Software engineers have become overreliant on models to build applications, and it’s time to put humans more firmly in the loop, the AI Engineer World’s Fair was told on Tuesday.
The term software engineering was first used in a 1968 NATO conference, which was called to discuss the problems of building large-scale, reliable applications at speed. It was suggested that applying industrial manufacturing to code creation would help speed this process up, but with larger and larger amounts of code now being machine generated, the cracks are starting to show in reliability.
“We are all racing to put AI coding into production, and there's been lots said about loop engineering, and we should probably write more loops,” argued Dex Horthy, the impressively mustachioed co-founder of HumanLayer.
“I'm here to convince you today that this is, in fact, not a joke, and that no amount of harness engineering or loops maxing can solve what is fundamentally a model training issue.”
The problem, he said, is that developers have become overreliant on badly trained models. While some agents are great at some parts of the development cycle, they are lousy at others. As a result, pull requests are up, code breaks are multiplying, and customers — and the companies supplying them — are losing out.
His proposal is to make humans much more involved in the early stages of software design, particularly with planning and architecture decisions. Human steering at the start can solve a lot of problems before they become serious issues, he opined, but 30 minutes more in preplanning and alignment can save hours in review.
Loops don’t offer a magic bullet, Horthy stated — it’s not possible to throw engineers into training agentic models to do their jobs for them. Loops can help, but they aren’t the universal panacea that many in the industry are saying.
Part of the problem is the reward system for training, Horthy said. If a serious flaw is committed in vibed code, it could only be discovered months or even years after the error was made. There’s no way to “propagate that reward signal back across the gap,” he told the crowd.
Human code reviews are too often being skimped on, or even eliminated, in some software houses. AI can help human checkers considerably, but it can’t be relied on without human oversight, he proposed. And if you’re getting drowned out in AI-generated pull requests, then that also indicates the models are at fault. He added that if machine-made code needs a 20% rewrite, then that’s actually pretty good for much of the agentic output.
Small fixes can be assigned to an agent to fix, but for key system architecture decisions, humans need to review and plan where a product is going, how its parts fit together, and how to get there without too many pull requests. Mistakes will always be made, but it’s important to minimize them if you want to keep users from getting overly pissed off. The answer is a fusion of man and machine, but with some realignment, the issue is fixable, he suggested.
“It's easy to hear all this and be a little bummed out. I really like the world where we can just not have to ever read code ever again,” he said.
“But we're engineers, and these are just constraints, and models are good at certain things, and they're not good at other things. And so, go figure out how to solve problems given a set of constraints. Use loops, they're great. But go solve the hard problems, seek leverage.”
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