Agentic Architectures on Azure: Building Production-Grade AI Agent Systems with Microsoft Cloud Services
暫譯: Azure 智慧代理架構：使用 Microsoft Cloud Services 建構生產等級 AI Agent 系統

Cataldi, Ricardo

Agentic Architectures on Azure: Building Production-Grade AI Agent Systems with Microsoft Cloud Services

商品描述

This book is a practical, production-focused guide to building autonomous AI agent systems that go beyond prototypes. As organizations move from demos to real-world deployments, they encounter challenges around scalability, security, cost, and reliability--areas often overlooked in existing resources. This book addresses those gaps by applying proven distributed systems principles, such as bounded contexts, contract-based communication, and resilience, to multi-agent AI systems on Azure.

Using a hands-on, progressive approach, you'll build a multi-agent retail system from scratch--starting with a single agent on Azure Container Apps and evolving into a full event-driven architecture powered by Azure OpenAI, Azure AI Foundry, and the Microsoft Agent Framework. You'll learn how to design agent boundaries, enable synchronous and asynchronous communication, and orchestrate intelligent workflows using modern Azure services, with working code and infrastructure templates throughout.

The book also covers what it takes to run these systems in production, including secure zero-trust architectures, CI/CD pipelines, observability, and cost control. By the end, you'll have a clear blueprint for designing, deploying, and managing scalable, enterprise-grade agentic systems on Azure.

What You Will Learn:

  • Design multi-agent architectures using Azure Container Apps, AKS, and Azure AI Foundry

  • Implement synchronous agent communication with MCP servers hosted on Azure Container Apps

  • Build event-driven agent workflows using Azure Service Bus, Event Hubs, and Event Grid

  • Deploy agent infrastructure using Bicep templates and the Azure Developer CLI (azd)

  • Operate multi-agent systems with Azure Monitor, Application Insights, and Cost Management

Who This Book Is For:

Azure Solution Architects designing AI-enhanced enterprise systems and back-end engineers on Azure

商品描述(中文翻譯)

本書是一本以實務與正式上線為導向的指南，帶您建置超越原型階段的自主 AI 代理系統。當組織從展示（demo）邁向實際部署時，往往會面臨擴充性、安全性、成本與可靠性等挑戰，而這些面向經常被現有資源忽略。本書將分散式系統中經過驗證的原則，例如有界內容（bounded contexts）、基於契約的通訊（contract-based communication）與韌性（resilience），應用於 Azure 上的多代理 AI 系統，以填補這些不足。

透過實作導向、循序漸進的方法，您將從零開始建置一套多代理零售系統：先在 Azure Container Apps 上部署單一代理，接著逐步演進為由 Azure OpenAI、Azure AI Foundry 與 Microsoft Agent Framework 驅動的完整事件驅動架構。您將學習如何設計代理邊界、實現同步與非同步通訊，以及運用現代 Azure 服務協調智慧型工作流程；全書並提供可運作的程式碼與基礎架構範本。

本書也涵蓋如何將這些系統正式投入生產環境，包括安全的零信任架構、CI/CD 管線、可觀測性與成本控管。讀完本書後，您將掌握一套清晰的藍圖，用於在 Azure 上設計、部署與管理具備擴充能力、符合企業等級需求的代理式系統。

您將學到：

• 使用 Azure Container Apps、AKS 與 Azure AI Foundry 設計多代理架構
• 使用託管於 Azure Container Apps 的 MCP 伺服器，實作代理之間的同步通訊
• 使用 Azure Service Bus、Event Hubs 與 Event Grid 建置事件驅動的代理工作流程
• 使用 Bicep 範本與 Azure Developer CLI（azd）部署代理基礎架構
• 使用 Azure Monitor、Application Insights 與 Cost Management 維運多代理系統

本書適合：

設計 AI 強化企業系統的 Azure 解決方案架構師，以及使用 Azure 的後端工程師

作者簡介

Ricardo Cataldi is a Senior Solution Engineer on Microsoft's Global Black Belt (GBB) team for AI, Apps, and Agents, where he drives enterprise AI initiatives from discovery to production. With a master's degree in Applied Economics (UFRGS) and graduate studies in Mathematics (USP), he brings rigorous analytical foundations to enterprise AI delivery. At Microsoft, Ricardo has delivered AI solutions for banking, education, retail, and telecommunications customers. He owns delivery from discovery to production, industrializing machine-learning engineering pipelines and building multi-agent workflows with reliability and governance controls. Ricardo teaches Machine Learning Engineering at FIAP. His published research on traceability and trust has accumulated more than 100 citations, and his Azure Samples contributions (Holiday Peak Hub, Tutor, and Tayra) demonstrate production-grade AI patterns.

作者簡介(中文翻譯)

Ricardo Cataldi 是 Microsoft AI、Apps 與 Agents 全球菁英團隊（Global Black Belt，GBB）的資深解決方案工程師，負責推動企業 AI 計畫，涵蓋從探索到正式上線的完整流程。他擁有 Applied Economics 碩士學位（UFRGS），並曾於 Mathematics 領域進行研究所進修（USP），具備扎實且嚴謹的分析基礎，致力於企業 AI 的交付。

在 Microsoft 任職期間，Ricardo 曾為銀行、教育、零售與電信業客戶交付 AI 解決方案。他負責從探索到正式上線的完整交付流程，將 Machine Learning Engineering 管線導入工業化流程，並建構具備可靠性與治理控制機制的多代理（multi-agent）工作流程。

Ricardo 也在 FIAP 教授 Machine Learning Engineering。他發表的可追溯性與信任相關研究已累積超過 100 次引用；此外，他對 Azure Samples 的貢獻（Holiday Peak Hub、Tutor 與 Tayra）也展現了可用於正式生產環境的 AI 模式。

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