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  3. Agentforce Specialist

Agentforce Specialist

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0/163Learned

Agentforce Specialist

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

Checklist progress

0/163Learned

  • When to use Prompt Builder
  • Prompt Builder versus predictive AI
  • Prompt Builder versus Flow automation
  • Prompt Builder versus Agent Builder
  • Prompt templates as Agentforce actions
  • Prompt Builder enablement and user assignment
  • Prompt Template Manager versus Prompt Template User
  • Type-specific permissions for prompt templates
  • Running-user data access for prompt templates
  • Field Generation prompt templates
  • Flex prompt templates
  • Sales Email prompt templates
  • Record Summary prompt templates
  • 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
  • Prompt template creation in Prompt Builder
  • Prompt template preview and revision
  • Prompt template activation and draft status
  • Prompt template execution surfaces
  • 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
  • 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
  • Supported LLMs for Prompt Builder and Agentforce
  • Org-level model enablement and blocking
  • Prompt template model selection
  • Agentforce model option and subagent overrides

Explain the considerations of Agentforce Data Library and its concepts.

0/9

  • 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

Explain foundational concepts of Data 360 such as chunking, indexing, and retrievers

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Retrieval augmented generation in Data 360

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Chunking sources for AI retrieval

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Search indexes for chunked Data 360 content

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Vector versus hybrid search indexes

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Retrievers versus search indexes

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Retriever creation and activation for grounding

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Ensemble retrievers in Data 360

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Knowledge retrieval troubleshooting for agents

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  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • When to use Model Context Protocol (MCP)
  • When to use Agent-to-Agent (A2A) protocol

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Search indexes for chunked Data 360 content

Explainer

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Practice Question

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Next conceptVector versus hybrid search indexes

Checklist progress

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Explainer

A Data 360 search index is how Data 360 stores chunked, vectorized content from a data model object (DMO) or unstructured data model object (UDMO) so other applications can search it. Chunking breaks the source into smaller, semantically meaningful units such as sentences or paragraphs, and vectorization turns those chunks into numeric embeddings that capture meaning. Retrievers in AI Models (formerly Einstein Studio) then search that index to ground prompt templates, agent actions, and related features with relevant results.

Core information
  • To add content to a search index you must first chunk it: Data 360 splits DMO or UDMO data into manageable units, then generates machine-readable vector embeddings from those chunks.
More details and nuances
  • Use vector search when the source and queries mainly need semantically aware matches in a large dataset, such as 'What is Acme famous for?'; use hybrid search when they also need keyword precision, such as a part number in 'Does Acme motor XYZ123 use hydraulic pumps?'