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Principles Of Building AI Agents: The Only Guide You'll Ever Need to Design, Build and Deploy Autonomous Agents with LLMs
Paperback
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Publisher: Independently Published
Published: Oct 1 2025
Pages: 264
Weight: 1.02
Height: 0.55 Width: 7.00 Depth: 10.00
Language: English
Principles of Building AI Agents is a definitive guide for anyone looking to design, build, and deploy autonomous agents powered by large language models (LLMs). This book provides a structured, hands-on approach to understanding the mechanics, architecture, and practical applications of agentic AI, bridging the gap between theory and real-world implementation.
The book covers core principles of AI agents, from understanding reasoning loops and memory systems to implementing tool usage, multi-agent collaboration, and robust safety measures. Detailed examples, step-by-step explanations, and practical exercises ensure that concepts can be applied directly to real projects, whether building research assistants, collaborative content agents, or intelligent workflow automation.
Guidance is provided on connecting agents to tools, APIs, and memory systems for practical applications, while also exploring advanced techniques like hierarchical planning, adaptive strategies, and multi-agent orchestration. Practical instruction for testing, deploying, and scaling agents in production is included, along with strategies for safety, trust, and human-in-the-loop design to ensure responsible AI deployment.
The unique strength of this book lies in its practical, mentor-like approach. Every concept is broken down in a pragmatic and accessible way with real-world examples and actionable steps, making complex agentic AI achievable. Its focus on real deployment, scaling, and continuous improvement sets it apart from other AI books that stop at theory.
Transform knowledge into action and unlock the potential of autonomous agents to solve real-world problems, streamline processes, and amplify human productivity.
Take the next step toward mastering AI agents and start building smarter, autonomous systems today.
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