Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Multi-Agent AI Systems Handbook: Design, Build, and Deploy Production-Ready Autonomous Agents Master RAG, LLMOps, and Responsible AI for Scalable Real

Multi-Agent AI Systems Handbook: Design, Build, and Deploy Production-Ready Autonomous Agents Master RAG, LLMOps, and Responsible AI for Scalable Real

Norman, Raymond

Paperback

General Computers

ISBN13: 9798195882440
Publisher: Independently Published
Published: May 6 2026
Pages: 338
Weight: 1.73
Height: 0.70 Width: 8.50 Depth: 11.00
Language: English
Are you tired of AI demos that work in tutorials-but fail the moment you try to deploy them in the real world?

Do your agents break under pressure, spiral into unpredictable loops, or quietly drain your budget without delivering results?

You're not alone. The gap between building AI systems and deploying production-ready agents that actually work is where most developers get stuck.

This book closes that gap.

Multi-Agent AI Systems Handbook: Design, Build, and Deploy Production-Ready Autonomous Agents is your complete, practical guide to engineering intelligent, scalable AI systems that move beyond experimentation into reliable, real-world performance.

Inside, you'll learn how to design and implement multi-agent architectures where autonomous agents collaborate, coordinate, and execute complex workflows with precision. From foundational concepts to advanced production strategies, this book equips you with the tools and frameworks needed to build systems that are not only powerful-but stable, observable, and cost-efficient.

What You'll Learn

  • How to design multi-agent AI systems that scale across real-world applications
  • Build and deploy autonomous agents that execute tasks reliably without constant supervision
  • Master Retrieval-Augmented Generation (RAG) to give your agents accurate, context-aware intelligence
  • Implement LLMOps and AgentOps practices for monitoring, debugging, and optimizing performance
  • Prevent failures with fault-tolerant architectures, circuit breakers, and guardrails
  • Design AI workflows and orchestration systems for complex, multi-step automation
  • Apply Responsible AI and governance principles to ensure compliance, transparency, and trust
  • Integrate modern frameworks like LangGraph, CrewAI, and AutoGen into production-ready systems
From Prototype to Production

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