Artificial Intelligence

Hand over the boring parts of your week to AI — no coding required. Five practical jobs, every prompt written out, and an honest guide to what to check before you trust it.

Move beyond AI demos and build agents that can actually run IT operations. This practical guide takes you from MCP fundamentals to production-ready monitoring, incident response, remediation, Kubernetes operations, security and multi-agent systems, with complete runnable code and a strong focus on safe autonomy.

一套普通人也能上手的 AI 研究方法论：用三天时间，把任何一件事查到心里有底。

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.

You already know how to build software.You can design APIs, debug production incidents, reason about latency, operate databases, and ship features that people rely on. But AI engineering interviews ask a different set of questions:How do you design a RAG system that does not confidently invent answers? When should you use an agent—and when is a simple workflow safer? How do you evaluate an LLM feature when quality can decline without an error message? How do you control token costs, defend against prompt injection, and safely deploy a prompt change?AI Engineer – Interview Book bridges the gap between being a capable software engineer and being ready for an AI engineering interview.Learn how to reason about models, retrieval, agents, evaluations, cost, latency, safety, and production failure modes. Practice answers out loud. Learn the trade-offs behind the right answer—not just the vocabulary.Stop preparing for yesterday’s system-design interview. Start preparing to build AI systems that work in the real world.

Three full passes. One focused half-pass. A repeatable way to find patterns without losing the evidence trail.

La IA no toma decisiones: traduce el criterio del profesional que conoce su organización. Primero es el criterio. Hasta después es la herramienta.

The eight volumes before this one each teach a single domain, and each is deliberatelyself-contained. The exams do not ask about domains — they ask about systems, and a system spansall of them at once. "A retrieval system is slow, expensive and occasionally wrong — which do you fix first?" needsretrieval, optimisation and operations together. Every one of the 150 questions here crosses atleast two domains, because that is the specific thing a single-domain book cannot teach you.

"The model is safe." "The filter is effective." "We red-teamed it and found nothing." "Wereviewed a sample and it looked fine." Each of those is a claim about evidence, and each is unfalsifiable as stated. Reviewing thirtyoutputs and finding no problems is consistent with a failure rate of one in eleven. Threehundred tests against a thousand possible failure modes leave seven hundred nobody looked at. This book supplies the missing arithmetic — and it was written without generating a singleharmful output to study.

A model that works in development and fails in production usually fails for reasons that havenothing to do with the model. It waits behind other requests. It loads slowly after a restart.It answers differently because somebody moved a decoding parameter by a tenth of a point. Aguardrail blocks a third of legitimate traffic and reports itself as working. None of those appear in a benchmark. They appear in an incident — and almost every one of themis invisible in an instantaneous measurement and obvious in a trend.

Quantise the weights. Batch the requests. Add a draft model. Prune what you can. Shorten thecache. A long list of techniques and a short list of reasons. This book measures the reason first: the machine streams memory at 22.54 GB/s and computes at116.58 GFLOP/s, so anything performing fewer than 5.17 operations per byte is waiting. Decodingperforms 0.22. Every technique in the book is then measured against that one number — andseveral turn out to be addressing the wrong side of it.

The first thing many people hit with an AI coding assistant is a plateau. A correction made on Monday is gone by Thursday. This book is about the operating model above the prompt, where truth lives, how a correction becomes a standing rule that holds, and which judgments stay in human hands.

Move Beyond Simple Prompts. Build Reliable, Autonomous AI Systems.Connecting an LLM to an API is easy; building an AI agent that works reliably in production is hard. Mastering AI Agents gives you the exact architectural frameworks, workflows, and engineering practices needed to design, deploy, and scale intelligent agents that solve real-world problems.Practical Engineering: Master memory systems, tool integration, multi-agent orchestration, and system security.Production-Ready Focus: Learn how to evaluate, monitor, and maintain agents so they remain controllable and safe.Evergreen Principles: Built on core software architecture principles that stay valuable even as frameworks and models evolve.Grab your copy today and start building the next generation of intelligent software!

Short Description of the BookSecuring Modern AI Applications is a practical guide to securing LLM-powered applications, RAG systems, AI agents, and modern AI development environments.The book explains AI-specific threats such as prompt injection, data leakage, RAG poisoning, insecure tool use, agent abuse, and AI supply-chain risks while also covering traditional application security, privacy, governance, secure development, monitoring, testing, and red teaming.Designed for developers, AI engineers, cybersecurity professionals, architects, and technology leaders, the book focuses on practical principles that remain useful as AI technologies continue to evolve.Build AI. Secure AI. Govern AI.