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Agentic AI Engineering with Microsoft Foundry: Design, Build, Evaluate, Monitor, and Deploy Enterprise AI Agents That Solve Real Business Problems
Paperback
Series: AI Agents Made Easy from Scratch
Publisher: Independently Published
Published: May 25 2026
Pages: 226
Weight: 0.88
Height: 0.48 Width: 7.00 Depth: 10.00
Language: English
Agentic AI Engineering with Microsoft Foundry: Design, Build, Evaluate, Monitor, and Deploy Enterprise AI Agents That Solve Real Business Problems
Most AI projects fail before they reach production. Teams struggle with hallucinations, unreliable outputs, and agents that can't handle real-world complexity. You've seen the demos-chatbots that sound impressive but collapse under actual business logic, tools that promise automation but require constant human intervention, and systems that work in controlled environments but break when deployed at scale.
What if you could build AI agents that actually work?
This book gives you the complete engineering framework for creating reliable, production-ready AI agents using Microsoft Azure AI Foundry. You'll move beyond prompt engineering and ChatGPT experiments to build multi-agent systems that integrate with your enterprise infrastructure, handle complex workflows, and deliver measurable business value.
Here's what makes this different: You get practical, hands-on guidance grounded in real engineering principles, not theoretical overviews or marketing fluff. Every chapter walks you through specific implementation patterns, from designing agent architectures and managing context windows to implementing retrieval-augmented generation (RAG), running systematic evaluations, and deploying agents that scale. You'll work with Azure AI Search, Prompt Flow, Content Safety tools, and the full Microsoft Foundry stack through clear examples and working code.
What you'll learn:
- Agent architecture patterns for single-agent and multi-agent systems that handle real business complexity
- RAG implementation with vector databases, semantic chunking, and hybrid search strategies
- Evaluation frameworks to measure accuracy, relevance, groundedness, and safety before deployment
- Prompt engineering techniques that reduce hallucinations and improve response quality
- Monitoring and observability to track agent performance, token usage, and cost in production
- Enterprise integration with authentication, data governance, and compliance requirements
- Complete capstone project building a customer support agent from design through deployment
You'll build three major projects: a document analysis agent, a code generation assistant, and a full customer service system with escalation logic. Each project includes architecture diagrams, implementation code, evaluation metrics, and deployment configurations you can adapt for your own use cases.
This book is for: Software engineers, data scientists, solution architects, and technical leads who need to move AI from proof-of-concept to production. Whether you're building your first agent or scaling existing AI systems, you'll find practical patterns and proven approaches that work in enterprise environments.
Stop building AI that breaks in production. Start engineering agents that deliver results. Get your copy today and transform how your organization builds with AI.
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