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

Ricardo Cataldi is a Global Black Belt (GBB) Solution Engineer at Microsoft, specializing in AI Apps and Agents. He covers enterprise customers across the Americas, designing and reviewing production AI agent systems running on Azure. He is also Academic Coordinator for Machine Learning Engineering at FIAP (Faculdade de Informática e Administração Paulista), one of Brazil's leading technology universities, where he built and teaches the graduate MLEng curriculum covering distributed AI systems, MLOps, and production agent architecture. He holds M.Sc. degrees in Applied Mathematics (USP, 2018) and Applied Economics (UFRGS, 2014), a specialization from IMPA, and a B.Sc. in Computer Science (Mackenzie). He is lead contributor to Azure Samples projects: Holiday Peak Hub (21-agent retail system), Tayra (GenAI call center analytics on Azure AI Foundry), and Tutor (AI speech avatar tutoring). He has influenced US$180M+ in cloud revenue through AI architecture consulting.