Palerwal | Agentic AI for Salesforce Enterprises | Buch | 979-8-8688-3218-5 | www.sack.de

Buch, Englisch, Format (B × H): 178 mm x 254 mm

Palerwal

Agentic AI for Salesforce Enterprises

Building Intelligent Systems with Agentforce, MCP, and Multi-Agent Architecture
1. Auflage 2027
ISBN: 979-8-8688-3218-5
Verlag: APRESS L.P.

Building Intelligent Systems with Agentforce, MCP, and Multi-Agent Architecture

Buch, Englisch, Format (B × H): 178 mm x 254 mm

ISBN: 979-8-8688-3218-5
Verlag: APRESS L.P.


Enterprise software is undergoing a fundamental shift with the rise of agentic AI systems that can reason, act, and collaborate to solve complex business problems. This book provides a comprehensive, practitioner-focused guide to understanding and implementing Agentforce, starting with the foundations of agentic AI and progressing into a deep dive on architecture, protocols, and production-ready design. From building your first enterprise-grade agent to integrating external frameworks and leveraging the Model Context Protocol, the book equips you with the knowledge needed to move beyond experimentation and into real-world deployment.

As the book advances, the focus shifts to scaling and operationalizing agentic systems through multi-agent orchestration patterns, robust data architecture, and effective prompt and instruction design. The book also emphasizes critical enterprise concerns such as testing, monitoring, observability, security, and governance, ensuring that these systems are not only functional but reliable, secure, and aligned with organizational standards. By the end of this book, you will have a clear roadmap for designing, building, and managing agent-driven applications within enterprise ecosystems.

Unlike guides that assume Agentforce licensing, this book works with or without it—covering native, open-source, and hybrid paths across the full stack: Agentforce, MCP, the Agent2Agent (A2A) protocol, and external frameworks such as LangChain and CrewAI. Whichever path your enterprise takes, you will finish with agents that clear the blockers that stall most AI initiatives: security, governance, and compliance, from SOX to GDPR to HIPAA.

What You Will Learn:
  • Design and build production-ready AI agents using Agentforce and enterprise architecture principles
  • Implement multi-agent systems, integrate external frameworks, and structure effective prompts for reliable outcomes
  • Apply best practices for testing, observability, security, and governance in enterprise-grade agentic systems

Who This Book Is For: 

Salesforce Technical Architects designing enterprise AI solutions across complex, multi-cloud environments

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Zielgruppe


Professional/practitioner


Autoren/Hrsg.


Weitere Infos & Material


Chapter 1: The Agentic AI Revolution in Enterprise Software.- Chapter 2: Agentforce Architecture Deep Dive.- Chapter 3: Building Your First Production Agent.- Chapter 4: The Model Context Protocol.- Chapter 5: Bringing External Agent Frameworks to Salesforce.- Chapter 6: Multi-Agent Orchestration Patterns.- Chapter 7: Prompt Engineering and Instruction Design for Enterprise Agents.- Chapter 8: Data Architecture for Agentic Systems.- Chapter 9: Testing, Monitoring, and Observability.- Chapter 10: Enterprise Security, Governance, and the Road Ahead.


Himanshu Palerwal is a Senior Manager of Post-Sales Technical Architecture at ServiceTitan. He brings more than 15 years of deep Salesforce ecosystem experience and holds 20 Salesforce certifications. Previously, he served as VP Technical Lead/Architect (Consumer Banking) at JPMorgan Chase and held architecture roles at Bank of America, designing large-scale platform integrations and automation frameworks within highly regulated financial services environments. Himanshu specializes in enterprise architecture at the intersection of Salesforce platforms and agentic AI—including multi-agent orchestration, Model Context Protocol (MCP), LLM integration patterns, and AI governance frameworks.

Himanshu's unique vantage point (straddling enterprise Salesforce architecture and the emerging agentic AI landscape) positions him to bridge the gap between theoretical AI capabilities and practical, production-grade implementations. This book draws directly from his experience building intelligent systems across industries where reliability, governance, and scale are non-negotiable.



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