4posts
Every enterprise AI procurement follows the same script: demo works, POC metrics look good, checklist gets checked, someone signs. Then six months later the team is in a room figuring out why the system produces confidently wrong outputs on real data. The evaluation was not wrong. It was answering the wrong question.
Guardrail products are a category error. LLMs fail deterministically in production not because they lack safety filters, but because the architecture treats the model as trusted when it should be treated as an untrusted proposer. The fix is structural, not commercial.
88% of enterprise AI pilots never reach production. The failure is not the model, and it is not the data. It is the assumption that a pilot and a production system are the same kind of thing. They are not.
79% of multi-agent failures come from poor specification and coordination, not model capability. The industry is building runtime guardrails around systems that were never correctly defined to begin with. That is not an engineering investment. It is expensive procrastination.