Agentforce Specialist Course Notes

AI Agents questions test how Agentforce turns user intent into controlled work: the reasoning engine uses configured subagent descriptions and conversational context to select and chain actions. Deterministic behavior comes from shared variables, explicit input mapping, and availability conditions, while roles, instructions, actions, knowledge, and guardrails define purpose and boundaries. Channel selection determines where an agent is available, and agent type selection depends on whether a supported preconfigured pattern or a custom design fits the job. Security remains separate from reasoning and channel design: execution context, permissions, sharing, field-level access, and Einstein Trust Layer protections work together rather than replacing one another.

• Trust Layer protections govern AI processing and monitoring; permissions, sharing, record access, and field-level security govern what data and actions are available.

• Select Trust Layer controls when the requirement concerns protected prompt processing, harmful-output detection, retention handling, or interaction monitoring.

Data 360 for Agentforce questions test how approved structured and unstructured data grounds agent responses. An Agentforce Data Library provides a streamlined path for supported content, while advanced Data 360 configurations support broader harmonized-data needs. Search indexes prepare content for retrieval; each library has its own retriever, and an ensemble retriever searches multiple individual retrievers in parallel when the use case spans retrieval sources. Choose exact, semantic, or hybrid search based on whether the query depends on identifiers, meaning, or both.

• A single individual retriever searches its configured retrieval target; an ensemble retriever is the supported pattern for parallel search across multiple individual retrievers.

• When a response depends on current customer or product facts, ground the prompt with the appropriate approved data source.

Deployment Lifecycle questions distinguish interactive preview, repeatable Testing Center evaluation, and post-run diagnostics. Preview Simulate uses mock data and actions, Preview Live Test exercises real data and actions in a safe environment, Test Suites handle repeatable cases at scale, and detailed session traces expose action calls, prompts, errors, and execution records beyond an interaction summary. Production promotion also depends on complete agent metadata and channel components, target-org licenses and features, intended active Flow versions, a properly permissioned agent user, and published customer-facing deployments. After release, monitor adoption and validate behavior in every intended channel before treating results as representative.

• An interaction summary only provides a high-level overview of the subagent and reasoning, not the detailed execution records.

• When the question asks for action calls, resolved prompts, errors, or execution details, select the detailed session trace.

Prompt Engineering questions test when to use Prompt Builder, how to choose Field Generation versus Flex, who can manage or execute templates, and how to ground and validate output. Field Generation fits a supported record-field workflow; Flex supports custom purposes with designer-defined resources, and both are processed through the Einstein Trust Layer. Grounding choices depend on the data and processing required: merge fields resolve direct record values, template-triggered Prompt Flow supports maintainable declarative retrieval, branching, and assembly, and Apex fits logic that must be implemented programmatically. Effective templates state the audience, task, context, constraints, and output requirements, then use representative records in Preview before activation.

• A template-triggered prompt flow can declaratively branch, retrieve, process, and assemble data; Apex is appropriate when the required calculation or assembly must be implemented programmatically.

• Use suitable grounding resources for required record context, and use Flow or Apex when the task is actually transactional record automation.

Multi-Agent Interoperability questions begin by identifying the relationship being connected. MCP standardizes access from an AI application to external tools, APIs, data sources, or services; A2A supports discovery, delegation, and stateful collaboration between autonomous agents. Agent API supports live programmatic sessions and message exchange between an application and a Salesforce agent, while Testing API is for automated evaluation rather than customer conversations. Match the protocol to the interaction instead of selecting one based only on the client or channel.

• Testing API is for automated evaluation, so stitching evaluation calls together does not provide the supported live conversational session lifecycle.

• When one autonomous agent must discover and delegate work to another, select A2A.