The line that stood out to me was: "an assistant answers you; an agent does the thing." A lot of teams talk about agentic AI as if it's a model upgrade, but in practice the hard part is everything around the model permissions, confirmation flows, retrieval strategy, observability, and failure handling.

We've seen a similar pattern at IT Path Solutions when building AI agents: the model is often the easiest component to swap, while the real engineering effort goes into creating safe execution paths and making sure mistakes are recoverable rather than expensive.

I also liked your principle: "Let the model be wrong cheaply, but never wrong expensively." That’s probably one of the clearest ways to explain production-grade agent design. Great breakdown of the gap between a demo assistant and a reliable agent.