The Age of AI Agents: Architectures, Tools, Memory, and Reliable Agent Systems

Most AI agent tutorials stop at "hello world." This book takes you to production. The hype around AI agents is everywhere, but the engineering guidance is scarce. The Age of AI Agents fills that gap with battle-tested patterns for building agents that work reliably in the real world, not just in demos. This isn't a prompt engineering cookbook. It's a rigorous treatment of the architectures, algorithms, and operational practices that separate toy prototypes from deployed systems. You'll learn when to use agents and when simpler approaches win), how to design for failure, and why most "autonomous" systems should actually be automated workflows with human oversight. What's inside:Foundations: The PEAS framework, MDPs, Bellman equations, and the minimal agent loopArchitectures: ReAct, tool-augmented LLMs, planner-executor patterns, and when each appliesReinforcement learning: Q-learning, policy gradients, PPO, and the RL-warranted scorecardPlanning: A* search, MCTS, hierarchical task networks, and budget-aware replanningTool use: Function calling, scoped approval grants, and safe execution sandboxesMemory systems: Episodic, semantic, and working memory for long-running agentsMulti-agent vs. workflows: When coordination helps and when it's unnecessary complexityEvaluation: Shadow deployments, canary rollouts, Wilson intervals, and offline policy evaluation Who this book is for:Software engineers and ML practitioners building LLM-powered systems who want to move beyond chains and prompts to true agent architectures. Assumes familiarity with Python and basic machine learning concepts.Practical, tested, production-ready:Every algorithm comes with working code. The companion repository includes 77 tested Python examplescovering bandits, planning algorithms, memory implementations, and evaluation pipelines, all with type hints. frozen dataclasses, and production-grade patterns. Book 5 in The AI Engineer's Library series. Each book stands alone while building toward mastery of modern AI systems. Read more