Practical Salesforce Agentforce Playbook : Design, Build, and Deploy Enterprise-Grade AI Agents with Salesforce Agentforce and Data Cloud.

Format:

Book

Author/Creator:

Malmqvist, Lars.

Language:

English

Subjects (All):

Salesforce (Online service).

Artificial intelligence.

Physical Description:

1 online resource (298 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2026.

Summary:

Build production-ready AI agents on Salesforce Agentforce with Atlas Reasoning Engine, Data Cloud, Flow, and ApexFree with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Build intelligent, autonomous AI agents with the Agent Builder and the Atlas Reasoning Engine Extend agents with Apex, Flow, and secure.

Contents:

Intro

Practical Salesforce Agentforce Playbook

Design, build, and deploy enterprise-grade AI agents with Salesforce Agentforce and Data Cloud

Contributors

About the author

About the reviewer

Table of Contents

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Free benefits with your book

How to Unlock

Share your thoughts

Part 1

Building Your First Agentforce Agent

1

Understanding Agentforce Architecture

Transitioning from copilots to autonomous agents

What a copilot does well

What an autonomous agent changes

Why the wider industry is moving

Agentforce and the evolution from Einstein Copilot

Implications for Salesforce teams

Adoption patterns you can expect

Understanding Agentforce building blocks

Agent

Topic

Action

Instructions

Scope

Channel

Agent identity and permissions

Prompt template

Variables

Knowledge and retrieval

Guardrails

Einstein Trust Layer

Configuration surface

Deconstructing the Atlas Reasoning Engine

Core components

Topic selection

The runtime loop

Operations and results

Variables and working memory

Context and retrieval

Stop conditions and handoff

Exploring the Einstein Trust Layer

Prompt journey

Security provisions

Identifying agent types and use cases

What a template represents

Core agent types on the platform

Use case patterns that fit agents

Summary

Get this book's PDF version and more

2

Setting Up Your Agentforce Environment

Technical requirements

Creating your free developer org

Licensed sandboxes

Agentforce Developer Edition

Signing up for your org.

Enabling Agentforce, Einstein generative AI, and Data Cloud

Activating Einstein generative AI

Verifying Agentforce availability

Provisioning and connecting Data Cloud

Configuring the Einstein Trust Layer

Installing Salesforce CLI, VS Code, and the Agentforce plugin

Granting the required permission sets and licenses

Human user permissions

Automated agent user permissions

Smoke-testing the setup with a sample agent

Accessing Agent Builder and the Conversation Preview

Performing a basic interaction test

Verifying trust layer masking

3

Building with Agent Builder

Navigating the Agent Builder interface

Accessing the Builder through Agentforce Studio

The three panes of Agent Builder

The left configuration pane

The center plan tracer pane

The right-hand Conversation Preview pane

Key interface controls for effective development

Creating and configuring Topics

The anatomy of a Topic

Adding Topics to your agent

Assigning Actions from the Asset Library

The role of the Agent Actions library

Attaching an Action to a Topic

Configuring Action Inputs, Outputs, and Instructions

Writing natural language instructions

Topic-level instructions to define the overall plan

Action-level instructions to choose the right tool

Best practices for writing effective instructions

Testing with Conversation Preview

The iterative testing workflow

Simulating real-world context with Preview Conditions

The updated Agent Builder (Beta) interface

The Explorer panel

Canvas and tabbed editing

Preview and the Agentforce assistant

Versioning and saving

4

Creating a Room Booking Agent

Technical requirements.

Analyzing requirements and designing the solution

Defining the business scenario

Solution architecture

Data modeling strategy

The user interaction design

Implementing core booking functionality

Configuring the scheduler data model

Phantom" users and permissions

Developing the Apex toolbelt

Action 1: Finding availability (RoomSlotsAction)

Action 2: Booking the room (RoomBookingAction)

Building calendar integration and availability checking

Creating the room booking agent

Configuring the Topic details

Registering the Actions

Adding the "Query Records" action

Adding the "Find room slots" action

Writing the instructions

Instruction 1: Data collection

Instruction 2: Pre-processing

Instruction 3: Execution

Instruction 4: Presentation logic

Instruction 5: Error handling

Testing the availability logic

Handling conflicts and edge cases

Creating the transactional topic

Instruction 1: The confirmation handshake

Instruction 2: The transaction (with guardrails)

Instruction 3: The conflict resolution loop

Instruction 4: The success summary

Testing the happy path and the edge case

Enhancing the user interface with smart features

Handling incomplete information

Humanizing data presentation

Proactive problem solving

5

Testing and Debugging Agents

Leveraging the testing center interface

The agent testing lifecycle

Anatomy of a test suite

Running a test in the testing center

Building test suites

Structuring the test data

The importance of API names

Defining semantic expectations

Executing the test run

Analyzing the results overview

Debugging conversation flows

Interpreting expected versus actual results

Categorizing failure types.

From triage to reproduction

Analyzing agent reasoning with Plan Tracer

Reproducing the failure

Deciphering the execution path

The iterative debugging loop

Implementing automated testing strategies

The Agentforce DX philosophy: tests as metadata

The CI/CD pipeline integration

A "gold standard" Agent pipeline

Programmatic execution via Connect API

Part 2

Scaling Beyond Basic Agents

6

Implementing Business Logic with Flow and Apex

Designing agent-optimized flows

The shift from user interface to agent interface

Step 1: Defining the input/output contract

Step 2: Implementing defensive logic

Step 3: The "error as data" pattern

Step 4: Finalizing and activating

Design considerations for production

Creating invocable Apex actions

The Invocable interface

Step 1: Defining the data transfer objects (DTOs)

Step 2: The bulkification pattern

Step 3: Implementing secure logic

Step 4: Registering the Apex Action

Implementing asynchronous processing patterns

The async offload architecture

Step 1: Defining the heavy logic (the queueable)

Step 2: The dispatcher action (the invocable)

Architectural considerations

Event-driven architectures

Building reusable component libraries

The philosophy of atomic design

Sharing actions across personas

Managing the lifecycle

Testing and deploying custom logic

Unit testing invocable Apex

The permissions trap

Orchestrating actions with Agent Script

The hybrid reasoning model

The structure of Agent Script

Working with Agent Script

Verify your work

7

Establishing Data Foundations with Data Cloud

Understanding Data Cloud architecture for Agents.

The hybrid storage model

The ingestion pipeline

Unstructured data architecture

Data Spaces as the governance boundary

Appreciating vector databases

The concept of embeddings

Chunking and indexing

Creating an Einstein Data Library

Verifying the artifacts in Data Cloud

Configuring semantic and hybrid search

Vector versus hybrid search

Building real-time data pipelines

Ingestion versus retrieval

Optimizing data retrieval performance

The limits of pure vector search

8

Securing Your Agentforce Implementation

The secure gateway and zero data retention

Data masking and privacy controls

Toxicity detection

Implementing data access controls

Execution contexts: User vs. Agent

Verifying the Agent identity

Guardrails and scope

Policy controls through instructions

Managing Agent-specific permissions

The author vs. the user

Configuring Agent access

The dependency chain: Data, cloud, and knowledge

Best practice: The "agent user" persona (for service agents)

Establishing audit and compliance

Enabling Agentforce session tracing

The audit data model

The event log

Protecting sensitive data

Data cloud security: policy-based governance

Enabling platform-level AI governance

Part 3

Managing Agents at Scale

9

Deploying Agents with DevOps Best Practices

Managing agent metadata in source control

The dual nature of agent metadata

The Agent pseudo-metadata type

The Agent Release Unit

Packaging for distribution (2GP)

Building CI/CD pipelines for agents

The environment strategy

DevOps Center as the governance layer

Quality gates: Making safety mandatory.

Implementing safe deployment strategies.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-80638-922-3

9781806389223