Capability without a boundary is a demo
Choosing a model is often the easiest part of an AI initiative. The harder work is deciding which goal the system serves, which inputs it may see, which outputs it may propose, and which effects it may never trigger alone.
Treat the model as a replaceable component behind a typed interface. Version prompts, tools, schemas, and evaluation cases the same way you would version an API. Prefer structured output validated against a schema over free text that downstream code must interpret.
Keep judgment in the loop
Probabilistic output is a recommendation until evidence, policy, and an accountable person say otherwise. Design the workflow so sources, assumptions, and uncertainty stay visible at the point of decision.
The useful question is not “how smart is the model?” It is “what remains safe, recoverable, and understandable when the model is wrong, slow, or unavailable?”