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[ARTICLE · art-57779] src=ronaknathani.com ↗ pub= topic=artificial-intelligence verified=true sentiment=↑ positive

Spend the AI Dividend on Quality

Coding agents lower the cost of writing code, enabling developers to spend the savings on quality instead of just more features. By using agents to explore edge cases and simplify designs before implementation, teams can build simpler, more reliable production systems.

read2 min views1 publishedJul 13, 2026

Coding agents make quality cheaper.

One thing I’ve been thinking about lately is that coding agents don’t just help us ship more software. They can help us ship better software.

If you’ve used a coding agent, you’ve probably seen the agent say something like this: “I could also add X.”

“We could improve this by building Y.”

“Just say the word and I’ll implement it.”

It’s incredibly tempting to say yes because adding another feature is almost free. But that’s rarely what makes software good.

The best software isn’t the one with the longest list of features. It’s the one with a coherent set of features that work reliably every single time. The systems people trust the most are usually the simplest ones. They do fewer things, and do them extremely well.

This is especially true for infrastructure. In my experience, some of the best infra engineers start with a default “no” to new features.

Coding agents lower the cost of writing code. The default is to spend those savings on more features. I think we should also spend some of it on quality. Instead of asking an agent, “What else should we build?”, I increasingly ask:

The thing is that agents are really good at finding issues. As humans, it’s hard to think of all the possible edge cases. We rely on production and gradual rollouts to identify bugs. These are often the paths we never considered. An agent can walk through several failure modes, challenge assumptions, and pressure-test a design long before any code reaches production.

I’ve found this changes how I write design docs. Instead of using an agent to implement the design I wrote, I use it to iterate on the design itself. We keep simplifying it, questioning invariants, exploring failure cases, and removing unnecessary complexity until the design feels solid.

By the time implementation starts, most of the hard thinking has already happened. Steering the agent to write the code becomes increasingly easy.

This doesn’t matter much for disposable software. But for production systems, simplicity and reliability are features.

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