Beyond LLMs: Learn how to design reliable AI systems with memory, agents, planning, control, and evaluation 1st Edition, Kindle Edition

Build AI systems that move beyond LLM demos by combining memory, planning, control, and evaluation to create reliable, production-ready intelligenceKey FeaturesDesign AI systems with memory and control instead of relying on prompts aloneMeasure reliability, safety, cost, and drift with evaluation and monitoring patternsBuild and evolve a production-style AI project across the book in PythonBook DescriptionArtificial Intelligence (AI) has moved very quickly in recent years, and large language models (LLMs) have opened the door for teams to build impressive prototypes in a short space of time. However, many of these systems struggle when they need to operate reliably in real-world settings. Prompting alone is often not enough when the goal is consistency, control, observability, and long-term performance. Teams now need to think beyond single model calls and start designing AI as a system made up of multiple working parts.Beyond LLMs provides a practical way to understand and build the next generation of AI systems using Python. This book introduces you to the core ideas that sit beyond basic LLM usage, including memory, reasoning loops, planning, agents, control mechanisms, evaluation, monitoring, and hybrid constraints.Using a recurring SupportOps AI project across the book, you will see how these capabilities fit together in a structured system rather than as isolated techniques. You will learn how to design modular components, measure reliability and drift, handle failure modes, and make safer update decisions as the system evolves.By the end of the book, you will be able to move from prompt-driven demos to more reliable, production-ready AI systems that are easier to test, monitor, and improve.What you will learnUnderstand AI system design beyond single model callsBuild memory and retrieval for continuity and response qualityCreate reasoning and planning loops for reliable task executionLearn how agents use bounded actions and control policiesMeasure reliability, safety, cost, and drift with evaluationHandle failures and update AI systems with safer rolloutsApply hybrid rules and constraints to improve consistencyWho this book is forThis book is for AI engineers, data scientists, ML engineers, technical leads, and ambitious builders who want to move beyond prompt-based prototypes and design reliable AI systems. If you already understand the basics of LLMs and want to take your AI work to the next level, this book will help you build stronger skills so you can create systems that are easier to test, observe, scale, and improve in real-world production environments.It is also valuable for students who want to build strong foundations in AI system design and stay ahead as the field evolvesTable of ContentsBeyond LLM Centrism, Intelligence as a SystemAgents That Do Not Break, Planning and ControlMemory, World Models, and LearningFrom Pipelines to AgentsPlanning, Search, and Decision LoopsCoordinating Multiple AgentsMemory as a First-Class System ComponentWorld Models and Internal RepresentationsContinual Learning in Production SystemsNeuro-Symbolic and Hybrid ArchitecturesProduction Trade-Offs and System DesignWhat Comes Next Read more