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Stateful AI Engineering: Building Memory, Context, and Intelligent Agent Systems
Paperback
Publisher: Independently Published
Published: May 24 2026
Pages: 262
Weight: 0.78
Height: 0.55 Width: 6.00 Depth: 9.00
Language: English
As AI systems evolve from static large language model applications into autonomous, multi-step agent systems, the real engineering challenge is no longer just model usage, but state management, memory design, context orchestration, and long-running intelligent behavior. This book provides a structured, production-oriented approach to solving that challenge.
What You Will Learn
Inside this book, you will learn how to:
Design stateful AI architectures that maintain persistent memory across sessions
Build context engineering pipelines that improve reasoning quality and stability
Implement agent-based systems using structured workflows and graph-based execution models
Develop durable memory systems using event sourcing and long-term storage strategies
Architect retrieval systems that combine vector search, memory ranking, and contextual relevance
Deploy production-ready AI systems that scale across distributed infrastructure
Optimize performance, latency, and cost in real-world AI applications
Design self-improving systems that incorporate feedback loops and adaptive behavior
Who This Book Is For
This book is designed for:
AI engineers and machine learning practitioners
Software engineers building AI-powered applications
Data scientists transitioning into AI system design
Backend engineers working on LLM-based products
Advanced learners interested in agentic AI systems and architecture
A solid understanding of Python and basic machine learning concepts is recommended.
What Makes This Book Different
Most AI resources focus on using models. This book focuses on engineering intelligence systems.
Instead of treating AI as isolated prompts or APIs, this book teaches you how to build:
persistent memory systems that survive across sessions and failures
multi-agent architectures that coordinate complex reasoning tasks
graph-based execution systems for adaptive workflows
scalable backend infrastructures for production AI deployments
optimization strategies for real-world performance and cost constraints
It bridges the gap between LLM experimentation and real AI system engineering.
A Systems-Level Approach to Modern AI
Through detailed explanations, architectural breakdowns, and practical Python implementations, this book walks you through building AI systems that behave less like tools and more like continuous intelligent systems.
You will move from:
simple prompt engineering → to structured AI architecture design → to production-grade intelligent systems engineering
Conclusion
Stateful AI Engineering is a foundational guide for engineers who want to move beyond experimentation and into building real-world AI systems with memory, structure, and long-term intelligence.
If you are serious about designing the next generation of AI applications, this book provides the blueprint.
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