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Agentic Architectural Patterns for Building Multi-Agent Systems

Agentic Architectural Patterns for Building Multi-Agent Systems

By : Dr. Ali Arsanjani, Juan Pablo Bustos
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Agentic Architectural Patterns for Building Multi-Agent Systems

Agentic Architectural Patterns for Building Multi-Agent Systems

5 (1)
By: Dr. Ali Arsanjani, Juan Pablo Bustos

Overview of this book

Generative AI has moved beyond the hype, and enterprises now face the challenge of turning prototypes into scalable solutions. This book is your guide to building intelligent agents powered by LLMs. Starting with a GenAI maturity model, you’ll learn how to assess your organization’s readiness and create a roadmap toward agentic AI adoption. You’ll master foundational topics such as model selection and LLM deployment, progressing to advanced methods such as RAG, fine-tuning, in-context learning, and LLMOps, especially in the context of agentic AI. You'll explore a rich library of agentic AI design patterns to address coordination, explainability, fault tolerance, and human-agent interaction. This book introduces a concrete, hierarchical multi-agent architecture where high-level orchestrator agents manage complex business workflows by delegating entire sub-processes to specialized agents. You’ll see how these agents collaborate and communicate using the Agent-to-Agent (A2A) protocol. To ensure your systems are production-ready, we provide a practical framework for observability using life cycle callbacks, giving you the granular traceability needed for debugging, compliance, and cost management. Each pattern is backed by real-world scenarios and code examples using the open source Agent Development Kit (ADK). *Email sign-up and proof of purchase required
Table of Contents (21 chapters)
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Part 1: Foundations and Core Agent Concepts
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Part 2: Agentic AI: Architecture and Design Patterns
14
Part 3: Execution: Strategy, Use Cases, and The Future
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Index

Part 1

Foundations and Core Agent Concepts

In this part, you will build the strategic and technical foundation required to transition from simple GenAI experiments to robust, production-grade agentic systems. We begin by exploring the GenAI Maturity Model, a roadmap that charts the evolution from basic data preparation to advanced multi-agent collaboration. You will dissect the fundamental anatomy of an agent—Sense, Reason, Plan, and Act—and introduce the emerging agentic stack, including interoperability standards like the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication. Finally, we focus on the agent’s cognitive core, detailing how to select, deploy, and adapt LLMs using techniques ranging from retrieval-augmented generation (RAG) to fine-tuning, ensuring your models are truly "agent-ready."

This part of the book includes the following chapters:

  • Chapter 1, GenAI in the Enterprise: Landscape, Maturity, and Agent Focus
  • Chapter 2, Agent-Ready LLMs: Selection, Deployment, and Adaptation
  • Chapter 3, The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning
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Agentic Architectural Patterns for Building Multi-Agent Systems
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