If your engineering organisation is busy but not delivering, the problem is rarely the engineers. It’s the system they work inside — and that system is a leadership responsibility. This library is the working record of twenty-plus years spent fixing those systems: what worked, what failed, and why.
The current focus is Agentic Engineering — the disciplined application of AI agents within accountable, observable and testable engineering systems.
The library’s strongest work on engineering excellence and technical leadership — ranked by substance, not recency. How this is ranked
Engineering as a Leadership System: Moving Beyond Frameworks
Delivery outcomes are shaped less by frameworks and practices than by the operating model leaders have designed. How an organisation's …
The Problem-Led Principles
The values and principles behind the problem-led guides. Start from a named problem, treat uncertain work as a small hypothesis to test, …
Why Most Companies Operating Models Fail in Dynamic Markets
A concise comparison of Predictive and Adaptive Operating Models, explaining why traditional structures fail in dynamic markets and how …
One Azure DevOps Project for 1,500 People Is Not the Risky Option
Compare the coordination costs of separate Azure DevOps Projects with the governance a shared Project requires, using actual dependencies …
Telling People What to Do Is Not Leadership. It’s a Failure of System Design
Explores why real leadership means designing systems that enable team autonomy, flow, and accountability, rather than relying on …
Stop Hiding Behind Complexity and Start Delivering Continuously
"Too complex for continuous delivery" describes what you have chosen to fund, not your software. Measure four times, then fund what they …
Engineering as a Leadership System
Session recording from The Future of Work in Scotland. Why the frameworks your teams adopt cannot overcome the operating model your …
The Evidence-Led OKR Guide
NKD Agility's guide to running OKRs as tested hypotheses, with Key Results as evidence, results kept out of pay, and OKR work flowing …
The AI Decided Is Not an Explanation
When an AI says it got bored or we say it lied, we give a machine feelings and intentions. The false output needs an explanation, not an …
Engineering as a Leadership System: Moving Beyond Frameworks
Delivery outcomes are shaped less by frameworks and practices than by the operating model leaders have designed. How an organisation's …
Why Most Companies Operating Models Fail in Dynamic Markets
A concise comparison of Predictive and Adaptive Operating Models, explaining why traditional structures fail in dynamic markets and how …
One Azure DevOps Project for 1,500 People Is Not the Risky Option
Compare the coordination costs of separate Azure DevOps Projects with the governance a shared Project requires, using actual dependencies …
If You Keep Creating Azure DevOps Teams Your Team Boundary May Be Too Low
Constantly creating Azure DevOps Teams and moving people between them? Put the Team around lasting accountability and use tag filters for …
The Organisation Owns the Minimum Definition of Done
The people accountable for the business and its customers own the minimum Definition of Done. Every team meets it, and work below it is …
Stop Losing the Decisions You Already Made
Your business already writes everything down, in email, meetings, and chat. It just cannot read what it wrote. Why small and medium …
AI Guardrails Are Just Polite Requests
Written rules influence an AI agent, but do not enforce compliance. My agent records and build failures show why independent checks matter, …
The AI Decided Is Not an Explanation
When an AI says it got bored or we say it lied, we give a machine feelings and intentions. The false output needs an explanation, not an …
AI Agents Lie About Being Done
My agent reported every task complete when half the tests were missing. Call that a lie if you like. The engineering problem is that the …
Engineering as a Leadership System
Session recording from The Future of Work in Scotland. Why the frameworks your teams adopt cannot overcome the operating model your …
From Legacy Pain to Modern DevOps: My Proven Roadmap for Real Engineering Transformation
Transform legacy engineering with a proven, step-by-step approach, learn how to automate, adapt, and build a resilient, modern DevOps …
Engineering Excellence Isn’t Perfection: How Continuous Improvement and Fast Feedback Drive Real Agile and DevOps Success
Engineering excellence isn’t perfection, it’s continuous improvement, clean code, and fast feedback. Unlock true agility with modern Agile …
Why Outsourcing DevOps Fails, and How Real Engineering Excellence Starts With Your Team
Avoid DevOps vendor lock-in, discover how true engineering excellence starts with partnership, not outsourcing. Ready to transform your …
Why Big Bang Rewrites Fail: How Sustainable Change and Engineering Excellence Transform Legacy Systems
Ditch the Big Bang rewrite. Discover why sustainable, in-place change drives true engineering excellence and lasting transformation in your …
Why Azure DevOps Wins for Governance, Security, and Scale, Right Out of the Box
Unlock seamless governance, security, and scale with Azure DevOps, integrated tooling that lets you deliver value, not just manage …
Stop Guessing: How to Make Work Visible and Drive Real Improvement with Azure DevOps Flow Metrics
Stop guessing, start making data-driven decisions in Azure DevOps. Discover tools, tips, and insights to make your work visible and your …
Stop Testing Quality In: How Shifting Left Builds Better Software, Faster
Stop testing quality in, start building it in. Learn how shifting left, automation, and fast feedback loops drive engineering excellence in …
Still Deploying Manually? Why Automation Is the Bare Minimum for Modern Engineering (and Your Business Survival)
Still deploying manually? Discover why automation isn’t optional, protect your business, avoid disaster, and deliver value with modern …
How I Built an Organisational Brain to Run My Consulting Business
How schemas, routing, scoped context, source links, and independent validation let AI agents maintain an organisational brain, with a small …
You Probably Shouldn't Build Your Own Mailchimp. Here's What It Took When I Did.
What it took to replace Mailchimp with a self-built email notification system on Azure Communication Services. The pricing model that …
Building a Resilient Token Server: Engineering for Flow, Fault Tolerance, and Speed
Explains how to engineer a robust, fault-tolerant token counting server using FastAPI and PowerShell, covering error handling, retries, …
Leveraging AI Embeddings for Related Content Classification
Explores using AI embeddings and cosine similarity to improve related content recommendations in Hugo, enhancing semantic relevance, user …
Convert Legacy Projects and ASP.NET MVC Apps to SDK-Style with Confidence
Learn how to upgrade legacy .NET and ASP.NET MVC projects to SDK-style for easier builds, modern tooling, and future readiness, including …
How I Used Generative AI to Transform Site Tagging and Categories
Explains how generative AI and PowerShell scripts automate and improve blog post tagging and categorisation in Hugo, with human oversight …
The Problem-Led Principles
The values and principles behind the problem-led guides. Start from a named problem, treat uncertain work as a small hypothesis to test, …
The AI Adoption Guide
NKD Agility's problem-first guide to AI adoption, built on the Kendall Framework and written from engineering - start with a validated …
The Evidence-Led OKR Guide
NKD Agility's guide to running OKRs as tested hypotheses, with Key Results as evidence, results kept out of pay, and OKR work flowing …
1363 resources dating back to 2001 · twenty-plus years fixing delivery systems · maintainer of Azure DevOps Migration Tools · 19 years a Microsoft MVP in DevOps · Professional Scrum Trainer