It was a Tuesday afternoon in March 2026.
A senior engineer, let's call her Priya, was three slides into a quarterly planning meeting when her phone buzzed. A notification from her terminal. Claude Code had opened a pull request.
She'd started a refactor before the meeting. A sprawling authentication module: 14 files, deprecated patterns, a test suite nobody had touched in two years. She gave the agent a brief in plain language, set the parameters, and walked into the room.
Forty-five minutes later, the PR was open. The code was clean. The tests passed. The deprecated patterns were gone.
She reviewed it that evening, approved it at 6:15 p.m., and closed her laptop.
Here's the question that keeps me up at night:
Was that engineering? Or was that management?
Because if the agent wrote the code, ran the tests, and opened the PR, what exactly did Priya do?
She wrote the brief. She set the parameters. She reviewed the output. She made the call to merge.
She directed it.
And that, directing rather than implementing, is what this entire moment in software engineering is about.
I've been a software engineer for 9 years. I've built SaaS products, fintech systems, and DevOps pipelines from scratch. I watched Copilot arrive and thought "neat autocomplete." Then Cursor arrived and I realised something had fundamentally shifted.
Not because the tools were impressive. Because I finally understood what they were.
They are not smart colleagues. They are not replacements. They are the most powerful leverage mechanism software engineering has ever produced for engineers who understand them deeply enough to wield them.
That's what this book is about.
For the next 20 days I'm going to share an excerpt from each chapter. Some days will make you uncomfortable. Some days will change how you work on Monday morning. All of them are grounded in what's actually happening in engineering teams in 2026, not hype, not fear, just the territory as it is.
Tomorrow: The one sentence about AI that changes everything.
Top comments (1)
This perfectly captures the shift in software engineering brought by AI agents. The key insight—that directing and reviewing AI output is as much engineering as writing the code yourself—is profound. Tools like Claude Code aren’t colleagues; they are leverage mechanisms that allow experienced engineers to focus on architecture, correctness, and decision-making, while the AI handles repetitive or refactoring tasks.
I’d love to collaborate and explore how this paradigm scales across teams: integrating AI agents for CI/CD workflows, automated testing, and multi-module refactors while maintaining full human oversight. Sharing strategies on prompt design, parameter setting, and verification loops could help teams maximize efficiency without sacrificing quality or control.
Would you be open to experimenting together on AI-driven workflow orchestration in real projects?