Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • 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
Agentic Design Patterns Made Simple: A Practical Guide to Building Smarter AI Systems

Agentic Design Patterns Made Simple: A Practical Guide to Building Smarter AI Systems

Everett, Mason J.

Paperback

Programming

ISBN13: 9798271633911
Publisher: Independently Published
Published: Oct 26 2025
Pages: 342
Weight: 1.01
Height: 0.71 Width: 6.00 Depth: 9.00
Language: English
Artificial intelligence has entered a new phase - one where systems no longer just respond but act, adapt, and collaborate on their own. Agentic Design Patterns Made Simple: A Practical Guide to Building Smarter AI Systems gives you the knowledge and structure to design these next-generation intelligent systems with clarity and confidence.

This book is a complete guide to understanding and applying agentic design patterns - reusable frameworks that help AI agents think, plan, and execute tasks autonomously. From the fundamentals of tool use and prompt chaining to advanced multi-agent collaboration and adaptive behavior, each chapter bridges theory with hands-on practice.

You'll move through the entire architecture of intelligent agent design - understanding how agents perceive their environment, make informed decisions, delegate complex tasks, and improve through feedback. With clear explanations, real-world examples, and guided exercises, this book turns technical complexity into practical steps anyone can follow.

Inside, you'll learn how to:

  • Build structured, scalable AI agents using modern frameworks such as LangChain and LangGraph.
  • Design workflows where multiple agents work together efficiently.
  • Apply reinforcement learning principles to create adaptive and goal-driven systems.
  • Implement responsible and ethical AI design that prioritizes fairness, privacy, and accountability.
  • Evaluate, test, and maintain large AI systems with confidence and precision.

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