Event-Driven Systems with Microsoft AI

This book will be a practical, end-to-end guide to designing and building event-driven systems on Microsoft Azure, with a strong focus on real-world APIs and carefully chosen AI integrations. Instead of starting from abstract patterns, the book follows a concrete case study-a food ordering and delivery platform-built step by step using Azure Service Bus, Azure Functions, Azure Communication Services, Azure Container Apps, Key Vault, Application Insights, and Azure OpenAI or compatible AI services.

These topics are especially important now for several reasons. Modern systems are increasingly distributed, integrated, and real-time: users expect instant notifications, businesses integrate with partners across tenants, and services must stay responsive even under heavy load or partial failures. Event-driven design is one of the few approaches that addresses scalability, resilience, and decoupling at the same time-but many teams still struggle to move from simple REST APIs and create jobs to robust event pipelines. At the same time, AI is moving from "nice demo" to "must-have," yet most guidance focuses on chatbots, not on how to use AI to help operations teams understand and control complex event flows.

This book aims to fill that gap. It shows everyday cloud developers how to evolve from a single API to a production-style event-driven system, how to make responsible use of AI for summaries, anomaly hints, and decision support, and how to keep everything observable, secure, and cost-aware. While the tools are Azure-native, the patterns-events before endpoints, contracts and versioning, sagas and compensations, DLQ handling, back-pressure, and AI-assisted operations-are portable to any modern cloud platform. The goal is that readers finish with a working blueprint they can adapt directly in their own organizations, not just theory.

What You Will Learn

* Learn how to model business flows as domain events, design robust event contracts, and evolve them safely over time without breaking consumers.

* Use Azure Service Bus, Azure Functions, and Container-based APIs together to implement reliable, loosely coupled workflows such as user registration, order processing, and notification pipelines.

* Integrate Azure OpenAI (or compatible models) into event flows for tasks like summarizing operational data, prioritizing work, detecting anomalies, and enhancing customer communication-without turning the system into an expensive "AI toy.

* Implement patterns like outbox, retries, dead-letter queues, idempotent consumers, and back-pressure so the system keeps working under real-world load and partial outages.

* Protect secrets with Key Vault, use managed identities and role-based access control correctly, and design multi-tenant and cross-team boundaries for larger organizations.

* Use Application Insights and log queries to trace an order from start to finish, set meaningful SLOs, tune performance and cost, and automate deployments with GitHub Actions and infrastructure-as-code.