A lot of AI products make the same architectural mistake. They place the model in the middle of every path because the model seems capable of doing almost everything.
That assumption is expensive.
Language models are excellent at interpretation, ambiguity resolution, and helping refine intent. They are bad candidates for tasks where the correct answer can be derived exactly. Routing, formatting, extraction, arithmetic, list retrieval, and structured transformations should not wait behind an LLM call.
The problem is not only cost. It is predictability. Every time a deterministic path is delegated to a model, the system becomes slower, harder to observe, and more vulnerable to drift.
I learned this the unglamorous way. A fast path that should have been handled with simple parsing and branching was being sent through a model. The result was extra latency, inconsistent formatting, and occasional nonsense on paths that should have had zero ambiguity.
The better pattern is simple.
Use code for certainty. Use the model for uncertainty. Build the handoff between them deliberately.
That changes the character of the system. The deterministic layer becomes fast and inspectable. The model becomes a specialist rather than an overlord. Human review becomes easier because the system is clearer about what was computed and what was interpreted.
This is the difference between “AI everywhere” and systems architecture. The former sounds ambitious. The latter actually scales.