I used a coding agent to help generate infrastructure as code for an AWS solution.
It did what I asked. The code was deployed. Everything looked fine at first.
But when I checked the AWS console more closely, I realized something important: the solution was using a legacy service pattern that AWS had already moved past.
That moment changed how I think about AI-generated code.
The code worked. That wasn’t the same as being right.
This is the part that matters.
The agent was confident. It even suggested that a newer approach was not necessary. But after I went back to the AWS documentation and release notes, I found that the newer guidance was real, and in some cases, the newer service choices could simplify the architecture and reduce cost.
So I updated the code with the official reference.
I’ve always kept in mind that AI-generated code is not 100% accurate.
That part is easy to say and easy to agree with.
But in real life, when you’re trying to get a solution working, ship faster, or unblock a problem, that warning can quietly move to the back of your mind. You start trusting the output a little too much because it looks good, deploys cleanly, and seems to solve the immediate problem.
That experience reminded me of something simple:
AI can generate code fast, but it cannot guarantee that the code reflects the latest cloud guidance.
Why this happens
Coding agents are useful, but they are not magic.
They can be trained on older patterns, older examples, and older assumptions. In a cloud platform like AWS, that matters a lot because services evolve quickly. What was recommended last year may no longer be the best choice today.
That means AI-generated IaC can be:
- syntactically correct,
- deployable,
- and still architecturally outdated.
That is a dangerous combination if you are not validating it carefully.
My new workflow
I still use coding agents. They save time and help me move faster.
But now I treat their output as a first draft, not a final answer.
My checklist looks more like this:
- Generate the IaC with the agent.
- Review the AWS service choices manually.
- Check AWS documentation and lifecycle updates.
- Verify whether there is a newer or cheaper alternative.
- Re-test after any changes.
- Look at the deployed resources in the console, not just the code.
That extra validation step is worth it.
It helps avoid unnecessary cost, stale patterns, and avoidable cleanup later.
That’s where pragmatism matters.
I’m curious how others handle this
This was one case from my side, but I know I’m not the only one dealing with it.
I’d love to hear how other engineers are handling AI-generated code.
- Do you treat it as a draft and always review it manually?
- Do you have a fixed validation step before deployment?
- Have you caught legacy or outdated choices in code generated by an agent?
I’m especially interested in how people decide when to trust the output and when to slow down.
For me, this was a reminder that speed is useful, but correctness still has to win.
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