- Preface — Why I Built This
- Introduction — The Friday Deploy Problem
- Part I — Why We Deploy Blind
- 1. DORA Told Us What Happened. Nobody Told Us What Was About to Happen.
- 2. AI Is Making This Worse: The Instability Paradox
- 3. The Anatomy of a Production Incident
- Part II — The Signals
- 4. Signal Taxonomy
- 5. Structural Signals
- 6. Historical Signals
- 7. Process & Environmental Signals
- Part III — The System
- 8. Building the Score
- 9. Integration & Rollout
- 10. The Risk Review
- Part IV — The Culture
- 11. Team Adoption
- 12. The Intelligent Organization
- Part V — In Practice
- 13. Case Studies
- 14. Implementation Guide
- 15. Building the Business Case
- 16. The Horizon
- Appendices
- A — The 33 Signals Reference
- B — Implementation Checklist
- C — Glossary
- D — The Research Base
- E — Worked Examples & Common Questions
- References
Deployment Intelligence
How Engineering Leaders Stop Deploying Blind
Every post-mortem starts the same way: "We didn't see it coming." This book argues that's no longer acceptable — and shows you the science behind predicting deployment risk before it becomes an incident.
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About
About the Book
Your DORA metrics tell you your deployment frequency, your change failure rate, your mean time to restore. What they don't tell you is which deployment is about to fail.
Engineering leaders have spent a decade measuring what happened. Deployment Intelligence is about what's about to happen.
Drawing on academic research in Just-in-Time defect prediction, Google SRE practice, and data from millions of production deployments, this book introduces a new discipline for engineering leadership: the systematic, signal-based prediction of deployment risk before a single line of code reaches production.
You'll learn why the size of a change tells only a fraction of the story — and why signals like change entropy, database migration presence, author file-level expertise, and SLO error budget burn rate are the real predictors of production incidents. You'll understand why AI-assisted development is quietly increasing deployment risk at many organisations, and what to do about it.
Deployment Intelligence covers:
- The 33 research-validated signals that predict deployment failure — ranked by predictive power
- Why DORA metrics are necessary but insufficient for engineering leaders who want to prevent incidents, not just measure them
- How to build a risk-aware deployment culture without slowing your teams down
- The architecture of a deployment intelligence system — from signal collection to risk scoring to hard gates
- The LLM semantic layer: why AI can read PR descriptions and identify "quick fix," "temporarily disabling," and "bypassing validation" as incident precursors
- What the most resilient engineering organisations do differently on Fridays at 4pm
Whether you lead a team of 10 engineers or 10,000, the gap between "we deploy a lot" and "we deploy safely" is filled by intelligence — not instinct.
Andrew McCarron has spent over 20 years advising engineering organisations on delivery performance, risk management, and technical strategy. He is the founder of Koalr, an engineering intelligence platform built on the research and frameworks described in this book.
Author
About the Author
Andrew McCarron
Andrew McCarron is a software delivery consultant with over twenty years of experience helping enterprises ship software safely at scale. He has advised organisations ranging from regional banks to global logistics companies, running post-mortems on hundreds of production incidents and developing a practitioner's intuition for deployment risk.
That intuition — and the frustration of watching preventable incidents repeat themselves — led him to found Koalr, the Deployment Intelligence™ Platform. Koalr scores every pull request 0–100 using 32 validated signals before merge, giving engineering teams the risk signal that post-mortems consistently show was always present but never surfaced.
Andrew holds SAFe Practice Consultant, CSM, CSPO and PSM certifications and writes about deployment safety, engineering metrics, and the gap between research and production tooling.
Contents
Table of Contents
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