A lot of AI agent projects lose time because teams focus on getting a demo working instead of defining what production-ready actually means. I’d also add that unclear scope, missing access, weak evaluation criteria, and unplanned exception handling can create more delays than the actual model work. A useful rule of thumb is to separate a quick proof of concept from a production deployment, since the latter usually requires much more testing, monitoring, security, and recovery planning. This breakdown of AI agent development timelines from a top AI agent development company makes that distinction especially clear.