Enterprise AI Agents in C# and .NET: Build Scalable, Autonomous AI Solutions for the Microsoft Ecosystem
Enterprise AI work begins where a convincing demo stops. Building an agent that can reason over a request is one challenge; building a dependable .NET service that respects identity, authorization, privacy, reliability, and audit requirements is another.
This book shows you how to design and implement governed, production-ready AI agents for enterprise Microsoft environments. You will build a cumulative C# and .NET service-desk solution that turns model capabilities into bounded, observable application behavior.
Designed for intermediate .NET developers, software architects, and enterprise IT professionals, the book assumes practical experience with C#, asynchronous programming, dependency injection, ASP.NET Core, HTTP APIs, configuration, logging, and automated testing. Familiarity with language models, embeddings, databases, containers, and distributed systems is helpful.
You will learn how to create provider-independent agent contracts and bounded execution loops; orchestrate prompts, plugins, structured output, cloud models, and local models with Semantic Kernel; design tools that validate arguments, enforce authorization, protect tenants, audit actions, and prevent duplicate side effects; and separate execution state, conversation history, durable memory, business records, and retrieved knowledge.
The implementation progresses through enterprise retrieval pipelines with citations, controlled workflows with approvals and resumable state, reliability guardrails, prompt-injection defenses, multi-agent coordination, long-running ASP.NET Core and worker hosting, observability, evaluation, testing, container deployment, scaling, and production operations.
Examples are project-based and architecture-focused, with typed contracts, failure handling, concurrency controls, cancellation, deadlines, budgets, idempotency, reconciliation, and privacy-safe telemetry. The emphasis is not maximum autonomy, but dependable assistance and carefully delegated execution.
By the end, you will have a practical foundation for building AI agent systems that fit established .NET applications, security models, operational practices, and governance requirements.
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