Salesforce Agentforce 360 for Public Sector AP study guide
Salesforce Agentforce 360 for Public Sector AP · Work through your exam, one topic at a time.
Agentforce 360 for Public Sector is a Salesforce accreditation for practitioners who build, ground, and govern AI agents for government and public-sector organisations. It suits administrators, consultants, solution architects, and developers working with Agentforce alongside Government Cloud and Public Sector Solutions, where an agent's answer must be traceable to an approved source and must respect citizen-facing policy and record permissions. The Salesforce Agentforce 360 for Public Sector AP study guide has 5 sections and about 1,732 words.
01Agentforce Fundamentals and the Agentic Enterprise
An assistant suggests; an agent acts. In Agentforce, the reasoning layer interprets a constituent's request in natural language, picks a topic , selects an action , fills that action's inputs, executes it, and then explains the result to the user.
335 words, about 2 min02Agentforce Configuration: Topics, Actions, and Agent Script
A topic is defined by a name , a description the planner reads to decide whether a request belongs here, instructions that constrain tone and behavior inside the scope, and the actions it may use. Routing quality is a description problem, not a code problem - vague or overlapping topic descriptions are the number-one cause of an agent answering in the wrong scope or refusing.
365 words, about 2 min03Grounding Agents with Data 360 and Trusted Public Sector Data
Grounding means retrieving relevant, permission-respecting data at conversation time and placing it in the prompt. The model is not retrained ; nothing is learned permanently.
331 words, about 2 min04Public Sector Solutions and Government Cloud Use Cases
Candidates most often lose points by conflating Public Sector Solutions (PSS) with Government Cloud . PSS is an industry product layer - data model, components, and processes - built on the platform for licensing, permitting, inspections, benefits, grants, and constituent service.
354 words, about 2 min05Trust, Security, Governance, and Testing of Agents
The Einstein Trust Layer sits between the agent and the model: secure retrieval, dynamic grounding, data masking of sensitive fields, zero data retention by the model provider, toxicity detection, prompt-injection defences, and an audit trail of interactions. Trust Cards document a model's characteristics.
347 words, about 2 min