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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
5
Part 2: Agentic AI: Architecture and Design Patterns
14
Part 3: Execution: Strategy, Use Cases, and The Future
20
Index

Adversarial Testing and Red Teaming

Inidx_6fb4bbbc traditional software engineering, quality assurance is a cornerstone of building reliable systems. We design comprehensive test suites to find bugs, handle edge cases, and ensure that the software behaves as expected under stress. For systems with static, predictable logic, this approach is highly effective.

However, a self-improving agentic system presents a new and profound challenge: its behavior is not static. As the agent learns and adapts, its potential failure modes also evolve, making a fixed set of tests quickly obsolete. A prompt that was safe yesterday might trigger a hallucination or a security breach today after the model has updated its weights.

To ensure the long-term reliability of these dynamic systems, we must adopt an equally dynamic approach to testing. The Adversarial Testing and Red Teaming pattern provides this by creating an automated, continuous quality assurance loop. The solution is to deploy a specialized...

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Agentic Architectural Patterns for Building Multi-Agent Systems
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