Eval Engineering

An agent's answer can look right while its actions are wrong. Learn to evaluate tasks, traces, tool calls and changed state with reproducible harnesses, tested graders and regression gates.

A practical guide to evaluating small and local language models in the real world. Learn how to benchmark reliably, compare models across hardware and runtimes, optimize performance and avoid misleading results. With practical code and rigorous methods, you can build your own evaluation setup and make confident deployment decisions.

AI can write code, create images and make decisions, but do you really understand what happens behind the scenes? This book cuts through the hype to explore how AI works, where it falls short and what it takes to build reliable systems. If you want to understand AI beyond the chat window, start with the code.

The model is one box. This book is about every other box: retries, contracts, evals, budgets, guardrails and agents for backend engineers shipping LLM features to production.

From Mathematical Foundations to the Future of AIMost deep learning still lives in flat, Euclidean space. Real-world data does not.This 15-chapter university textbook takes you from the basics of topology and geometry to the frontier of Geometric Deep Learning, Topological Data Analysis, equivariant networks, and optimization on manifolds.Clear explanations. Worked examples. Research roadmaps.

Build agents that write code, fix tests, and commit changes while you sleep — safely, cheaply, and entirely on your own hardware (622 manuscript pages).

AI can decide. That does not mean it is authorized to act. Governance Before Decision examines the missing control layer between intelligent systems and real-world consequences — where authority, scope, verification, and execution must meet.

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.

Master AI/ML System Design with a Structured Interview FrameworkLearn how to approach complex ML system design interviews using a 20-step framework, architecture blueprints, real-world case studies, scalability strategies, data and model pipelines, deployment patterns, monitoring, and interview-ready templates.

Build Smarter Chatbots and Voice AssistantsExplore Conversational AI from fundamentals to real-world applications. Learn NLP, intent and context management, intelligent chatbots, voice assistants, speech technologies, multimodal interfaces, APIs, deployment, business integration, analytics, ethics, LLMs, and Generative AI.

Secure AI. Understand the Threats. Build More Trustworthy Intelligent Systems.Explore adversarial attacks, data poisoning, model theft, AI-enabled cyber threats, secure AI development, adversarial defenses, trustworthy AI, MLOps security, governance, privacy, red teaming, and the future of AI cybersecurity through practical concepts and real-world case studies.

Explore the Future of AI-Powered RoboticsDiscover how Artificial Intelligence is transforming robots into intelligent machines capable of learning, seeing, communicating, navigating, and making decisions. Explore Machine Learning, Computer Vision, NLP, Deep Learning, autonomous systems, smart factories, healthcare robots, drones, humanoids, agricultural robotics, ethics, and future trends.

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.

Build a fully functional semantic search engine from first principles using pure Python and NumPy—no heavy AI frameworks, no vector databases, just pure intuition and mathematics.

Specification Engineering turns complex ideas into clear, workable specifications. This practical guide takes you from core principles to advanced methods for defining, validating, managing and evolving requirements across products, software, systems and services. Built for real-world use, it gives you the tools to create specifications that people can understand and act on.