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Title: Salesforce AI-201 Agent force Specialist Exam Guide with Topics and Ideal Candid [Print This Page]

Author: sawaira Time: yesterday 23:33
Title: Salesforce AI-201 Agent force Specialist Exam Guide with Topics and Ideal Candid

The AI-201 Agentforce Specialist exam validates your ability to work with Salesforce’s AI agents, configure them for business workflows, and use prompt engineering and data grounding techniques. It focuses on practical skills for building, managing, and deploying AI-powered agents that automate tasks, support users, and enhance customer experiences inside the Salesforce ecosystem. The exam measures real-world knowledge of Agentforce features, data usage, system behavior, and deployment best practices.

Ideal For

Professionals who want to build and configure AI agents for business workflows.
Salesforce admins, consultants, or analysts looking to enhance automation with AI.
Users familiar with CRM processes who want to integrate intelligent agents into sales, service, or internal operations.
Teams aiming to streamline tasks, improve support efficiency, or automate repetitive actions using AI.

Exam Overview

Exam Code: AI-201
Exam Name: Agentforce Specialist
Format: Multiple-choice and multiple-select questions
Time Allowed: 105 minutes
Passing Score: 73%
Difficulty: Intermediate (requires basic Salesforce familiarity)

Covering Topics1. AI Agents (Largest Section)

Understanding agent types and capabilities
Setting up agent topics, instructions, and rules
Connecting agents to channels like chat, email, or internal systems
Using actions, tasks, and decision-making tools inside agents

2. Prompt Engineering

Creating prompts using templates
Writing structured and grounded prompts
Adjusting tone, style, and output behavior
Improving accuracy through prompt optimization

3. Data Cloud and AI Data Library

Setting up data libraries for agent grounding
Choosing retriever types such as keyword, vector, or hybrid
Ensuring data relevance, security, and accuracy
Managing data connections used by AI agents

4. Deployment Lifecycle

Testing agent behavior with real scenarios
Sandboxes vs. production deployment
Monitoring agent performance
Handling permissions, security, and system access

5. Multi-Agent and Integration Concepts

Configuring agents to work together in workflows
Connecting agents to APIs and automation tools
Managing complex, multi-step AI processes

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