Build production LLM and agentic AI systems using modern patterns for context engineering, memory, loops, harnesses, RAG, tools, MCP, observability, security, governance, and reliable deployment.
Key Features
- Master modern LLM patterns for context engineering, loops, memory, harnesses, RAG, tools, and agents
- Apply evaluation, observability, security, governance, and cost controls across production AI
- Learn by building one evolving product capability per chapter, ending with an integrated capstone
Book Description
Building an LLM application is easy; making it reliable, secure, observable, and cost-effective in production is far harder. This book presents a practical pattern language for LLM and agentic AI systems through a PolicyOps project. You'll begin with LLM architecture and evaluation-driven development, then compare adaptation strategies and optimize test-time reasoning, latency, cost, and quality. Next, you'll engineer context, build advanced and agentic RAG pipelines, integrate Graph RAG, and add governed long-term memory. Each chapter adds one production component or concern, including model boundaries, evaluation gates, context planning, retrieval, memory, secure extensions, agent loops, observability, deployment, and release review. You'll extend the system with typed tools, MCP, Agent Skills, plugins, and hooks; design single-agent, multi-agent, browser, and computer-use workflows; and add durable harnesses, LLMOps, security, governance, and human oversight. The capstone unifies these patterns in a governed, observable, secure knowledge-and-action agent, with examples from healthcare, finance, customer support, and software engineering. By the end of the book, you'll be able to design, secure, deploy, and operate production LLM systems against quality, cost, privacy, safety, reliability, and autonomy requirements.
What you will learn
- Build production LLM and agentic AI systems with reusable design patterns
- Engineer context, retrieval-augmented generation, agentic RAG, and Graph RAG
- Add AI memory, tool use, Model Context Protocol, Agent Skills, plugins, and hooks
- Design reliable AI agents, multi-agent workflows, harnesses, and execution loops
- Apply LLM evaluation, LLMOps, observability, and cost optimization
- Secure AI applications with guardrails, governance, and human oversight
- Deploy scalable, resilient, production-ready LLM systems
Who this book is for
This book is for AI engineers, machine learning engineers, software engineers, platform engineers, security engineers, solutions architects, and technical leads who build or operate LLM and agentic AI applications. It is especially useful for practitioners moving from demos to production or redesigning brittle RAG and agent systems.. Readers should know Python, APIs, Git, containers, and basic cloud concepts. Familiarity with LLMs, embeddings, and retrieval is helpful, but each pattern is introduced before it is applied.
