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Agentforce Service Consultant

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Agentforce Service Consultant Study Checklist
  • Platform Administrator
  • Platform App Builder
  • Platform Foundations
  • Platform Developer
  • Platform Administrator II
  • Agentforce Sales Consultant
  • Agentforce Service Consultant
  • Platform Data Architect
  • Platform Development Lifecycle and Deployment Architect
  • Platform Identity and Access Management Architect
  • Platform Integration Architect
  • Platform Sharing and Visibility Architect
  • Heroku Architect
  • B2C Solution Architect
  • Experience Cloud Consultant
  • Agentforce Field Service and Operations Consultant
  • Agentforce Nonprofit Consultant
  • Data 360 Consultant
  • Omnistudio Consultant
  • CRM Analytics and Einstein Discovery Consultant
  • Platform User Experience Designer
  • Platform Strategy Designer
  • B2C Commerce Developer
  • JavaScript Developer
  • Omnistudio Developer
  • Platform Developer II
  • Marketing Cloud Engagement Administrator
  • Marketing Cloud Engagement Specialist
  • Marketing Cloud Engagement Consultant
  • Agentforce Sales Foundations
  • Business Analyst
  • Marketing Cloud Engagement Developer
  • Marketing Cloud Engagement Foundations
  • Agentforce Specialist
  • Agentforce Life Sciences Consultant
  • B2B Commerce Administrator AP
  • B2B Commerce Developer AP
  • Agentforce Consumer Goods AP
  • Agentforce Financial Services AP
  • Agentforce Health AP
  • Agentforce Manufacturing AP
  • MuleSoft Integration Foundations
  • MuleSoft Developer
  • MuleSoft Developer II
  • MuleSoft Platform Integration Architect
  • MuleSoft Platform Architect
  • Tableau Desktop Foundations
  • Tableau Data Analyst
  • Tableau Consultant
  • Tableau Server Administrator
  • Tableau Architect
  • Slack Administrator

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0/254Learned

  • Core components of contact center management
  • Call center metrics versus omnichannel contact center metrics
  • Average handle time components and drivers
  • Speed to answer, service level and abandonment drivers
  • First contact resolution drivers
  • Case resolution time and SLA attainment drivers
  • CSAT versus NPS as service KPIs
  • Customer effort score and effort drivers
  • Case deflection rate drivers
  • Cost per contact and the drivers that lower it
  • Rep occupancy, burnout and retention
  • Efficiency metrics versus customer experience trade-offs
  • Business policy and data definitions behind KPI values
  • AI agents and AI assistance effects on service KPIs
  • Prebuilt analytics for contact center KPIs and AI impact
  • Uncovering the root business challenge behind a requested solution
  • Needs, wants and expectations when prioritizing client requirements
  • Business case and case for change for a service transformation
  • Disconnected channels and siloed customer data as a service challenge
  • Handoff challenges between frontline support and technical teams
  • Identifying and categorizing risks in a service implementation
  • Scope creep and triple-constraint trade-offs
  • Benefits, risks and ROI goals of a self-service portal
  • Benefits and risks of introducing AI agents into customer service
  • Data readiness as a risk to AI-driven service outcomes
  • Consultant activities across the customer engagement lifecycle
  • Identifying and managing service implementation stakeholders
  • Customer-centric discovery steps for a service engagement
  • Gathering service requirements across support stakeholders
  • Defining measurable success criteria at the start of an engagement
  • Building a business case for an Agentforce Service investment
  • Prioritizing service requirements into a right-sized scope
  • Statement of work and change control for implementation scope
  • Co-creating a phased change roadmap with stakeholders
  • LEVERS model for leading change in a service rollout
  • Phased, pilot and big-bang rollout approaches for service teams
  • Deployment timing and release cadence for contact center operations
  • Sandbox types for user acceptance testing and training environments
  • Launch communication plan by rollout stage
  • Super users, early adopters and executive sponsors as adoption champions
  • Core elements of a user training strategy
  • Role-based training plans for service users
  • Choosing training delivery methods for audience, scale and budget
  • In-app guidance and custom Help Menu for learning in the flow of work
  • Assigning and tracking training completion
  • Post-go-live support model and ongoing refresher training
  • Measuring and recognizing user adoption after launch
  • Staged launch and testing of an Agentforce Service agent
  • Rollout and enablement strategy for AI-assisted rep features
  • Scoping which legacy service data to migrate
  • Object load sequence for service data migrations
  • Choosing a data loading tool for a service migration
  • Suspending validation rules, triggers, and automation during large loads
  • Deferring sharing calculations during large data loads
  • Test loads and validation before a production data cutover
  • Planning a Classic to Lightning Knowledge migration
  • Assessing and improving service data quality before and after migration
  • Data quality readiness for Agentforce grounding
  • Data governance framework roles and components
  • Data governance controls for regulated service data
  • Large data volume risks and up-front data model planning
  • Data skew in service data models
  • Data tiering with archiving and external objects for large data volumes
  • Human-centric principles for the service rep experience
  • Lightning console navigation for multitasking service reps
  • Dynamic Forms and Dynamic Actions on case record pages
  • Surfacing customer and related-record context for reps
  • Knowledge access in the rep workspace
  • Macros versus quick text for repetitive rep tasks
  • Guided screen flows and Actions and Recommendations for reps
  • Einstein Case Classification and Article Recommendations for reps
  • Generative reply drafts and conversation summaries for reps
  • Service Assistant service plans and record summaries
  • Unified voice and messaging workspace for reps in Agentforce Contact Center
  • Standard features versus custom or AgentExchange solutions
  • Customer journey mapping to shape service experience requirements
  • Channel mix design for customer contact preferences and issue urgency
  • Tiered service model across self-service, AI agents and service reps
  • Experience Cloud help center versus authenticated customer portal
  • Agentforce Service agent on an Experience Cloud self-service site
  • Default versus specialized Agentforce Service agent templates
  • Escalation from Agentforce Service agents to service reps
  • Context continuity and personalization across customer channels
  • Transparency with customers about AI agents and recorded conversations
  • Language coverage for AI agents serving multilingual customers
  • Case record visibility across support teams and tiers
  • External customer case access on Experience Cloud sites
  • Knowledge article visibility with data categories and channels
  • Agent user object and record access under least privilege
  • Service agent run-as context by customer authentication level
  • Authentication patterns for service agents by deployment channel
  • Customer verification and scoping of private agent actions
  • Einstein Trust Layer protections for Agentforce Service agents
  • Field-level security versus Shield Platform Encryption for sensitive data
  • Sensitive data protection in messaging sessions and voice calls
  • Messaging channel selection under HIPAA and regulated-data requirements
  • Consent and communication preference tracking for service customers
  • Audit trail and field history options for service compliance
  • Fulfilling customer data deletion and portability requests
  • Knowledge-Centered Service core principles and benefits
  • KCS Solve Loop steps during case handling
  • KCS Evolve Loop and article quality audits
  • Knowledge article publishing cycle and archiving
  • Article Validation Status versus publication status
  • Updating published articles and article version history
  • Knowledge record types, page layouts and article fields
  • Data category groups, hierarchies and visibility
  • Knowledge access for readers, authors and KCS publishers
  • Article approval processes and publishing automation
  • Multilingual knowledge base and article translation
  • Knowledge base planning for a new implementation
  • Importing existing content and moving to Lightning Knowledge
  • KCS program adoption: leadership, coaching and agent buy-in
  • Article channels for internal, customer, partner and public audiences
  • Separating internal notes from customer-facing article content
  • Knowledge component on case pages in the console
  • Sharing Knowledge articles with customers from a case
  • Suggested Articles versus Einstein Article Recommendations
  • Knowledge article access for site members and guest users
  • Topics and data categories for articles in Experience Cloud sites
  • Knowledge search tuning with synonyms and promoted terms
  • Measuring article usage and helpfulness
  • Einstein Knowledge Creation for drafting articles from cases
  • Grounding Agentforce service agents in Knowledge articles

Given business process requirements, recommend the appropriate approach to intake channels and their design

0/6

  • Intake channel mix for customer and business requirements
  • Email-to-Case intake design: variants and routing addresses
  • Web-to-Case capture, spam protection and limits
  • Web-to-Case versus Experience Cloud self-service case submission
  • Enhanced Chat for website and mobile app intake
  • Apple Messages for Business journey planning and entry points

Given a set of business goals, discuss the use cases and functionality for proposed interaction channels

0/9

  • Third-party messaging app channels for a customer base
  • Capability differences across enhanced messaging channels
  • Messaging components for interactive service conversations
  • Rep transfer, conferencing and supervisor help in messaging sessions
  • Proactive outbound messaging and automated notifications
  • Voice channel capabilities for reps and supervisors
  • Social media channels for customer service
  • Unified customer conversation history across channels
  • Bring Your Own Channel for Messaging versus BYOC for CCaaS

Assess the design considerations and best practices when configuring an interaction channel solution

0/11

Standard versus enhanced messaging channels

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Messaging setup prerequisites and licensing

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Omni-Channel routing configurations and work prioritization

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Skills-based versus queue-based Omni-Channel routing

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Queue-based routing versus Omni-Channel flows for messaging channels

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Tab-based versus status-based Omni-Channel capacity models

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Presence configurations and after-conversation work time

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Quick text versus reply recommendations for messaging reps

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Messaging consent and sensitive data protection

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Omni Supervisor for real-time channel monitoring

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Reporting on messaging channels and sessions

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Given a set of business requirements, recommend an appropriate use for AI agents in the customer service process

0/5

  • Assessing which service work suits an autonomous AI agent
  • Planning and prioritizing AI agent use cases against business goals
  • Choosing among Agentforce agent types for a service need
  • When to recommend Einstein Bots instead of an AI agent
  • Combining an Einstein bot and an AI agent in one conversation path

Explain agentic service capabilities, use cases, and configuration processes

0/12

  • Agentforce Service Agent versus Einstein Bots capabilities
  • Service agent setup prerequisites, versioning and activation
  • Configuring subagents, instructions and actions for a service agent
  • Grounding service agent answers with Agentforce Data Library
  • Context variables and customer verification in agent conversations
  • Channel deployment options for an Agentforce Service agent
  • Agent connections and adaptive response formats across channels
  • Inbound and outbound Omni-Channel flows for AI agent routing
  • Escalation subagent and handoff from AI agent to service reps
  • Agentforce Service agent on email and incoming case processing
  • Testing and reviewing service agent behavior
  • Migrating from Einstein Bots with the Create Agent from Bot tool
  • Stages of the case resolution process
  • Support processes and case record types for distinct case lifecycles
  • Email-to-Case versus Web-to-Case for automated case creation
  • Case sharing model and taking ownership of queued cases
  • Case feed quick actions and case comments
  • Case Timeline and Case History for tracking case activity
  • Case teams and swarming for collaborative case resolution
  • Parent and child case hierarchies for grouping or splitting work
  • Case merge and Enhanced Case Merge for duplicate cases
  • Case closure design and closed status behaviour
  • Customer feedback surveys versus evaluation forms for case quality
  • Aligning case lifecycle design to service KPIs
  • Customer Service template self-service site built on Knowledge
  • Case Deflection component paired with the Contact Support Form
  • Guided self-service flows and account actions that avoid a case
  • Grounding a customer-facing agent in Knowledge for deflection
  • Fallback paths from site self-service to a service rep
  • Capturing deflection feedback with Deflection Metrics and Deflection Tracking
  • Reporting on site case deflection effectiveness
  • Matching deflection requirements to site, Knowledge or Agentforce features
  • Entitlement management building blocks and what each represents
  • Choosing an entitlement management model for support eligibility
  • Service contract line items and entitlement templates for product coverage
  • Assets in entitlement coverage and case lookups
  • Automatically applying entitlements to web, email and site cases
  • SLA policy entry and exit criteria on support records
  • Milestone recurrence types for repeating SLA steps
  • Milestone success, warning and violation actions
  • Auto-completing milestones and controlling manual completion
  • Milestone tracker and rep visibility into SLA countdowns
  • Pausing milestone timers on stopped cases
  • Business hours and support holidays for multi-region support
  • Business hours hierarchy across milestones, SLA policies and cases
  • Entitlement process versioning versus simplified SLA setup
  • Entitlement and service contract access for Experience Cloud users
  • Reporting on entitlement and milestone compliance
  • Case assignment rules versus Omni-Channel routing
  • Automatic case assignment rules for Experience Cloud site cases
  • Auto-response rules versus other automated customer notifications
  • Escalation rules versus entitlement milestone actions
  • Flow Builder versus built-in case rules for service automation
  • Einstein Case Classification versus Einstein Case Routing
  • Assistive AI for service reps versus autonomous service agents
  • Agentic versus deterministic workflow automation for service processes
  • Incident Management versus case hierarchies for service disruptions
  • Matching contact center KPIs to executive, supervisor and rep dashboards
  • Lightning reports versus CRM Analytics, Data 360 and Tableau for service reporting
  • Selecting report types for service data requirements
  • Report formats, groupings and filters for service data
  • Dashboard components and filters for service KPIs
  • Prebuilt service reports and dashboards for contact center supervisors
  • Case report views for queues, case teams, comments and parent cases
  • Measuring first-contact resolution with case reports
  • Case aging and case field history reporting
  • Trending service metrics over time
  • Dynamic dashboards and running user for stakeholder data visibility
  • Report and dashboard subscriptions for stakeholder updates
  • Embedding dashboards and KPIs in the Service Console for reps
  • Command Center for Service real-time monitoring versus historical reporting
  • Contact center KPI reporting for Salesforce Voice
  • Service Analytics templated app in CRM Analytics
  • Service Intelligence dashboards for supervisors and reps
  • Service Intelligence licensing, data prerequisites and refresh
  • Service Insights versus Service Intelligence
  • Einstein Conversation Mining for identifying contact reasons
  • Service AI Adoption and Analytics dashboard
  • Agent Analytics in Agentforce Studio for AI agent performance
  • Agent Analytics effectiveness, usage and health metrics
  • Data integration considerations and tool selection
  • Integration user licensing and authentication best practices
  • Salesforce Connect virtualization versus replicating data between systems
  • Salesforce Connect adapter selection for external and cross-org data
  • Writable external objects and external data source identity type
  • API-led connectivity and MuleSoft Anypoint connectors
  • Unifying customer data across multiple orgs and external systems
  • Flow options for reaching third-party systems from service processes
  • External Services for declarative calls to external APIs
  • Platform events versus Outbound Messaging for notifying external systems
  • Agentforce Contact Center versus Partner Contact Center telephony integration
  • Voice-enabled Agentforce agents and IVR call flow placement
  • Slack integration for service collaboration and case swarming
  • Customer Service Incident Management integrations with third-party tools
  • Agentforce Commerce and Agentforce Service integration for order-related service
  • Grounding service agents in Salesforce, external and unstructured data
  • Bringing third-party knowledge sources into service search and AI
  • MCP for Agentforce to connect service agents with third-party tools
  • AppExchange packaged solutions versus custom-built integrations

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  1. Agentforce Service Consultant Checklist
  3. Intake and Interaction Channels

Tab-based versus status-based Omni-Channel capacity models

Explainer

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Practice Question

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Next conceptPresence configurations and after-conversation work time

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  1. Agentforce Service Consultant Checklist
  3. Intake and Interaction Channels
Tab-based versus status-based Omni-Channel capacity models

Explainer

Learn More

Practice Question

Keep going

Next conceptPresence configurations and after-conversation work time

Explainer

Omni-Channel tracks how much of each service rep's capacity is used up by the work assigned to them. It measures this in one of two ways: tab-based, which counts the console tabs open in the rep's Omni-Channel session, or status-based, which checks the status of the rep's work items. The choice decides whether closing a tab or logging out frees capacity, or whether work keeps counting until it is completed or reassigned.
Core information
  • Omni-Channel determines service rep capacity in one of two ways: Tab-Based or Status-Based. The capacity model is chosen on each service channel.
More details and nuances
  • The Capacity Model field appears on service channels only after Enable Status-Based Capacity Model is selected in Omni-Channel Settings. After that, each service channel is set to either the tab-based or the status-based model.

Explainer

Omni-Channel tracks how much of each service rep's capacity is used up by the work assigned to them. It measures this in one of two ways: tab-based, which counts the console tabs open in the rep's Omni-Channel session, or status-based, which checks the status of the rep's work items. The choice decides whether closing a tab or logging out frees capacity, or whether work keeps counting until it is completed or reassigned.
Core information
  • Omni-Channel determines service rep capacity in one of two ways: Tab-Based or Status-Based. The capacity model is chosen on each service channel.
More details and nuances
  • The Capacity Model field appears on service channels only after Enable Status-Based Capacity Model is selected in Omni-Channel Settings. After that, each service channel is set to either the tab-based or the status-based model.