I recently worked through a practical problem that many enterprise teams will run into when using LLMs: how do you use powerful frontier models without exposing proprietary data unnecessarily? I cannot describe the exact use case, because it involves internal and proprietary information. So the example below is invented. But the architectural issue is the same. Imagine a financial-services workflow where a system receives an abbreviated security description such as: “UBS Grp 4.20% Call Sr Nts 30” and needs to resolve it into something more explicit: “UBS Group AG, 4.20% Callable Senior Notes, maturity 2030.” The obvious way to solve this is to give the task to a frontier model. It will probably do a good job. But in a real enterprise setting, the question is not only whether the model can solve the task. The more important question is: what else does the model see while solving it? That is where the architecture matters. There are at least two common ways to build this kind...