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Microsoft Foundry for Enterprise AI Engineering
Paperback
ISBN13: 9785604390443
Publisher: Devlin Nexley
Published: Sep 26 2026
Pages: 188
Weight: 0.57
Height: 0.40 Width: 6.00 Depth: 9.00
Language: English
Microsoft Foundry represents a shift in enterprise AI from isolated model usage to integrated systems of reasoning, retrieval, and execution. It consolidates foundation models, agent frameworks, knowledge grounding, orchestration tools, and governance controls into a unified platform for building production-grade AI systems on Azure.
This book provides a technical, architecture-focused examination of Microsoft Foundry from the perspective of enterprise system design. It is intended for AI engineers, cloud architects, solution architects, and technical decision-makers responsible for designing, deploying, and operating AI systems at scale.
Rather than focusing on UI workflows or introductory tutorials, the content is structured around real-world engineering problems: how to design reliable AI agents, how to build grounded knowledge systems, how to manage model diversity, and how to operationalize AI within governed enterprise environments.
The book begins by establishing architectural foundations for enterprise AI workloads, including system boundaries, identity design, and multi-environment strategies. It then explores model ecosystems, evaluation frameworks, and selection methodologies for production use cases where latency, cost, and accuracy must be balanced.
A significant portion of the book is dedicated to retrieval-augmented generation systems and enterprise knowledge grounding. It examines how structured and unstructured data sources are transformed into retrieval layers that support deterministic and context-aware reasoning.
The book then transitions into agent engineering, covering single-agent and multi-agent systems, tool integration patterns, workflow orchestration, and state management for long-running intelligent processes. These architectures are framed in the context of enterprise constraints such as security, compliance, and operational reliability.
Security and governance are treated as first-class architectural concerns rather than deployment add-ons. The book explores identity-aware agents, prompt and context security boundaries, policy enforcement models, and responsible AI frameworks required in regulated environments.
Operational concerns such as observability, evaluation pipelines, performance monitoring, and cost optimization are also addressed, with emphasis on maintaining system reliability under production workloads.
Finally, the book presents enterprise solution blueprints across key domains including knowledge assistants, customer service ecosystems, operational automation platforms, and decision intelligence systems. These patterns demonstrate how Microsoft Foundry components can be composed into scalable, production-ready AI systems.
By the end of the book, readers will understand how to design and operate enterprise AI systems that extend beyond chat-based interfaces into fully governed, agent-driven architectures capable of executing business processes, augmenting decision-making, and integrating deeply with enterprise data ecosystems.
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