Agentic Engineering

Traditional test automation can execute scripts, but it struggles to explain failures, adapt safely to application changes, evaluate AI-powered features, or govern autonomous testing agents. AI-Native Software Testing shows practitioners how to design a modern testing architecture that combines Playwright, AI agents, LLMs, controlled self-healing, and governed CI/CD automation.

Build a powerful AI assistant that runs entirely on your own hardware. The Ollama Assistant Handbook takes you from installation to a production-ready assistant with memory, tools, voice, vision, and automation through practical, hands-on examples you can use immediately.

AI can generate code in seconds, but shipping production-grade software requires far more than fast prompts. Vibe Engineering shows you how to work with AI coding assistants to build systems that are reliable, secure, and maintainable. Through practical examples and proven workflows, you will learn how to turn AI from a code generator into a trusted engineering partner.

Artificial intelligence is reshaping financial markets. Intelligent Markets shows you how to build production-ready AI trading systems from first principles, combining market fundamentals, practical architecture, and fully runnable code to create autonomous agents that can research, execute, and manage trades in live markets.

Most businesses use only a fraction of what Claude can actually do. The difference between average results and real business impact is not the AI. It is the prompt. This book gives you 100 proven Claude prompts for solving high-value business problems, complete with practical guidance so you can implement them immediately and turn AI into a competitive advantage.

Build AI agents that do real work with the Google Agent Development Kit (ADK), Python, and Google Cloud. Through complete, runnable examples, you'll learn how to build, orchestrate, and deploy production-ready multi-agent systems with practical skills you can apply from day one.

Go beyond simple chatbots and build production-ready AI agents with LlamaIndex. Through practical projects and working Python code, you will learn to design reliable systems with retrieval, workflows, multi-agent architectures, observability, and deployment techniques for real-world applications.

Discover how to build production-ready AI agents and multi-agent systems with CrewAI. Through practical examples and real-world projects, you will learn to create autonomous agents that collaborate, use tools, integrate with external data, and scale from prototype to production.

Learn how to build intelligent, production-ready AI agents with the OpenAI Agents SDK. Through practical Python examples and step by step guidance, you'll master everything from the fundamentals to advanced multi-agent workflows, tools, and deployment.

Learn to build production-ready LLM applications with DSPy through hands-on tutorials, complete runnable examples, and real-world projects. Master DSPy's core abstractions and create AI systems that improve with data instead of endless prompt tweaking.

Learn how to build autonomous, production-ready multi-agent AI systems in C# with .NET. Using Harness, Hermes Agents, and Loop Engineering, you'll create intelligent agents that reason, collaborate, and solve real-world problems through practical examples and hands-on projects.

You don't have to be a programmer to learn from this book. This book asks nothing of you but your willingness to tell Claude what and how you think. I've written this for the professional who's been thinking, maybe quietly, that their real value wasn't in the busywork. You can automate anything that's between you and them.

Build smarter AI systems that go beyond the limits of large language models. Retrieval-Augmented Generation is a practical guide to designing, implementing, and scaling RAG applications with modern retrieval techniques, vector databases, and real-world deployment strategies.

You shipped the demo. The model worked. Now production is coming, and the demo is not a system. An agentic system is a distributed-systems engineering problem — the reliability lives in the shell, not the model.

A hands-on guide to designing, building, testing, and deploying secure stdio and SSE MCP servers in Python and TypeScript (447 manuscript pages).