AI Agent System Design

<p><strong>AI Agent System Design: How to Build LLM Applications Architect LLM Agents and Engineer Real-World AI Systems</strong><br>By Julian F. Faraday</p><p>AI is moving faster than any technology in history - but most teams are still stuck building <em>demos</em> instead of <em>deployable systems</em>. This book changes that.</p><p><strong>AI Agent System Design</strong> is the first end-to-end engineering-focused guide that teaches you how to build real LLM applications architect reliable agent systems and design production-ready AI workflows that scale. Whether you're an engineer architect technical product lead or founder this book shows you how to move from prototypes that impress... to systems that endure.</p><p>While other books stay high-level or theoretical this one is unapologetically practical. You'll learn how modern AI systems actually work - across models data architecture RAG pipelines tools APIs function calling safety layers orchestration frameworks agents evaluation observability performance and deployment.</p><p>Built on real engineering lessons modern patterns and field-tested architectures this is your blueprint for building AI that works in the real world.</p><p><strong>What You Will Learn</strong></p><p><strong>Design AI systems that are fast safe and scalable</strong><br>Move from naïve prompt hacks to reliable interfaces schemas guardrails and orchestration pipelines used by professional AI teams.</p><p><strong>Build LLM agents without the hype</strong><br>Understand what an agent truly is how agent loops work when memory helps (and when it doesn't) and how to avoid runaway unstable or unpredictable behaviors.</p><p><strong>Make RAG actually work</strong><br>Chunking indexing metadata reranking hybrid search evidence routing evaluation - a complete RAG engineering playbook.</p><p><strong>Engineer real-world tools and function calling</strong><br>Implement planning multi-step reasoning multi-tool pipelines and agent-tool collaboration with correctness guarantees.</p><p><strong>Master evaluation beyond it feels good</strong><br>Learn measurable metrics test harnesses AI-as-a-judge systems stress testing edge-case validation and regression protocols.</p><p><strong>Scale for performance cost and reliability</strong><br>Latency patterns inference optimization caching systems routing strategies load balancing and cost engineering.</p><p><strong>Deploy and operate production-grade AI systems</strong><br>Monitoring logging drift detection governance model versioning and continuous improvement workflows.</p><p> </p><p><strong>This book is designed for:</strong></p><p><strong>AI Engineers & ML Engineers</strong></p><p><strong>Software Engineers</strong></p><p><strong>Product Leaders & Founders</strong></p><p><strong>Technical Architects & Researchers</strong></p><p>If you want to build <strong>LLM applications that don't collapse under real users real data or real scale</strong> this book will become your most-used reference.</p><p><strong>A New Standard for AI Engineering Books</strong></p><p>• End-to-end architecture patterns<br>• Real examples and case studies<br>• Failure modes and how to avoid them<br>• Practical micro-modules at the end of every chapter<br>• A complete library of agent and system design patterns</p><p>This is not a conceptual overview or an academic text.<br>This is a <strong>hands-on engineering guide</strong> for people who want to build.</p><p><strong>Stop building demos. Start engineering systems.</strong></p><p>If you're ready to build the next generation of intelligent applications - not just talk about them - this book is your blueprint.</p>