How to Engineer Reliable Systems on Top of Unpredictable LLMs (pub.towardsai.net)

A new engineering framework—branded "Uncertainty Architecture"—argues that LLMs are not “just another API” and urges teams to treat them as a distinct architectural class. The writeup documents recurring failure modes (call‑center pilot success collapsing in production, silent drift, runaway costs) and makes the case that the industry lacks a shared methodology: roles, versioning, evaluation gates, and governance. Instead of chasing better prompts or new models, the framework says teams must accept unpredictability as an architectural constant and build processes and contracts around it. Technically, the guidance centers on treating variability as inevitable—caused by ordering/context effects, opaque internal state, batching/numerical quirks, tokenization differences and silent vendor or infra updates—and designing for safety when the model misbehaves. Practical prescriptions include JSON‑schema output contracts, prompt and model versioning, golden regression sets and eval gates, drift detection, per‑flow cost tracking, canary/blue‑green releases, fallback paths, quick rollback mechanisms, human‑in‑the‑loop review tooling, and an AI control plane (prompt/agent/orchestration governance). The implication for ML/engineering teams is clear: scale LLM systems only with operational discipline—otherwise fixes compound into unmanageable technical debt.