Overview
Agentforce covers the administrator's role with Salesforce AI agents. You need to recognize good use cases (variable, language-based requests such as answering questions, summarizing records or drafting replies) and poor ones (fixed rules that a flow handles exactly). You need to understand security: agents act through actions with defined permissions, the Einstein Trust Layer protects data sent to models, and access problems are often permission gaps on the agent or its user. You also maintain agents in Agent Builder, where topics group related jobs, instructions guide behavior, actions do the work, and conversation preview lets you test.
The section was added in the December 2025 refresh of the guide. It reuses earlier sections: permission sets from Configuration and Setup, flows from Automation as agent actions, and case and lead data from the Sales and Service sections.
How it's tested
Questions present a business need or a misbehaving agent and ask what to recommend or check first. Some test whether AI fits at all. Others give a symptom from conversation preview, such as the wrong topic chosen or an action failing, and you identify whether the fix is in instructions, topic scope, action assignment or permissions.
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Is AI the right tool
A requirement is described, and you decide between an agent, a prompt template or a deterministic flow based on whether the task needs language understanding or exact rules.
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Diagnose the agent
Conversation preview shows a symptom, and you trace it to topic selection, instructions, a missing action or a permission gap.
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Secure the agent
Scenarios about customer data or external users test the agent user, permission sets and the protections of the Einstein Trust Layer.
An admin tests a service agent in conversation preview. The agent picks the correct topic and calls the right order-lookup action, but the action returns no data for customers. Because topic selection and action choice are correct, rewriting instructions will not help. The symptom points to access: the agent's running user lacks permission to the object or flow behind the action, so the fix is a permission set for that user.
Objectives 2
Official objectives from the Salesforce outline, in outline order.
8.1 Describe the capabilities and use cases of Agentforce (scenarios about use cases, when it's appropriate to use AI, security, troubleshooting agent permissions).Where agents fit, how they stay secure, and why they fail.
- What it means
- Agentforce agents take natural-language requests from employees or customers, reason about which topic applies, and run actions such as flows, Apex, prompt templates and standard actions to answer or complete work. The Einstein Trust Layer adds protections such as grounding with org data, masking of sensitive data, toxicity checks and zero data retention agreements with model providers.
- What to understand
- AI is appropriate when requests vary in wording and need understanding, summarization or generation; deterministic rules with fixed inputs belong in a flow or standard rule. Agents can only do what their assigned actions allow, and those actions run with the permissions of the agent's running user, so service agents for customers rely on a dedicated agent user with explicitly granted permission sets. Permission gaps are the first thing to check when an action fails.
- How it shows up
- A company wants an agent to answer order questions on its website, and you identify the right use case, the need for a dedicated agent user, and the permission check when it cannot read orders.
- Common confusion
- Candidates assume an agent sees everything in the org; it is limited to its actions and the running user's access, which is a security feature rather than a defect.
8.2 Explain how to maintain, update, or install prompts and instructions in Agent Builder (light testing, conversation preview).Topics, instructions, actions and testing in Agent Builder.
- What it means
- In Agent Builder, admins configure topics (a job with a classification description and scope), instructions (natural-language guidance for how the agent should behave within a topic), and the actions available to each topic. Prompt templates built in Prompt Builder can be added as actions, and prompts and agent assets can be installed from packages such as AgentExchange listings. Conversation preview lets admins chat with the agent and inspect which topic and actions it chose.
- What to understand
- Each component fixes a different problem: a vague topic description causes wrong topic selection, missing or unclear instructions cause wrong behavior within a topic, and a missing action means the agent simply cannot do the task. Light testing with varied phrasings in conversation preview should follow every change before the agent is used by real users.
- How it shows up
- An agent sends billing questions to the wrong topic, and the fix is to sharpen the topic's classification description and scope, then retest in conversation preview.
- Common confusion
- Instructions guide reasoning but do not grant abilities; telling the agent to update a record does nothing unless an action that updates the record is assigned to the topic.
High-yield concepts
The terms, rules and formulas this domain turns into questions most often.
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Agent
An AI assistant built in Agent Builder that interprets requests, chooses a topic and runs actions to respond or complete work.
- Remember
- An agent is only as capable as the actions assigned to its topics.
- Tested as
- Foundation for every question in this section.
- Trap
- Expecting an agent to act on data it has no action for.
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Topic
A category of jobs the agent can do, defined by a classification description, a scope and instructions, with actions attached.
- Remember
- The classification description drives which topic the agent selects for a request.
- Tested as
- Wrong-topic symptoms in conversation preview.
- Trap
- Rewriting actions when the topic description is the cause of misrouting.
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Instructions
Natural-language guidance within a topic that shapes how the agent behaves, what it should ask, and what it must avoid.
- Remember
- Clear, specific instructions improve behavior but never add permissions or capabilities.
- Tested as
- Scenarios where the agent does the right job in the wrong way.
- Trap
- Using instructions to make the agent perform an action it does not have.
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Actions
The things an agent can do: flows, Apex, prompt templates and standard actions assigned to topics.
- Remember
- An action runs with the access of the agent's running user.
- Tested as
- Diagnosing why an agent cannot complete a task.
- Trap
- Building a new action when the existing one only lacks permissions.
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Prompt template
A reusable prompt built in Prompt Builder, grounded with record fields and other data, that generates text such as summaries or emails.
- Remember
- Templates can be used directly in the UI or added to agents as actions.
- Tested as
- Requirements for consistent AI-generated content tied to records.
- Trap
- Choosing a full agent when a single prompt template meets the need.
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Conversation preview
The Agent Builder test panel where admins chat with the agent and inspect its chosen topic, actions and responses.
- Remember
- Use it after every change, with several phrasings of the same request.
- Tested as
- Named in the guide as the light-testing tool.
- Trap
- Activating changes for users without previewing them.
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Agent user and permission sets
Customer-facing service agents run as a dedicated agent user whose permission sets define which objects, fields and actions it can use.
- Remember
- Grant the least access the agent's actions need.
- Tested as
- Troubleshooting agent permissions, which the guide names explicitly.
- Trap
- Giving the agent user broad access such as View All Data to make errors disappear.
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Einstein Trust Layer
Salesforce's set of protections for generative AI, including data grounding, masking of sensitive data, toxicity detection and zero data retention with model providers.
- Remember
- It protects data sent to and returned from models; record access is still controlled by permissions.
- Tested as
- Security questions about customer data and AI.
- Trap
- Assuming the Trust Layer replaces permission design.
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When AI is appropriate
AI fits tasks with variable language, summarization, classification or generation; fixed rules with known inputs fit a flow.
- Remember
- If a rule can be written exactly, automate it without AI.
- Tested as
- Tool-selection scenarios that include AI as an option.
- Trap
- Choosing an agent for a simple deterministic field update.
Side by side
Concepts candidates mix up, compared on the point that decides the answer.
Topic vs. instructions vs. action
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Topic | The job category the agent chooses | Selected from its classification description and scope | Blaming actions for misrouted requests |
| Instructions | Guidance on how to behave within a topic | Shape responses; add no capabilities | Expecting them to grant data access |
| Action | What the agent can actually do | Runs a flow, Apex, prompt template or standard action | Forgetting to assign it to the topic |
Agent vs. prompt template vs. flow
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Agent | Conversational AI that chooses topics and actions | Handles varied requests across several jobs | Using it for a single fixed rule |
| Prompt template | Single grounded generation task | Consistent output for one purpose | Building a whole agent for one summary |
| Flow | Deterministic automation | Exact, repeatable logic without AI | Adding AI where rules are fixed |
Exam traps
Each trap is an answer that looks right. Learn the clue that should change your mind.
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Prompt fix for a permission problem
- What candidates pick
- Rewrite the topic instructions when an action returns an access error.
- Why it looks right
- Agent behavior feels like a prompt problem.
- Why it's wrong
- If preview shows the right topic and action, the failure is access; the agent user needs a permission set for the object or flow.
Clue to watch for Conversation preview shows correct topic and action selection but the action fails.
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AI for fixed rules
- What candidates pick
- Use an agent to set a priority field from a fixed list of case types.
- Why it looks right
- AI is new and appears in the guide.
- Why it's wrong
- The rule is deterministic and fully specified, so a flow or standard rule is simpler, cheaper and predictable.
Clue to watch for The requirement lists exact conditions and outcomes.
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Instructions as capabilities
- What candidates pick
- Add an instruction telling the agent to create a return case for customers.
- Why it looks right
- Instructions are where behavior is defined.
- Why it's wrong
- Without an action that creates the case assigned to the topic, the agent cannot perform it, whatever the instructions say.
Clue to watch for The agent says it cannot help with a task it was instructed to do.
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Broad access to fix errors
- What candidates pick
- Assign View All Data or a powerful profile to the agent user.
- Why it looks right
- It makes access errors disappear quickly.
- Why it's wrong
- It exposes far more data to an external-facing agent than its actions need, breaking least privilege.
Clue to watch for The agent serves customers or external users.
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Untested changes
- What candidates pick
- Activate updated topics and instructions without testing.
- Why it looks right
- The changes look small.
- Why it's wrong
- Small wording changes can shift topic selection; conversation preview with several phrasings catches regressions first.
Clue to watch for The guide's mention of light testing and conversation preview.
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Trust Layer as access control
- What candidates pick
- Rely on the Einstein Trust Layer to stop the agent reading records it should not.
- Why it looks right
- The Trust Layer is described as Salesforce's AI security.
- Why it's wrong
- The Trust Layer protects data sent to models; which records an agent can read is controlled by permissions and sharing.
Clue to watch for The question is about which data the agent can reach.
Answer framework
For any Agentforce scenario, decide whether AI fits, then work through the agent's layers from selection to access.
- Confirm the use case needs AI: varied language, summarization or generation; otherwise choose a flow or standard rule.
- If the agent picks the wrong job, refine the topic's classification description and scope, then retest in conversation preview.
- If it picks the right topic but behaves poorly, clarify that topic's instructions with specific guidance and limits.
- If it cannot do the task, check that the needed action exists and is assigned to the topic, then check the agent user's permission sets for the objects and flows involved.
- Grant only the access the actions need, test with several phrasings before users see changes, and rely on permissions rather than the Trust Layer for data access.
Practice question
Want more like this? The Salesforce Admin practice test has questions for every domain.
Checklist
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Study strategy
Start with concepts, because many candidates have not built an agent at work. Learn the vocabulary first: agent, topic, instructions, action, prompt template, agent user and Einstein Trust Layer, and be able to say what each one controls in one sentence. The Trailhead content on Agentforce and Prompt Builder gives a free practice environment for hands-on work.
High-priority concepts are when AI is appropriate and troubleshooting agent permissions, both named in the guide. In a practice org, build a simple agent with one topic, a flow action and clear instructions, then break it deliberately: remove the action, change the topic description, and remove the agent user's permission set. Watch how each failure looks in conversation preview.
Commonly underestimated areas are the boundaries between instructions and actions, and between the Trust Layer and permissions. Afterwards, practice scenario questions that describe a preview symptom, and name the layer at fault before choosing a fix. Reread Configuration and Setup on permission sets, since the same least-privilege logic decides many questions here.
FAQ
How much of the exam is Agentforce?
Agentforce is 8% of the exam, the smallest section, which suggests about five of the 60 scored questions as a planning estimate.
When was Agentforce added to the Platform Administrator exam?
It was added in the refreshed exam guide effective 15 December 2025, which aligns questions to the Summer '25 release.
Do I need to build agents to pass?
Hands-on practice helps a great deal, because questions describe symptoms you will recognize from conversation preview. The guide focuses on use cases, security, permissions and maintaining prompts and instructions, not advanced development.
What is the difference between a topic and an instruction?
A topic defines a job the agent can do and is selected from its classification description. Instructions guide how the agent behaves within that topic but add no new abilities.
Why does an agent action fail when it works for me as an admin?
The action runs with the agent user's permissions, not yours. Check that the agent user has a permission set covering the objects, fields and flow the action uses.
How many hours should I plan for this section?
About 10 to 15 hours is a reasonable editorial estimate, more if you have never used Agent Builder or Prompt Builder.
How does Agentforce relate to Automation?
Flows are common agent actions, and the Automation section lists AI among the tools to choose from. Use a flow for fixed rules and an agent when requests vary in language.
Other CRM & Business Applications certifications
Where the same subject shows up in other exams. Cards marked "Same subject" open the matching domain.
Test your Domain 8 knowledge
You have covered when AI fits, agent security and permissions, and maintaining topics, instructions and prompts with conversation preview. Check whether you can diagnose agent symptoms in timed scenarios.
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