Software delivery is accelerating. Release confidence is not. Artificial intelligence can generate requirements, code, tests, and documentation in minutes, but faster output does not automatically make software safer to release. This book presents a practical operating model for building confidence as quickly as teams create change by connecting product intent, risk, validation, automation, release readiness, and production learning.
As you move through the chapters you will follow the Hyper-Agile Quality Loop from idea to production. You will learn how to turn requirements into test expectations, adjust validation depth to risk, and choose automation by value rather than test count. Additionally you will also gain expertise in keeping continuous integration and continuous delivery signals trustworthy, and using artificial intelligence to support requirements review, impact analysis, defect triage, and release decisions. The chapters are supported by practical examples, diagrams, checklists, and personal stories that will show you how these ideas work under real delivery pressure.
This book will guide you in applying the model across delivery stages and risk levels—from prototypes and internal pilots to early adopter and general availability releases, including high-risk or regulated work. You will see how Product, Development, Quality Engineering, Support, and Operations each contribute to quality. It will also help you to understand how production feedback improves the next delivery cycle and how Quality Engineering can move beyond late-stage testing toward quality decision support.
By the end of the book, you will have a practical framework for creating safer software and increasing release confidence. Applying it will help you learn faster, make clearer release decisions, and reduce the risk pushed downstream.
What You Will Learn
- Apply the Hyper-Agile Quality Loop from product intent through production learning
- Match validation depth to risk, release stage, and customer impact
- Build trustworthy automation and CI/CD signals that support release decisions
- Use AI responsibly for requirements review, test design and automation, impact analysis, and defect triage
Who This Book Is For
Quality engineers, QA leads, engineering managers, product managers, and technology leaders working in fast‑moving delivery environments.
About the Author :
Evgeny Tkachenko is a software engineering and quality leader with over twenty years of experience working at the intersection of technology, quality, and business. He has worked in large product organizations such as Wayfair and Amazon, as well as in healthcare and startup environments. His experience includes Hyper-Agile and DevOps transformations, AI-assisted software delivery workflows, automation and release strategy, and helping organizations build quality practices that support faster, safer software delivery.
Evgeny is also an active speaker in the quality engineering and software testing communities. Through his presentations, he engages with quality engineers, engineering leaders, testers, and practitioners who face many of the same challenges addressed in this book: how to deliver faster without sacrificing quality.
He is the author of two additional books, Navigating Quality Engineering in the AI Era and Testing AI-Powered Applications: Ensuring Quality in the Age of Intelligent Software. Across his writing and speaking, Evgeny focuses on helping quality engineers and engineering leaders modernize their quality practices, use AI responsibly, improve release readiness, and bring quality earlier into the software delivery process instead of treating it as a final gate.
His work combines practical quality engineering experience with a broader view of how the discipline is changing. He continues to advocate for quality practices that help teams move faster, make better release decisions, and build software with greater confidence.
