The confession arrives with a familiar sting: four months of running AI agents, a fivefold jump in output, and one near miss that snapped everything into focus. The question isn't how to code faster. They're asking what we're supposed to do while the AI writes the code. That's the question we should all be sitting with, because it exposes a gap between the tools we're using and the skills we're quietly letting atrophy. Speed is easy to measure. Judgment is not. And right now, too many of us are mistaking the former for the latter.
This isn't a Luddite's warning, and it shouldn't be. The productivity gains are real, and we'd be foolish to pretend otherwise. But the near miss in the story is the detail that matters. It's the moment the author realized that delegating execution to an agent without maintaining a clear mental model of the work turned them into a reviewer of code they no longer fully understood. That's a tradeoff worth examining. It's the same tension we see in the broader shift toward AI-assisted workflows: the tools demand that we become better at asking questions, not just giving commands. If you're not careful, you end up with a team member who never sleeps, but also never explains its reasoning. And you're left to clean up the edge cases it didn't know to flag. This is why we keep coming back to the human side of the loop, whether we're talking about Unlock LLM Training: A Practical Guide to Distributed Algorithms or the more mundane reality of debugging an agent's output at 4 p.m. on a Friday.
The practical takeaway here isn't to ditch the agents. It's to redefine what "doing the job" means. If the AI writes the code, your job shifts to verification, design, and the messy art of deciding what *not* to build. That requires a different kind of expertise, one that's closer to architecture and product sense than to syntax. It's the same reason we're seeing job postings blur the line between AI and software engineering, a topic we've covered in Navigating AI/ML Job Requirements: A Shift in Expected Skills. The people who thrive won't be the ones who type the best prompts. They'll be the ones who can look at an agent's output and know, with confidence, whether it's actually correct. That's not a technical skill. It's a judgment skill. And judgment only improves with practice, which means you have to keep your hands in the work, even when the machine is doing the typing.
So what do we tell a reader who asks about this? Start by treating the near miss as a feature, not a bug. Build checkpoints into your workflow where you pause and explain the agent's logic back to yourself, out loud, in plain language. If you can't do that, you've lost the thread. And before you ship anything, run a verification pass that tests your own understanding, not just the code's output. We've written about simple checks for AI understanding before, like in Verify Your AI's Understanding: A Simple Check for Tax Season, and the principle holds: the tool is only as good as your ability to audit it. The real risk isn't that you'll become 5x worse at your job. It's that you won't notice until the near miss becomes a direct hit. Watch for the moment you stop being able to explain why the code works. That's the moment to step back, slow down, and reclaim the part of the work that makes you human.