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AI Agent Context Engineering: The Developer's Guide to Designing, Building, and Deploying Autonomous AI Agents
Build AI Agents That Actually Work, Not Just Chatbots
If you’ve ever tried to build an autonomous AI agent, and watched it hallucinate, freeze, or crash, you already know the the problem isn’t the model. It’s the architecture. This book shows you how to fix that.
What This Book Allows You to DoThis guide gives developers a complete, practical roadmap for designing reliable, state-driven, tool-using AI agents that can plan, act, learn, and adapt. Instead of writing endless prompts, you’ll learn how to engineer the entire context system that makes autonomy possible.
About the TechnologyModern LLMs are powerful, but they aren’t autonomous out of the box. Through context engineering, you’ll learn to orchestrate constitutions, tool manifests, memory systems, RAG pipelines, and agentic control loops, transforming LLMs from reactive responders into goal-driven autonomous systems ready for real-world tasks.
Book SummaryIn clear, developer-friendly language, this book explains why traditional "prompt engineering" collapses in multi-step workflows, and replaces it with a robust architectural discipline for building production-grade agents. You’ll explore the context stack, understand system prompts as operating systems, and learn how to control an agent’s reasoning, memory, and decision-making.
Across 12 chapters, you’re guided through every layer of designing system prompts, generating structured tool use, building RAG-enabled memory pipelines, managing short- and long-term agent state, securing agents against prompt injection attacks, evaluating reliability, and deploying multi-agent teams capable of complex cooperation.
By the end, you’ll not only understand how agentic systems work, you’ll be able to build them with confidence.
What’s Inside This Book? (Key Benefits)Master the Context Stack system prompts, tasks, RAG, tool outputs, and historyDesign constitutional prompts that enforce rules, boundaries, and agent personaBuild complete ReAct and MDP agent loops with real tool executionImplement practical RAG pipelines including chunking, embedding, retrieval, hybrid search, and contextual filteringHandle real-world errors with self-correction, guarded tools, escalation, and human-in-the-loop patternsEngineer multi-agent systems with shared scratchpads, manager/worker hierarchies, and coordinated workflowsDeploy agents in production with evaluation frameworks, observability, cost tracking, and CI/CD integration
About the ReaderThis book is perfect for software engineers, AI developers, system designers, and technical founders who want to go beyond chatbots and build autonomous, reliable, production-ready AI systems. Whether you’re new to agents or already experimenting with ReAct loops and RAG pipelines, this guide gives you the architectural clarity you’ve been missing.
Ready to Build Agents That Don’t Break? Transform your understanding of LLMs and learn how to engineer real autonomy.
Get your copy today and start building next-generation AI agents the right way.
What This Book Allows You to DoThis guide gives developers a complete, practical roadmap for designing reliable, state-driven, tool-using AI agents that can plan, act, learn, and adapt. Instead of writing endless prompts, you’ll learn how to engineer the entire context system that makes autonomy possible.
About the TechnologyModern LLMs are powerful, but they aren’t autonomous out of the box. Through context engineering, you’ll learn to orchestrate constitutions, tool manifests, memory systems, RAG pipelines, and agentic control loops, transforming LLMs from reactive responders into goal-driven autonomous systems ready for real-world tasks.
Book SummaryIn clear, developer-friendly language, this book explains why traditional "prompt engineering" collapses in multi-step workflows, and replaces it with a robust architectural discipline for building production-grade agents. You’ll explore the context stack, understand system prompts as operating systems, and learn how to control an agent’s reasoning, memory, and decision-making.
Across 12 chapters, you’re guided through every layer of designing system prompts, generating structured tool use, building RAG-enabled memory pipelines, managing short- and long-term agent state, securing agents against prompt injection attacks, evaluating reliability, and deploying multi-agent teams capable of complex cooperation.
By the end, you’ll not only understand how agentic systems work, you’ll be able to build them with confidence.
What’s Inside This Book? (Key Benefits)Master the Context Stack system prompts, tasks, RAG, tool outputs, and historyDesign constitutional prompts that enforce rules, boundaries, and agent personaBuild complete ReAct and MDP agent loops with real tool executionImplement practical RAG pipelines including chunking, embedding, retrieval, hybrid search, and contextual filteringHandle real-world errors with self-correction, guarded tools, escalation, and human-in-the-loop patternsEngineer multi-agent systems with shared scratchpads, manager/worker hierarchies, and coordinated workflowsDeploy agents in production with evaluation frameworks, observability, cost tracking, and CI/CD integration
About the ReaderThis book is perfect for software engineers, AI developers, system designers, and technical founders who want to go beyond chatbots and build autonomous, reliable, production-ready AI systems. Whether you’re new to agents or already experimenting with ReAct loops and RAG pipelines, this guide gives you the architectural clarity you’ve been missing.
Ready to Build Agents That Don’t Break? Transform your understanding of LLMs and learn how to engineer real autonomy.
Get your copy today and start building next-generation AI agents the right way.
303 pages, Kindle Edition
Published November 17, 2025
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