AI Book Club: Agentic Architectural Patterns for Building Multi-Agent Systems
June's book is "Agentic Architectural Patterns for Building Multi-Agent Systems"!
This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.
Feel free to join the discussion even if you have not read the book chapters! :)
Want to discuss the contents during the reading week? Join the Flyte MLOps Slack group.
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About the book:
Title: Agentic Architectural Patterns for Building Multi-Agent Systems
Authors: Dr. Ali Arsanjani, Juan Pablo Bustos
Published: January 2026
O'rielly platform: https://learning.oreilly.com/library/view/agentic-architectural-patterns/9781806029570/
Chapters:
Part 1: Foundations and Core Agent Concepts
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
Part 2: Agentic AI: Architecture and Design Patterns
Chapter 4: Agentic AI Architecture: Components and Interactions
Chapter 5: Multi-Agent Coordination Patterns
Chapter 6: Explainability and Compliance Agentic Patterns
Chapter 7: Robustness and Fault Tolerance Patterns
Chapter 8: Human-Agent Interaction Patterns
Chapter 9: Agent-Level Patterns
Chapter 10: System-Level Patterns for Production Readiness
Chapter 11: Advanced Adaptation: Building Agents That Learn
Part 3: Execution: Strategy, Use Cases, and The Future
Chapter 12: A Practical Roadmap: Implementing Agentic Patterns by Maturity Level
Chapter 13: Use Case: A Single Agent for Loan Processing
Chapter 14: Use Case: A Multi-Agent System for Loan Processing
Chapter 15: Agent Frameworks – Use Case: A Multi-Agent System for Loan Processing with CrewAI and LangGraph
Chapter 16: Conclusion: Charting Your Agentic AI Journey
Book Description
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).