Books

Scrum made human teams 10× faster. Now it's doing the same for AI agents. Here's the architecture.

Your AI coding agent starts every session knowing nothing about your project, so it guesses - and the guess is sometimes expensive. The AGENTS.md Cookbook shows you how to write short, specific rules files, how to test them with a simple before-and-after method, and includes twelve ready-to-adapt recipes for real project types, labeled honestly about what has and has not been tested.

Build AI agents that wake up, detect problems, and take action without waiting for a prompt. Through illustrated architectures, six practical use cases, and a runnable Python project, learn how heartbeat systems turn autonomous behavior into reliable automation and how to build a business around it.

What changes when an AI assistant can take action? Follow a fictional enterprise team as it works through permissions, prompt injection, testing, monitoring, and governance, using practical diagrams, published cases, and exercises to design and operate agents with clear boundaries

The code looks right. The agent sounds sure. Then it breaks. Learn the 50 most common ways AI-built apps fail, how to spot each one early, and how to fix it before your users find out.

What happens when AI moves beyond answering questions and begins to pursue goals?Agentic AI: The Rise of Autonomous, Goal-Driven Intelligence explores the emerging world of intelligent systems that can perceive, reason, plan, learn, collaborate, and act within defined environments.From the foundations of autonomous agents and multi-agent systems to robotics, finance, logistics, security, ethics, and future research, this book provides a structured journey through the rapidly evolving landscape of Agentic AI.Discover how goal-driven intelligence works, how autonomous agents make decisions, how multiple agents collaborate, and why safety, trust, accountability, and human oversight are essential to the future of autonomous systems.Understand the agents. Explore the architectures. Examine the applications. Think about the future.

The second half of a from-scratch AI/ML course that refuses to skip the hard parts: LSTMs, real attention, a working transformer, LoRA fine-tuning derived and measured, and a genuine multi-tool, multi-agent system — every mechanism built by hand, then checked against PyTorch and real numbers.

Building a model that performs well on technical reasoning tasks requires more than scaling up training data. It requires deliberate control over problem difficulty, data quality, feedback signals, verification, and the progression from foundational skills to complex multi-step tasks.Advancing Technical AI via Curriculum Reasoning is a 119-page engineering blueprint for designing, training, and evaluating reasoning-focused language models. It presents a structured, 48-step workflow that connects curriculum learning with synthetic data generation, process supervision, automated verification, preference optimization, and evaluation across demanding technical domains.

What happens when AI agents stop working alone and start working as a team?Artificial Intelligence is entering a new phase in which intelligent systems can do more than respond to individual instructions. Multiple specialized agents can collaborate, divide complex tasks, communicate with one another, use tools, evaluate results, and coordinate their actions toward a shared objective.Multi-Agent AI Systems: The Complete Handbook for Building Intelligent, Scalable, and Autonomous Agent Teams provides a practical roadmap for understanding this emerging paradigm.The book begins with the fundamentals of multi-agent systems and explains why collaboration between specialized agents can be valuable for complex workflows. Readers will learn about hierarchical, peer-to-peer, and hybrid architectures, along with roles such as manager, planner, worker, critic, and supervisor agents.It then moves into modern frameworks and technologies, including CrewAI, AutoGen, LangGraph, MetaGPT, LLMs, vector databases, and agent memory systems. Practical chapters explain how to design agent teams, decompose tasks, establish communication protocols, manage shared memory, coordinate workflows, integrate tools and APIs, and recover from failures.Readers will also explore advanced concepts such as dynamic replanning, parallel execution, swarm intelligence, agent debates, self-organizing systems, human-in-the-loop workflows, and multimodal agents.The book goes beyond experimentation and addresses the challenges of deploying multi-agent systems in real environments. Cloud deployment, Docker, Kubernetes, monitoring, logging, scaling, evaluation, benchmarking, testing, and cost optimization are included.Real-world applications demonstrate how agent teams can support software development, research, customer service, content creation, and business operations.Equally important, the book examines AI safety, privacy, security, transparency, governance, alignment, and responsible AI development.Whether you are a student discovering agentic AI, a developer building your first agent team, a researcher exploring collaborative intelligence, or a professional preparing for the next generation of AI applications, this book provides a foundation for moving from individual AI agents toward coordinated intelligent systems.Understand the architecture. Design the team. Build the agents. Coordinate the intelligence.

La IA agéntica ya no es el futuro. Es el motor de la transformación hoy.¿Qué pasaría si la inteligencia artificial pudiera percibir, razonar, planificar, aprender y actuar para resolver problemas reales de tu organización?La IA Agéntica Aplicada a la Transformación Empresarial y Gubernamental te lleva de los fundamentos a la implementación, explorando arquitecturas, patrones de diseño, memoria, RAG, sistemas multiagente, herramientas, casos reales y gobernanza.Una guía para quienes quieren dejar de experimentar con IA y comenzar a construir agentes que trabajen, decidan y generen valor.De la teoría a la práctica. De la automatización a la transformación.

Domine a arquitetura de sistemas de IA autônomos. Um guia completo cobrindo motores de raciocínio, memória de longo prazo, orquestração multiagentes, otimização de custos e o Model Context Protocol (MCP). O livro definitivo para arquitetos e desenvolvedores.

Vectors, embeddings, retrieval, agents, and evaluation are all built from first principles inside the chapter that needs them. No mathematics. No machine learning background. No prior AI experience and no framework knowledge is required. We build with plain Python and small, single-purpose libraries.  

Go beyond using AI tools—learn to build AI solutions. AI Generalist Engineer is a practical guide for software engineers, architects, and technology leaders to master enterprise AI, AI agents, RAG, MCP, prompt engineering, orchestration, and modern AI architecture. Build the skills to design, integrate, and lead AI-powered systems with confidence.

Master automated workflows for your Python code with GitHub Actions for Python Projects. This practical guide covers automated testing, dependency management, coverage reports, documentation builds, and artifact generation to help you build reliable CI/CD pipelines effortlessly.

This book is a workshop where things go wrong on the page, in front of you, and then get fixed in code, with the output that proves the fix worked. There's no need to be a security expert to follow along. We'll be moving from the interpreter to the supply chain, from the token to the human clicking Allow, and from a single tool to a whole host of untrusting servers.