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?