Building Intelligent AI Agents for Enterprise Systems: How to design production-ready AI agents using Microsoft Foundry to automate decisions, integrate APIs, eliminate repetitive business tasks - Softcover

Phillips, Jefferson C

9798183614442: Building Intelligent AI Agents for Enterprise Systems: How to design production-ready AI agents using Microsoft Foundry to automate decisions, integrate APIs, eliminate repetitive business tasks

Softcover

ISBN 13: 9798183614442

Publisher: Independently published, 2026

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Most organizations are investing in AI, but very few are successfully turning it into reliable, production-ready systems. Chatbots fail in long workflows, APIs are misused by poorly controlled agents, costs spiral without clear visibility, and security risks emerge when systems are left without proper governance. The gap is no longer about whether AI works—it is about whether it can be engineered to work consistently at enterprise scale.

Building Intelligent AI Agents for Enterprise Systems addresses this challenge head-on by showing how to design, build, and operate production-grade AI agents using Microsoft Foundry. The book focuses on real engineering problems: how agents make decisions, how they execute tools, how they integrate with enterprise APIs, and how they are controlled in secure, observable environments. Instead of experimental setups, you will learn how to build systems that can be trusted in real business operations.

Across practical architectures and implementation-focused explanations, the book shows how to design AI agents that automate multi-step decisions, coordinate workflows, interact safely with external systems, and reduce repetitive operational work. It emphasizes the full lifecycle of enterprise AI systems—from execution design and memory management to monitoring, evaluation, and governance.

Readers will gain the ability to design and implement:

Production-ready AI agent architectures using Microsoft Foundry
Reliable tool execution systems with structured API orchestration
Memory and context strategies that prevent reasoning breakdowns
Multi-step workflow automation for real enterprise processes
Evaluation and monitoring systems for cost, latency, and accuracy control
Security and governance mechanisms including access control and audit logging
Debugging methods for identifying and fixing agent failures in production

By the end of the book, readers will be able to move beyond experimental AI prototypes and build systems that operate with stability, transparency, and measurable business value in real enterprise environments.

If you are ready to design AI systems that do more than respond—and instead execute, coordinate, and automate real work at scale—this book gives you the engineering foundation to build them with confidence.

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