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Aditya Agarwal
Aditya Agarwal

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Agentic coding isn't a trap. Lazy engineering is.

The hottest take in developer discourse right now isn't about frameworks or languages. It's about whether AI agents are silently destroying your codebase.

A viral post recently called agentic coding a "liability accelerator." It painted a picture of codebases rotting from the inside out, maintained by developers who no longer understand what they've built. The post spread fast. Engineers nodded along. And I think they're blaming the wrong thing entirely.

The real problem has a name

The issue is not with the representative. It is with the approach that exists, or the absence of it.

A counterpoint article on Dev.to actually provided production logs from a team that was using agents that had very precise well-written specs, and the results were clean output, actually, predictable output, and fewer bugs than we had experienced in their pre-agent workflow.

What made a difference was not the tool used, but rather that an individual took the time to write a solid specification before using the generation tool.

Agents don't skip code review. You do.

This is what goes down in reality when agentic coding flops:

β†’ Developer gives a vague prompt with no constraints
β†’ Agent produces plausible-looking code that technically works
β†’ Developer ships it without reading it carefully
β†’ Three months later, nobody understands the module

Sound familiar? Now substitute "agent" with "junior developer" or "Stack Overflow copy-paste" or "that contractor we hired for two weeks." The end result is the same. Throughout history, we've found ways to write code that escapes our comprehension. Agents just automated the process.

It's the speed that frightens everyone. I understand. However, it was never the problem. Being reckless was.

The codebase comprehension problem is real but not new

The biggest legitimate concern is that developers stop understanding their own systems. That's worth taking seriously. 🧠

However, we have to admit that this was already in motion. Enormous monorepos, automatically created boilerplate code, dependency trees that no one checks, copying and pasting infrastructure-as-code templates from blog posts. In fact, most teams were already working without a complete understanding of their stack.

Agents were not responsible for the comprehension gap. They simply brought it to our attention.

We shouldn't eliminate the tool. But regard agent output as any other contributed code: review it, test it, understand it before it merges. If your team lacks that discipline, you had a process problem long before you had an AI problem.

What good agentic workflows actually look like

Agents tend to provide real value to teams that have a few things in common:

β†’ They write detailed specs before prompting β€” acceptance criteria, edge cases, constraints
β†’ They review agent output line by line, same as a human PR
β†’ They use agents for the boring stuff β€” boilerplate, tests, migrations β€” not architecture decisions
β†’ They treat the agent like a fast but inexperienced teammate, not an oracle

All of this is evolutionary. It's engineering discipline being applied to a new-fangled tool. The type of discipline we've always known we needed, but often conveniently overlooked. πŸ˜…

The uncomfortable truth

Blaming agents for tech debt is comfortable. It gives us an external villain.

The developers who are now shipping unreviewed agent code are the ones who shipped unreviewed human code before. They are the ones who neglected to write tests. The same ones who approved their PRs on a Friday afternoon. The process may have changed, but their habits haven't.

Writing code in a way that reflects your engineering culture is not a bad thing. It's not a trap. It's a mirror. It shows you what you are. And it does so faster and more honestly than ever before. If you don’t like what you see, the problem isn’t the reflection.

Good process makes agents a multiplier. Bad process makes agents a liability accelerator. The variable was never the agent.

So here's my question: has your team established any specific rules or review practices for agent-generated code, or is it still the Wild West?

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