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  3. Agentforce Specialist

Agentforce Specialist

Checklist progress

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
  • 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
  • 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
  • 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

Given a scenario, test an agent using Testing Center

0/8

Agent testing strategy from preparation through retest

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Agentforce Builder versus Testing Center testing

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Builder Simulate versus Live Test modes

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Permissions for Testing Center tests

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Testing Center test case criteria

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Test data and conversation context for Testing Center

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Batch testing and iteration in Testing Center

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AI-generated tests in Testing Center

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Explain how Testing Center evaluations work

0/6

  • 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

Identify the considerations for deploying an agent from sandbox to production.

0/7

  • 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

Identify the considerations for deploying a template from sandbox to production

0/7

  • 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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Batch testing and iteration in Testing Center

Explainer

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Practice Question

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Next conceptAI-generated tests in Testing Center

Checklist progress

0/163 (0%)

0 of 163 concepts learned

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Prepare for the Exam

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Explainer

Batch testing in Testing Center runs many agent conversations at once so you can see whether a non-deterministic agent stays accurate across a wide range of inputs, not just the few you try by hand. You generate or upload a test suite, let scorers grade each response, then use the failures to refine instructions, actions, and knowledge and run the suite again. That loop continues after go-live whenever the agent, its data, or the business changes.

Core information
  • AI agents mix deterministic expressions with non-deterministic reasoning, so a few manual chats are not enough: you need a wide range of scenarios to confirm consistent, accurate behavior.
More details and nuances
  • Testing agents can modify CRM data, so use Testing Center only in a sandbox. Running tests consumes requests and credits, and generating tests consumes credits, so confirm the configuration before you generate.