After months of building agents, I've changed my mind about what matters most.

I think a lot of people are underestimating how hard it is to get AI agents into production. Building a demo is easy. Making something that works reliably after thousands of runs is where things get interesting. A few months ago, I thought better models would solve most of our problems. They didn't. The biggest issues weren't intelligence-related at all. It was agents getting stuck in loops. Losing context between steps. Failing on weird edge cases. Not knowing when they should stop and ask a human for help. We spent far more time building safeguards and recovery mechanisms than improving prompts. One thing that surprised me: The model was rarely the bottleneck. Most modern models are good enough for many tasks. The hard part is everything around them. The monitoring. The state management. The retries. The handoffs. The failure handling. In other words, the boring stuff. The more agent systems I work with, the more I'm convinced that reliable orchestration matters more than squeezing another few percentage points out of a model. Curious if others have found the same. What's been the hardest part of moving agents from a prototype to something people actually use?