AI Agent System Design: How to Build LLM Applications, Architect LLM Agents, and Engineer Real-World AI Systems - Tapa blanda

Sinopsis

AI Agent System Design: How to Build LLM Applications, Architect LLM Agents, and Engineer Real-World AI Systems
By Julian F. Faraday

AI is moving faster than any technology in history - but most teams are still stuck building demos instead of deployable systems. This book changes that.

AI Agent System Design 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.

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.

Built on real engineering lessons, modern patterns, and field-tested architectures, this is your blueprint for building AI that works in the real world.

What You Will Learn

Design AI systems that are fast, safe, and scalable
Move from naïve prompt hacks to reliable interfaces, schemas, guardrails, and orchestration pipelines used by professional AI teams.

Build LLM agents without the hype
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.

Make RAG actually work
Chunking, indexing, metadata, reranking, hybrid search, evidence routing, evaluation - a complete RAG engineering playbook.

Engineer real-world tools and function calling
Implement planning, multi-step reasoning, multi-tool pipelines, and agent-tool collaboration with correctness guarantees.

Master evaluation beyond "it feels good"
Learn measurable metrics, test harnesses, AI-as-a-judge systems, stress testing, edge-case validation, and regression protocols.

Scale for performance, cost, and reliability
Latency patterns, inference optimization, caching systems, routing strategies, load balancing, and cost engineering.

Deploy and operate production-grade AI systems
Monitoring, logging, drift detection, governance, model versioning, and continuous improvement workflows.

This book is designed for:

AI Engineers & ML Engineers

Software Engineers

Product Leaders & Founders

Technical Architects & Researchers

If you want to build LLM applications that don't collapse under real users, real data, or real scale, this book will become your most-used reference.

A New Standard for AI Engineering Books

- End-to-end architecture patterns
- Real examples and case studies
- Failure modes and how to avoid them
- Practical micro-modules at the end of every chapter
- A complete library of agent and system design patterns

This is not a conceptual overview or an academic text.
This is a hands-on engineering guide for people who want to build.

Stop building demos. Start engineering systems.

If you're ready to build the next generation of intelligent applications - not just talk about them - this book is your blueprint.

"Sinopsis" puede pertenecer a otra edición de este libro.