution.</li></ul>Module 2: Conversational Experiences <ul> <li>List the basic principles of a conversational experience.</li><li>Explain the role of Conversation virtual agents in a conversation experience.</li><li>Articulate how STT (Speech to Text) can determine the quality of a conversation experience.</li><li>Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent.</li><li>Recognize the different NLU (Natural Language Understanding) and NLP (Natural Language Processing) techniques and the role they play on conversation experiences.</li><li>Explain the different elements of a conversation (intents, entities, etc).</li><li>Use sentiment analysis to help with the achievement of a higher-quality conversation experience.</li><li>Improve conversation experiences by choosing different TTS voices (Wavenet vs Standard).</li><li>Modify the speed and pitch of a synthesized voice.</li><li>Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage.</li></ul>Module 3: Fundamentals of Designing Conversations <ul> <li>Identify user roles and their journeys.</li><li>Write personas for virtual agents and users.</li><li>Model user-agent interactions.</li></ul>Module 4: Dialogflow Product Options <ul> <li>Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX).</li><li>Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX.</li><li>Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX.</li><li>List the basic elements of the Dialogflow user interface.</li></ul>Module 5: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>Module 6: Fundamentals of building conversations with Dialogflow ES <ul> <li>List the basic elements of the Dialogflow CX User Interface.</li><li>Build a virtual agent to handle identified user journeys.</li><li>Train the NLU model through the Dialogflow console.</li><li>Define and test intents for a basic agent.</li><li>Train the agent to handle expected and unexpected user scenarios.</li><li>Recognize the different types of entities and when to use them.</li><li>Create entities.</li><li>Define and test entities on a basic agent.</li><li>Implement slot filling using the Dialogflow UI.</li><li>Describe when Mega Agent might be used.</li><li>Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ.</li></ul>Module 7: Maintaining Context in a Conversation <ul> <li>Create follow-up intents.</li><li>Recognize the scenarios in which context should be used.</li><li>Identify the possible statuses of a context (active versus inactive context).</li><li>Implement dialogs using input and output contexts.</li></ul>Module 8: Moving From Chat to Voice Virtual Agent <ul> <li>Describe two ways that the media type changes the conversation.</li><li>Configure the telephony gateway for testing.</li><li>Test a basic voice agent.</li><li>Modify the voice of the agent.</li><li>Show how the different media types can have different responses.</li><li>Consider the modifications needed when moving to production.</li><li>Be aware of the telephony integration for voice in a production environment.</li></ul>Module 9: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>Module 10: Testing and Logging <ul> <li>Use Dialogflow tools for troubleshooting.</li><li>Use Google Cloud tools for debugging your virtual agent.</li><li>Review logs generated by virtual agent activity.</li><li>Recognize ways an audit can be performed.</li></ul>Module 11: Taking Actions with Fulfillment <ul> <li>Characterize the role of fulfillment with respect to Contact Center AI.</li><li>Implement a virtual agent using Dialogflow ES.</li><li>Use Cloud Firestore to store customer data.</li><li>Implement fulfillment using Cloud Functions to read and write Firestore data.</li><li>Describe the use of Apigee for application deployment.</li></ul>Module 12: Integrating Virtual Agents <ul> <li>Describe how to use the Dialogflow API to programmatically create and modify the virtual agent.</li><li>Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN.</li><li>Describe how to replace existing head intent detection on IVRs with Dialogflow intents.</li><li>Describe virtual agent integration with Google Assistant.</li><li>Describe virtual agent integration with messaging platforms.</li><li>Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk).</li><li>Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio).</li><li>Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design.</li><li>Describe how to incorporate IVR features in the virtual agent.</li></ul>Module 13: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>Module 14: Environment Management <ul> <li>Create Draft and Published versions of your virtual agent.</li><li>Create environments where your virtual agent will be published.</li><li>Load a saved version of your virtual agent to Draft.</li><li>Change which version is loaded to an environment.</li></ul>Module 15: Drawing Insights from Recordings with SAF <ul> <li>Analyze audio recordings using the Speech Analytics Framework (SAF).</li></ul>Module 16: Intelligence Assistance for Live Agents <ul> <li>Recognize use cases where Agent Assist adds value.</li><li>Identify, collect and curate documents for knowledge base construction.</li><li>Describe how to set up knowledge bases.</li><li>Describe how FAQ Assist works.</li><li>Describe how Document Assist works.</li><li>Describe how the Agent Assist UI works.</li><li>Describe how Dialogflow Assist works.</li><li>Describe how Smart Reply works.</li><li>Describe how Real-time entity extraction works.</li></ul>Module 17: Compliance and Security <ul> <li>Describe two ways security can be implemented on a CCAI integration.</li><li>Identify current compliance measures and scenarios where compliance is needed.</li></ul>Module 18: Best Practices <ul> <li>Convert pattern matching and decision trees to smart conversational design.</li><li>Recognize situations that require escalation to a human agent.</li><li>Support multiple platforms, devices, languages, and dialects.</li><li>Use Diagflow’s built-in analytics to assess the health of the virtual agent.</li><li>Perform agent validation through the Dialogflow UI.</li><li>Monitor conversations and Agent Assist.</li><li>Institute a DevOps and version control framework for agent development and maintenance.</li><li>Consider enabling spell correction to increase the virtual agent's accuracy.</li></ul>Module 19: Implementation Methodology <ul> <li>Identify the stages of the Google Enterprise Sales Process.</li><li>Describe the Partner role in the Enterprise Sales Process.</li><li>Detail the steps in a Contact Center AI project using Google’s ESP.</li><li>Describe the key activities of the Implementation Phase in ESP.</li><li>Locate and understand how to use Google's support assets for Partners.</li></ul>Module 20: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>The course includes presentations, demonstrations, and hands-on labs. Module 1: Overview of Contact Center AI <ul> <li>Define what Contact Center AI (CCAI) is and what it can do for contact centers.</li><li>Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI.</li><li>Describe the role each component plays in a CCAI solution.</li></ul>Module 2: Conversational Experiences <ul> <li>List the basic principles of a conversational experience.</li><li>Explain the role of Conversation virtual agents in a conversation experience.</li><li>Articulate how STT (Speech to Text) can determine the quality of a conversation experience.</li><li>Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent.</li><li>Recognize the different NLU (Natural Language Understanding) and NLP (Natural Language Processing) techniques and the role they play on conversation experiences.</li><li>Explain the different elements of a conversation (intents, entities, etc).</li><li>Use sentiment analysis to help with the achievement of a higher-quality conversation experience.</li><li>Improve conversation experiences by choosing different TTS voices (Wavenet vs Standard).</li><li>Modify the speed and pitch of a synthesized voice.</li><li>Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage.</li></ul>Module 3: Fundamentals of Designing Conversations <ul> <li>Identify user roles and their journeys.</li><li>Write personas for virtual agents and users.</li><li>Model user-agent interactions.</li></ul>Module 4: Dialogflow Product Options <ul> <li>Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX).</li><li>Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX.</li><li>Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX.</li><li>List the basic elements of the Dialogflow user interface.</li></ul>Module 5: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>Module 6: Fundamentals of building conversations with Dialogflow ES <ul> <li>List the basic elements of the Dialogflow CX User Interface.</li><li>Build a virtual agent to handle identified user journeys.</li><li>Train the NLU model through the Dialogflow console.</li><li>Define and test intents for a basic agent.</li><li>Train the agent to handle expected and unexpected user scenarios.</li><li>Recognize the different types of entities and when to use them.</li><li>Create entities.</li><li>Define and test entities on a basic agent.</li><li>Implement slot filling using the Dialogflow UI.</li><li>Describe when Mega Agent might be used.</li><li>Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ.</li></ul>Module 7: Maintaining Context in a Conversation <ul> <li>Create follow-up intents.</li><li>Recognize the scenarios in which context should be used.</li><li>Identify the possible statuses of a context (active versus inactive context).</li><li>Implement dialogs using input and output contexts.</li></ul>Module 8: Moving From Chat to Voice Virtual Agent <ul> <li>Describe two ways that the media type changes the conversation.</li><li>Configure the telephony gateway for testing.</li><li>Test a basic voice agent.</li><li>Modify the voice of the agent.</li><li>Show how the different media types can have different responses.</li><li>Consider the modifications needed when moving to production.</li><li>Be aware of the telephony integration for voice in a production environment.</li></ul>Module 9: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>Module 10: Testing and Logging <ul> <li>Use Dialogflow tools for troubleshooting.</li><li>Use Google Cloud tools for debugging your virtual agent.</li><li>Review logs generated by virtual agent activity.</li><li>Recognize ways an audit can be performed.</li></ul>Module 11: Taking Actions with Fulfillment <ul> <li>Characterize the role of fulfillment with respect to Contact Center AI.</li><li>Implement a virtual agent using Dialogflow ES.</li><li>Use Cloud Firestore to store customer data.</li><li>Implement fulfillment using Cloud Functions to read and write Firestore data.</li><li>Describe the use of Apigee for application deployment.</li></ul>Module 12: Integrating Virtual Agents <ul> <li>Describe how to use the Dialogflow API to programmatically create and modify the virtual agent.</li><li>Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN.</li><li>Describe how to replace existing head intent detection on IVRs with Dialogflow intents.</li><li>Describe virtual agent integration with Google Assistant.</li><li>Describe virtual agent integration with messaging platforms.</li><li>Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk).</li><li>Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio).</li><li>Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design.</li><li>Describe how to incorporate IVR features in the virtual agent.</li></ul>Module 13: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>Module 14: Environment Management <ul> <li>Create Draft and Published versions of your virtual agent.</li><li>Create environments where your virtual agent will be published.</li><li>Load a saved version of your virtual agent to Draft.</li><li>Change which version is loaded to an environment.</li></ul>Module 15: Drawing Insights from Recordings with SAF <ul> <li>Analyze audio recordings using the Speech Analytics Framework (SAF).</li></ul>Module 16: Intelligence Assistance for Live Agents <ul> <li>Recognize use cases where Agent Assist adds value.</li><li>Identify, collect and curate documents for knowledge base construction.</li><li>Describe how to set up knowledge bases.</li><li>Describe how FAQ Assist works.</li><li>Describe how Document Assist works.</li><li>Describe how the Agent Assist UI works.</li><li>Describe how Dialogflow Assist works.</li><li>Describe how Smart Reply works.</li><li>Describe how Real-time entity extraction works.</li></ul>Module 17: Compliance and Security <ul> <li>Describe two ways security can be implemented on a CCAI integration.</li><li>Identify current compliance measures and scenarios where compliance is needed.</li></ul>Module 18: Best Practices <ul> <li>Convert pattern matching and decision trees to smart conversational design.</li><li>Recognize situations that require escalation to a human agent.</li><li>Support multiple platforms, devices, languages, and dialects.</li><li>Use Diagflow’s built-in analytics to assess the health of the virtual agent.</li><li>Perform agent validation through the Dialogflow UI.</li><li>Monitor conversations and Agent Assist.</li><li>Institute a DevOps and version control framework for agent development and maintenance.</li><li>Consider enabling spell correction to increase the virtual agent's accuracy.</li></ul>Module 19: Implementation Methodology <ul> <li>Identify the stages of the Google Enterprise Sales Process.</li><li>Describe the Partner role in the Enterprise Sales Process.</li><li>Detail the steps in a Contact Center AI project using Google’s ESP.</li><li>Describe the key activities of the Implementation Phase in ESP.</li><li>Locate and understand how to use Google's support assets for Partners.</li></ul>Module 20: Course Review <ul> <li>Review what was covered in the course as relates to the objectives.</li></ul>- Define what Contact Center AI (CCAI) is and what it can do for contact centers. - Explain how Dialogflow can be used in contact center applications. - Describe how natural language understanding (NLU) is used to enable Dialogflow conversations. - Implement a chat virtual agent using Dialogflow ES. - Describe how natural language understanding (NLU) is used to enable Dialogflow conversations. - Describe options for storing parameters and fulfilling user requests. - Describe how to deploy virtual agents to production. - Identify best practices for development of virtual agents in Dialogflow ES. - Identify key aspects, such as security and compliance, in the context of contact centers.Completed GCP Fundamentals or have equivalent experienceThis is a beginner to intermediate course, intended for learners with the following types of roles: - Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows. - Citizen developers: Creates new business applications for consumption by others using high level development and runtime environments. - Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.The course includes presentations, demonstrations, and hands-on labs. Module 1: Overview of Contact Center AI - Define what Contact Center AI (CCAI) is and what it can do for contact centers. - Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI. - Describe the role each component plays in a CCAI solution. Module 2: Conversational Experiences - List the basic principles of a conversational experience. - Explain the role of Conversation virtual agents in a conversation experience. - Articulate how STT (Speech to Text) can determine the quality of a conversation experience. - Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent. - Recognize the different NLU (Natural Language Understanding) and NLP (Natural Language Processing) techniques and the role they play on conversation experiences. - Explain the different elements of a conversation (intents, entities, etc). - Use sentiment analysis to help with the achievement of a higher-quality conversation experience. - Improve conversation experiences by choosing different TTS voices (Wavenet vs Standard). - Modify the speed and pitch of a synthesized voice. - Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage. Module 3: Fundamentals of Designing Conversations - Identify user roles and their journeys. - Write personas for virtual agents and users. - Model user-agent interactions. Module 4: Dialogflow Product Options - Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX). - Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX. - Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX. - List the basic elements of the Dialogflow user interface. Module 5: Course Review - Review what was covered in the course as relates to the objectives. Module 6: Fundamentals of building conversations with Dialogflow ES - List the basic elements of the Dialogflow CX User Interface. - Build a virtual agent to handle identified user journeys. - Train the NLU model through the Dialogflow console. - Define and test intents for a basic agent. - Train the agent to handle expected and unexpected user scenarios. - Recognize the different types of entities and when to use them. - Create entities. - Define and test entities on a basic agent. - Implement slot filling using the Dialogflow UI. - Describe when Mega Agent might be used. - Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ. Module 7: Maintaining Context in a Conversation - Create follow-up intents. - Recognize the scenarios in which context should be used. - Identify the possible statuses of a context (active versus inactive context). - Implement dialogs using input and output contexts. Module 8: Moving From Chat to Voice Virtual Agent - Describe two ways that the media type changes the conversation. - Configure the telephony gateway for testing. - Test a basic voice agent. - Modify the voice of the agent. - Show how the different media types can have different responses. - Consider the modifications needed when moving to production. - Be aware of the telephony integration for voice in a production environment. Module 9: Course Review - Review what was covered in the course as relates to the objectives. Module 10: Testing and Logging - Use Dialogflow tools for troubleshooting. - Use Google Cloud tools for debugging your virtual agent. - Review logs generated by virtual agent activity. - Recognize ways an audit can be performed. Module 11: Taking Actions with Fulfillment - Characterize the role of fulfillment with respect to Contact Center AI. - Implement a virtual agent using Dialogflow ES. - Use Cloud Firestore to store customer data. - Implement fulfillment using Cloud Functions to read and write Firestore data. - Describe the use of Apigee for application deployment. Module 12: Integrating Virtual Agents - Describe how to use the Dialogflow API to programmatically create and modify the virtual agent. - Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN. - Describe how to replace existing head intent detection on IVRs with Dialogflow intents. - Describe virtual agent integration with Google Assistant. - Describe virtual agent integration with messaging platforms. - Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk). - Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio). - Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design. - Describe how to incorporate IVR features in the virtual agent. Module 13: Course Review - Review what was covered in the course as relates to the objectives. Module 14: Environment Management - Create Draft and Published versions of your virtual agent. - Create environments where your virtual agent will be published. - Load a saved version of your virtual agent to Draft. - Change which version is loaded to an environment. Module 15: Drawing Insights from Recordings with SAF - Analyze audio recordings using the Speech Analytics Framework (SAF). Module 16: Intelligence Assistance for Live Agents - Recognize use cases where Agent Assist adds value. - Identify, collect and curate documents for knowledge base construction. - Describe how to set up knowledge bases. - Describe how FAQ Assist works. - Describe how Document Assist works. - Describe how the Agent Assist UI works. - Describe how Dialogflow Assist works. - Describe how Smart Reply works. - Describe how Real-time entity extraction works. Module 17: Compliance and Security - Describe two ways security can be implemented on a CCAI integration. - Identify current compliance measures and scenarios where compliance is needed. Module 18: Best Practices - Convert pattern matching and decision trees to smart conversational design. - Recognize situations that require escalation to a human agent. - Support multiple platforms, devices, languages, and dialects. - Use Diagflow’s built-in analytics to assess the health of the virtual agent. - Perform agent validation through the Dialogflow UI. - Monitor conversations and Agent Assist. - Institute a DevOps and version control framework for agent development and maintenance. - Consider enabling spell correction to increase the virtual agent's accuracy. Module 19: Implementation Methodology - Identify the stages of the Google Enterprise Sales Process. - Describe the Partner role in the Enterprise Sales Process. - Detail the steps in a Contact Center AI project using Google’s ESP. - Describe the key activities of the Implementation Phase in ESP. - Locate and understand how to use Google's support assets for Partners. Module 20: Course Review - Review what was covered in the course as relates to the objectives.The course includes presentations, demonstrations, and hands-on labs. Module 1: Overview of Contact Center AI - Define what Contact Center AI (CCAI) is and what it can do for contact centers. - Identify each component of the CCAI Architecture: Speech Recognition, Dialogflow, Speech Synthesis, Agent Assist, and Insights AI. - Describe the role each component plays in a CCAI solution. Module 2: Conversational Experiences - List the basic principles of a conversational experience. - Explain the role of Conversation virtual agents in a conversation experience. - Articulate how STT (Speech to Text) can determine the quality of a conversation experience. - Demonstrate and test how Speech adaptation can improve the speech recognition accuracy of the agent. - Recognize the different NLU (Natural Language Understanding) and NLP (Natural Language Processing) techniques and the role they play on conversation experiences. - Explain the different elements of a conversation (intents, entities, etc). - Use sentiment analysis to help with the achievement of a higher-quality conversation experience. - Improve conversation experiences by choosing different TTS voices (Wavenet vs Standard). - Modify the speed and pitch of a synthesized voice. - Describe how to leverage SSML to modify the tone and emphasis of a synthesized passage. Module 3: Fundamentals of Designing Conversations - Identify user roles and their journeys. - Write personas for virtual agents and users. - Model user-agent interactions. Module 4: Dialogflow Product Options - Describe two primary differences between Dialogflow Essentials (ES) and Dialogflow Customer Experience (CX). - Identify two design principles for your virtual agent which apply regardless of whether you implement in Dialogflow ES or CX. - Identify two ways your virtual agent implementation changes based on whether you implement in Dialogflow ES or CX. - List the basic elements of the Dialogflow user interface. Module 5: Course Review - Review what was covered in the course as relates to the objectives. Module 6: Fundamentals of building conversations with Dialogflow ES - List the basic elements of the Dialogflow CX User Interface. - Build a virtual agent to handle identified user journeys. - Train the NLU model through the Dialogflow console. - Define and test intents for a basic agent. - Train the agent to handle expected and unexpected user scenarios. - Recognize the different types of entities and when to use them. - Create entities. - Define and test entities on a basic agent. - Implement slot filling using the Dialogflow UI. - Describe when Mega Agent might be used. - Demonstrate how to add access to a knowledge base for your virtual agent to answer customer questions straight from a company FAQ. Module 7: Maintaining Context in a Conversation - Create follow-up intents. - Recognize the scenarios in which context should be used. - Identify the possible statuses of a context (active versus inactive context). - Implement dialogs using input and output contexts. Module 8: Moving From Chat to Voice Virtual Agent - Describe two ways that the media type changes the conversation. - Configure the telephony gateway for testing. - Test a basic voice agent. - Modify the voice of the agent. - Show how the different media types can have different responses. - Consider the modifications needed when moving to production. - Be aware of the telephony integration for voice in a production environment. Module 9: Course Review - Review what was covered in the course as relates to the objectives. Module 10: Testing and Logging - Use Dialogflow tools for troubleshooting. - Use Google Cloud tools for debugging your virtual agent. - Review logs generated by virtual agent activity. - Recognize ways an audit can be performed. Module 11: Taking Actions with Fulfillment - Characterize the role of fulfillment with respect to Contact Center AI. - Implement a virtual agent using Dialogflow ES. - Use Cloud Firestore to store customer data. - Implement fulfillment using Cloud Functions to read and write Firestore data. - Describe the use of Apigee for application deployment. Module 12: Integrating Virtual Agents - Describe how to use the Dialogflow API to programmatically create and modify the virtual agent. - Describe connectivity protocols: gRPC, REST, SIP endpoints, and phone numbers over PSTN. - Describe how to replace existing head intent detection on IVRs with Dialogflow intents. - Describe virtual agent integration with Google Assistant. - Describe virtual agent integration with messaging platforms. - Describe virtual agent integration with CRM platforms (such as Salesforce and Zendesk). - Describe virtual agent integration with enterprise communication platforms (such as Genesys, Avaya, Cisco, and Twilio). - Explain the ability that telephony providers have of identifying the caller and how that can modify the agent design. - Describe how to incorporate IVR features in the virtual agent. Module 13: Course Review - Review what was covered in the course as relates to the objectives. Module 14: Environment Management - Create Draft and Published versions of your virtual agent. - Create environments where your virtual agent will be published. - Load a saved version of your virtual agent to Draft. - Change which version is loaded to an environment. Module 15: Drawing Insights from Recordings with SAF - Analyze audio recordings using the Speech Analytics Framework (SAF). Module 16: Intelligence Assistance for Live Agents - Recognize use cases where Agent Assist adds value. - Identify, collect and curate documents for knowledge base construction. - Describe how to set up knowledge bases. - Describe how FAQ Assist works. - Describe how Document Assist works. - Describe how the Agent Assist UI works. - Describe how Dialogflow Assist works. - Describe how Smart Reply works. - Describe how Real-time entity extraction works. Module 17: Compliance and Security - Describe two ways security can be implemented on a CCAI integration. - Identify current compliance measures and scenarios where compliance is needed. Module 18: Best Practices - Convert pattern matching and decision trees to smart conversational design. - Recognize situations that require escalation to a human agent. - Support multiple platforms, devices, languages, and dialects. - Use Diagflow’s built-in analytics to assess the health of the virtual agent. - Perform agent validation through the Dialogflow UI. - Monitor conversations and Agent Assist. - Institute a DevOps and version control framework for agent development and maintenance. - Consider enabling spell correction to increase the virtual agent's accuracy. Module 19: Implementation Methodology - Identify the stages of the Google Enterprise Sales Process. - Describe the Partner role in the Enterprise Sales Process. - Detail the steps in a Contact Center AI project using Google’s ESP. - Describe the key activities of the Implementation Phase in ESP. - Locate and understand how to use Google's support assets for Partners. Module 20: Course Review - Review what was covered in the course as relates to the objectives.4 jours2495.002600.002950.002600.002600.009020.002695.002090.002090.002695.002090.002600.002640.003445.003170.00