The contrarian take: most agent failures are not the model's fault. They are architectural. Here is what I have debugged in real deployments and the exact fix for each.
Here is an opinion that has cost me clients and then won them back: most AI agents fail in production because of the loop, not the model. Everyone wants the failure to be exciting — a hallucination, an "emergent" behavior, an AGI moment gone wrong. The reality is more boring and far more fixable. I have debugged agent systems for a fintech, a logistics company, and a SaaS vendor, and every single production failure I have touched traced back to one of seven architectural causes. None of them required a better model. Most of them were fixed with a hundred lines of code and a decision about what "done" means.
This is the article I wish existed when I started shipping agents. It is opinionated, because the industry is drowning in enthusiasm and starved of post-mortems. I am going to name the seven failure modes I have actually seen, the fix for each, and the forward-looking claim that follows: agents will not fail because they are too dumb. They will fail because we designed them without a theory of when to stop.
The Contrarian Claim
