• Align security strategies with organizational goals • Implement AI-assisted Continuous Security across teams • Select and integrate AI-powered tools for vulnerability detection, automated compliance checks, and real-time incident response • Transition from reactive to proactive security to continuously adapt to emerging threats • Apply best practices to mitigate risks and avoid breaches

324 pages. Guiding principles and pillars that underpin continuous testing, quality, security, and feedback. Knowledge to integrate testing into every stage of the software development life cycle, adopt a proactive approach to quality and security, and foster a culture of continuous feedback and improvement. Practical insights and strategies for overcoming common challenges and leveraging best practices to achieve high-quality, secure, and user-centric software products.

DevOps improves flow and feedback. DevSecOps brings security into the engineering lifecycle. SRE makes reliability measurable and operationally manageable. Platform Engineering reduces friction by giving teams reusable, self-service capabilities and guardrails. AI can assist across all of them, but AI must operate inside a disciplined system of human control, evidence, governance, observability, and trust. The principle of human-in-control appears throughout the book because it is not optional. Humans define intent. Humans accept risk. Humans approve high-impact changes. Humans are accountable for security, reliability, customer trust, ethics, and business consequences. AI may recommend. AI may summarize. AI may automate bounded work. Responsibility remains with people. This book also reflects my belief that engineering respect and trust are not soft topics. They are part of the operating architecture. A system that exhausts engineers, hides risk, punishes honest reporting, rewards superficial velocity, or treats security and reliability as late-stage obstacles will not become trustworthy because AI is introduced. Trust is earned through evidence, responsible decisions, observable behavior, and continuous improvement. I have written this book as a practical guide rather than an academic treatment. It is intended for engineering leaders, DevOps practitioners, DevSecOps professionals, SRE leaders, platform teams, security leaders, consultants, educators, and anyone trying to understand what AI-native software delivery should mean beyond the tool demonstrations. The goal is not to provide slogans. The goal is to help readers make better decisions. The companion course provides a foundation in the terms, principles, and practices. This book goes further. It is meant to help readers apply those ideas to real operating models, real pipelines, real platforms, real security concerns, real reliability tradeoffs, and real organizations filled with human beings doing difficult work. Some readers may come to this book looking for AI tools. Tools matter, but they are not the starting point. The starting point is the engineering system. What promise is the system expected to keep? How is that promise validated? How does work flow? Where does security enter? How is reliability measured? What platform capabilities make good practice easier? What evidence supports decisions? Where is human approval required? How does the organization learn? Those are the questions that matter.

Artificial intelligence is neither a savior nor a threat by nature. It reflects the values, structures, and intentions of the systems that deploy it. When respect and trust are absent, AI accelerates despair, disconnection, and inequity. When they are deliberately engineered, AI becomes a force for clarity, dignity, and shared progress. The difference is not technical. It is human.

SDSE challenges that paradigm. Rather than treating testing as a downstream activity, SDSE moves behavioral validation to the forefront. It emphasizes modeling, simulating, and validating system behavior before implementation begins. This is not simply an incremental improvement to existing practices. It represents a structural change in how engineers think about correctness, risk, and system design. What makes this approach particularly relevant today is the scale and speed at which modern systems operate. Distributed architecture, asynchronous interactions, and dynamic runtime conditions make it increasingly difficult to reason about system behavior using traditional methods alone. At the same time, AI, particularly the rise of agentic systems that can observe, decide, and act autonomously—is accelerating development and operational cycles at an unprecedented pace.

A practical guide to mastering DevOps through strategy, discipline, and continuous improvement. Written by award-winning engineer Marc Hornbeek.

The principles of Avalon respect, courage, compassion, integrity, and the pursuit of wisdom: lead us toward a more meaningful, harmonious, and noble life. They help us align with our highest selves, strengthen our relationships, and contribute to a more just and beautiful world. Living by these principles creates inner peace, fosters trust, and invites others to rise with us.

“The Avalonian Way representa un rico tapiz tejido a partir de varios hilos religiosos y filosóficos, enfatizando temas de continuidad, curación e interconexión. Este sincretismo refleja el deseo de una comprensión holística de la existencia que trascienda los límites tradicionales, invitando a una visión más inclusiva y compasiva de la vida, la muerte y el viaje del alma”.

This document is an introduction to intelligent, adaptive systems   from generative foundations to swarms of autonomous agents

In the book Engineering DevOps by Marc Hornbeek, DevOps is organized in Nine Pillars.  These are permanent structural parts of DevOps engineering, no matter where you are trying to do DevOps. The Nine Pillars of DevOps provides a comprehensive framework to describe DevOps and DevSecOps practices, focusing on leadership, culture, design, integration, testing, delivery, monitoring, security, and infrastructure.

Las estructuras de Arco y Fundación cruzan los Nueve Pilares horizontalmente porque son relevantes para unir todos los pilares. Las estructuras de Fundación son “Orquestación y Automatización” y “Gobernanza” porque todos los pilares dependen de ellas. Los Arcos son “Entrega Continua CI/CD Pipeline”, “Automatización de Liberación de Aplicaciones” y “Gestión de la Cadena de Valor” que hacen uso de las prácticas de los Nueve Pilares.

28 pages. This book is a starter guide, introducing this concept and equip you with the knowledge of AI Based Automated testing. In the next chapter, you will take a deep dive into the types of testing, their disadvantages and how they pave the way for innovation using AI. Once the need is established, the book will delve deep into the processes, techniques, challenges, best practices and strategies associated with AI Based Testing. At the end, the reader will have a good understanding of the concept and will be equipped to start their own journey into the world of AI Based Testing.