New Book — March 2026
How software engineers learn to build with agents
AI agents don't just autocomplete your code. They read your codebase, reason about it, make changes across files, run your tests, and iterate. This book is your practical guide to the transition — from writing software to engineering it.
We're moving from writing every line by hand to directing, orchestrating, and collaborating. Less typing, more thinking. Less editor, more engineering.
The tools showed up fast — Copilot, then Claude, then agents that run entire tasks autonomously — and we're all figuring it out in real time.
Systems thinking, taste, judgement, knowing what to build and why — those matter more now, not less. This book shows you how to wield them.
Why does this book exist? Because I have a genuine, slightly irrational desire to share everything I've learned about agentic engineering. Because I want to shine in the eyes of my kids. And — let's be honest — because having written a book is technically a conversation starter, even if it's a terrible one.
— From the ForewordI've been a software engineer for a long time. I know what it feels like to have a codebase in your head — to open an editor, navigate to the right file, and just write the thing. That muscle memory, built over years of keystrokes and debugging sessions, is real. It's earned.
And now the ground is shifting under our feet.
17 chapters covering the shift, the tools, and the engineering practices that make agentic work actually work.
The fundamental loop of software engineering is breaking. What it means to go from writing software to engineering it — with a crew of agents at your side.
The single most important skill in agentic engineering isn't prompting — it's context management. Learn to curate what your agents see.
Placing AI tools on a spectrum from autocomplete to autonomous systems. What makes something truly agentic, how agents fail, and the right mental model for working with them.
Parallel visibility, structured handovers, configurable trust, and version control as infrastructure — principles that emerged from building a multi-agent control plane.
The trust gradient, permission scoping, approval gates, and environment-specific rules. How to give agents power without giving them the keys to production.
Small commits, descriptive messages, branch-per-task, and worktrees for parallel agents. Git as your undo button and review interface.
Giving agents the freedom to be wrong. Worktrees, containers, and ephemeral environments that make failure cheap and experimentation free.
Tests become the agent's eyes. TDD as a superpower, fast test suites as agent infrastructure, and the virtuous cycle of better tests and more autonomous agents.
Why consistent file naming, formatters, project layouts, and CLAUDE.md files make agents dramatically more effective.
Privacy, cost, capability trade-offs, team cost management, and getting started without paying the farm. The practical hybrid approach of matching the model to the task.
How to write effective agent prompts — task decomposition, constraint specification, the prompt-as-spec pattern, and anti-patterns to avoid.
Running agents in parallel, the handover pattern for sequential work, branch-per-agent isolation, and knowing when more agents isn't better.
Using agents in CI/CD — PR pre-screening, overnight agents, cost control in automation, and the trust question when nobody's watching.
War stories from the field — the eager refactorer, the hallucinated library, the infinite loop — plus a diagnostic playbook for systematically debugging agent failures.
The overhead tax, architecture decisions, security-critical code, working with legacy codebases, and the craft argument. Knowing when to put the agent away.
How agent-assisted development changes code review, the junior engineer question, knowledge distribution, compliance and audit trails, hiring, and team rhythms.
The craft isn't dying — it's shedding its skin. What endures, what changes, and why the captain still matters.
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