How to decide what should stay deterministic in an AI system
The easiest way to make an AI system unreliable is to give the model responsibility for work that normal software should still own.
One of the fastest ways to create a fragile AI product is to push too much responsibility into the model layer.
A language model can interpret, draft, summarize, and synthesize. That does not mean it should own every decision path in the system.
Start with the risk of being wrong
Ask a simple question:
What happens if the model gets this wrong silently?
If the cost is high, the workflow should probably stay deterministic or have a strong verification layer around it.
That includes things like:
- billing calculations
- permission rules
- compliance checks
- routing logic with hard requirements
- destructive actions
Those are usually poor places to rely on free-form model behavior.
Keep language problems with the model
The model is strongest where the problem is genuinely fuzzy:
- summarization
- classification with soft boundaries
- drafting
- extraction from messy text
- search interpretation
That is where probabilistic systems create leverage.
Keep exact logic in software
If the task needs:
- stable rules
- repeatable execution
- exact arithmetic
- explicit branching
- strong guarantees
normal software should still own it.
The model can help around the edges, but it should not become the source of truth for logic that needs determinism.
Hybrid systems are usually better
The strongest systems often split responsibilities:
- the model interprets the request
- deterministic code validates the intent
- tools or APIs execute the actual operation
- post-checks verify the result
That pattern is far more reliable than asking the model to be both interpreter and execution engine for everything.
A practical rule
Use the model where ambiguity is the problem.
Use deterministic software where correctness is the requirement.
The mistake is assuming one should replace the other.