Beyond LLMs: The Next Frontier of AI

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, ML engineers, data scientists, software engineers, technical leads, and advanced students who want to move beyond prompt-based LLM prototypes and build reliable, production-ready AI systems. Readers should be comfortable with Python and understand basic machine learning concepts, including training, inference, evaluation, and LLM behavior. Prior experience with agentic AI, world models, neuro-symbolic AI, or production AI architecture is not required.