The issue isn't the LLM's reasoning; it's the retrieval layer.
Most "Enterprise AI" is just a wrapper around a Vector DB doing cosine similarity. That’s probabilistic. It works 80% of the time, but for an enterprise product, the 20% hallucination rate on edge cases is a dealbreaker.
I spent the last 6 months trying to replace a legacy system with agents, and I hit this exact wall. I eventually had to rip out the Vector DB and replace it with a custom memory protocol using Optimal Transport (Wasserstein Distance) just to get deterministic retrieval.
If you treat memory as 'Geometry' (strict topology) instead of 'Search' (fuzzy matching), you can actually bound the hallucination error mathematically. It’s the only way I could sleep at night deploying this to production.
TL;DR: Yes, it’s realistic, but not if you use the standard RAG stack. You need stricter constraints on the context window.
The issue isn't the LLM's reasoning; it's the retrieval layer.
Most "Enterprise AI" is just a wrapper around a Vector DB doing cosine similarity. That’s probabilistic. It works 80% of the time, but for an enterprise product, the 20% hallucination rate on edge cases is a dealbreaker.
I spent the last 6 months trying to replace a legacy system with agents, and I hit this exact wall. I eventually had to rip out the Vector DB and replace it with a custom memory protocol using Optimal Transport (Wasserstein Distance) just to get deterministic retrieval.
If you treat memory as 'Geometry' (strict topology) instead of 'Search' (fuzzy matching), you can actually bound the hallucination error mathematically. It’s the only way I could sleep at night deploying this to production.
TL;DR: Yes, it’s realistic, but not if you use the standard RAG stack. You need stricter constraints on the context window.
Title: spelling "replacing".