LLMs are powerful, but enterprises are deterministic by nature
Over the last year, we’ve been experimenting with LLMs inside enterprise systems.
What keeps surfacing is a fundamental mismatch: LLMs are probabilistic and non-deterministic, while enterprises are built on predictability, auditability, and accountability.
Most current approaches try to “tame” LLMs with prompts, retries, or heuristics. That works for demos, but starts breaking down when you need explainability, policy enforcement, or post-incident accountability.
We’ve found that treating LLMs as suggestion engines rather than decision makers changes the architecture completely. The actual execution needs to live in a deterministic control layer that can enforce rules, log decisions, and fail safely.
Curious how others here are handling this gap between probabilistic AI and deterministic enterprise systems. Are you seeing similar issues in production?
> LLMs are probabilistic and non-deterministic
This is a polite way of saying unreliable and untrustworthy.
The problem facing enterprise is best understood by viewing LLMs as any other unreliable program.
> We’ve found that treating LLMs as suggestion engines rather than decision makers changes the architecture completely.
Figures. Look at the disruption LLM "suggestions" are inflicting on scientific journals, court cases and open source projects wordwide.
If enterprises are deterministic, that’s what a coding LLMs are for. To create the deterministic part with the help of the LLM.