Agentforce Specialist Study Guide
- 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
- Marketing Cloud Account Engagement Specialist
- Marketing Cloud Next 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 Communications AP
- Agentforce Consumer Goods AP
- Agentforce Financial Services AP
- Agentforce Health AP
- Agentforce Manufacturing AP
- Public Sector Solutions AP
- Revenue Cloud Consultant
- 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 Next Consultant
- Tableau Architect
- Slack Administrator
- Slack Consultant
- Slack Developer
Checklist progressProgress
0/204Learned
Checklist progressProgress
0/204Learned
Given business requirements, identify when it's appropriate to use Prompt Builder.
0/5
Identify access controls governing prompt templates
0/5
Identify the considerations for using a prompt template type such as field generation and flex types.
0/4
Given a scenario, identify the appropriate grounding technique.
0/11
- CRM merge fields for prompt grounding
- Flow versus merge fields for prompt grounding
- Apex merge fields versus Flow for prompt grounding
- Knowledge RAG grounding in prompt templates
- Data 360 retriever grounding in Prompt Builder
- Combining merge fields with retrieval grounding
- Web grounding for real-time external data
- File input grounding in Prompt Builder
- Record field merge fields versus Record Snapshots
- Flow and Apex input compatibility with prompt templates
- Retriever prefilters for scoping search results
Explain the process for creating, activating, and executing prompt templates.
0/5
Explain how to implement best practices for writing effective prompts.
0/10
- Prompt template anatomy and design
- Merge fields versus hardcoded values in prompt text
- Grounded-response instructions in prompt templates
- Context engineering versus prompt engineering
- Context clash, confusion, and poisoning
- Consistent writing style and plain-language wording in prompts
- Separating instructions from context and input data in prompt templates
- Restricting LLM output to the expected content type
- Choosing zero-shot, few-shot, or chain-of-thought prompting
- Diagnosing and fixing weak prompt template output
Identify the security and privacy features of the Trust Layer
0/8
- Einstein Trust Layer architecture
- Secure data retrieval for prompt grounding
- Pattern-based and field-based LLM data masking
- Prompt defense system policies
- Zero-data retention with third-party LLMs
- Toxicity detection on generated responses
- Generative AI audit trail and feedback data
- LLM data masking for agents versus embedded generative AI features
Explain how to manage and prevent specific models from being accessed
0/5
- Purpose of Agentforce Data Library
- Data Library source types
- Knowledge grounding with a Data Library
- Data Library setup versus runtime
- Data Library setup considerations
- Assigning a Data Library to an agent
- Default versus additional Data Libraries
- Custom retrievers in a Data Library
- Data Library versus advanced RAG setup
- File-based data libraries and Intelligent Context processing
- Retrieval augmented generation in Data 360
- Chunking sources for AI retrieval
- Search indexes for chunked Data 360 content
- Vector versus hybrid search indexes
- Retrievers versus search indexes
- Retriever creation and activation for grounding
- Ensemble retrievers in Data 360
- Knowledge retrieval troubleshooting for agents
- Easy Setup versus Advanced Setup for search indexes
- Choosing a chunking strategy for the content type
- Max token and overlap token settings for chunk size
- Prepend fields to add context to chunks
- Enriched indexing of content chunks
- Default retrievers versus custom retrievers
- Individual retriever filters and returned fields
- Retriever citations for verifying grounded responses
- Agentforce agent building blocks
- Atlas reasoning engine request handling
- Subagents versus actions
- Action chaining and subagent transitions
- Agent Script as the Agentforce Builder language
- Agent Script block types
- Next-generation authoring of Agent Script
- Final response validation for Service agents
- Hybrid reasoning components in Agent Script
- Benefits of hybrid reasoning
- Agent Script in Canvas View
- Agent Script in Script View
- Canvas versus Script View
- Before and after reasoning in Agent Script
- Topic filters for request gating
- Action filters for business-rule gating
- Input variables versus hardcoded action values
- Output variables for action-to-action data
- Context and conversation variables
- Template expressions in agent instructions
- Conditional logic for context-specific prompts
- Modality-aware responses for text and voice conversations
- Standard versus custom topics
- Topic classification descriptions and routing
- Custom topic scope and instructions
- Standard versus custom agent actions
- Custom action types from Flow, Apex, and prompt templates
- Creating custom agent actions
- Agent action descriptions and instructions
- Topic-level versus action-level instructions
- Assigning actions to a topic
- User access for standard agent actions
- Asset library versus in-agent creation of subagents and actions
- Customizing a standard subagent within one agent
- Customizing standard agent action behavior
- When to split, combine or add subagents
- Supported channels for Employee and Service agents
- Service Agent connections to digital experiences
- Service Agent connections to messaging channels
- Service Agent connections to email
- Service Agent connections to Agentforce Voice
- Omni-Channel routing to a Service Agent
- Employee Agent connections to Slack
- Employee Agent connections to Lightning Experience and Mobile
- Agent connections, connection instructions and adaptive response formats
- Execution identity by agent type
- Running-user permissions and sharing for actions
- Employee agent access for end users
- Credential-based verification and Service run-as
- Identity verification in sensitive agent actions
- Context variables versus data access control
- Flow run context within agent action execution
- Employee versus Service agent selection
- Agentforce Default versus Employee agent
- When to use Agent API versus standard channels
- Agent API supported agent types
- Agent API versus Models API
- Agent API versus custom agent actions
- Agent API versus other Agentforce APIs and SDKs
- Agent API actions in Flow and Apex
- Agent testing strategy from preparation through retest
- Agentforce Builder versus Testing Center testing
- Builder Simulate versus Live Test modes
- Permissions for Testing Center tests
- Testing Center test case criteria
- Test data and conversation context for Testing Center
- Batch testing and iteration in Testing Center
- AI-generated tests in Testing Center
- Automating agent tests with Testing API and Agentforce DX
- Builder validation checks and trace previews for troubleshooting
- Testing Center subagent and action evaluation
- Testing Center response evaluation
- Testing Center results interpretation
- Quality metric scorers in Testing Center
- Custom Testing Center scorers
- Testing Center limits and considerations
- Prompt template batch evaluation scorers
- Change sets versus Agentforce DX for agent deployment
- Dependent components in an agent change set
- Target-org prerequisites for a deployed agent
- Agent activation after production deployment
- Post-deployment channel and connection configuration
- Agent versioning for uninterrupted production updates
- Data 360 and Data Library considerations for agent promotion
- Agent user reassignment and permissions after a cross-org deployment
- Prompt template change sets and metadata deployment
- Dependent metadata for prompt template deployment
- Prompt Builder enablement in the production org
- Prompt template activation in the destination org
- Prompt template version behavior on deploy
- Field generation Lightning page assignment after deploy
- Retriever and Knowledge grounding after template deployment
- Monitoring in the Agent Development Lifecycle
- Agent manager permission sets
- Activate and deactivate agents
- Agent versioning lifecycle
- Agentforce guardrails and trust patterns
- AI agent risk identification
- Agent guardrail monitoring
- Einstein Audit and Feedback setup
- Audit trail and toxicity review
- Enhanced Event Logs for agent monitoring
- Investigating agent sessions and intents
- Omni-Channel monitoring for Service agents
- Permissions for managing agents versus viewing observability data
- Agent Health Monitoring alerts for silent agent failures
- Agent Analytics versus Agent Optimization
- Agent Analytics metrics dashboards and session insights
- Session Tracing Data Model for observability
- Intents in Agent Optimization
- Utterance analysis for agent conversations
- Quality scores and subagent performance trends
- Scorers and custom scorers in Agentforce Studio
- Context engineering for agent optimization
- Legacy Agentforce Analytics versus Agent Analytics
- Single-agent versus Multi Agent architecture
- Subagents versus a Multi Agent architecture
- Orchestrator routing to connected subagents
- Handoff mode versus supervisor mode orchestration
- Agent Script for deterministic multi-agent routing
- Single-org versus multi-org multi-agent architecture (SOMA and MOMA)
- When to use Model Context Protocol (MCP)
- When to use Agent-to-Agent (A2A) protocol
- MCP server, client and host roles
- Exposing Salesforce capabilities to external AI clients with Hosted MCP Servers
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