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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
20
Index

Fallback Model Invocation

Agentic systems idx_d6f1cfc3that rely on a single LLM have a critical single point of failure. The primary LLM, often the most powerful and expensive one, may experience performance degradation, a complete outage, or model drift that causes it to start failing critical tasks. Without a backup, any of these issues would bring the entire system to a halt.

The Fallback Model Invocation pattern provides a crucial business continuity strategy for LLM-powered features. It establishes a runtime mechanism to automatically switch from a failing primary idx_fa95a807model to a stable backup model, allowing the system to degrade gracefully rather than fail completely.

Context

This pattern idx_8e4851bbapplies when system reliability is paramount, and the primary LLM cannot be a single point of failure. It is a common strategy for balancing state-of-the-art performance (using the best model) with cost-effective stability (using a reliable backup).

Problem...

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Agentic Architectural Patterns for Building Multi-Agent Systems
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