# Functional Doesn’t Mean Correct: Why AI-Generated IaC Still Needs Human Validation > Source: > Published: 2026-06-30 13:14:52+00:00 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. 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.** 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: That is a dangerous combination if you are not validating it carefully. 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: That extra validation step is worth it. It helps avoid unnecessary cost, stale patterns, and avoidable cleanup later. That’s where pragmatism matters. 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. 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.