The team behind the machine

Most enterprises can instrument their agents but can't prove they work: 89 percent have adopted observability while only 52 percent run evaluations, and quality is now the top barrier to getting agents into production. This article frames evaluation as the engineering discipline that closes that gap, covering what to measure across the end-to-end, trajectory, and component altitudes, how the offline-online-optimize lifecycle runs in practice, and how the native eval tooling from AWS, Google Cloud, and Microsoft compares to the third-party ecosystem.

The Forward Deployed Engineer model has gained traction because complex AI projects need technical experts who work directly with customers to build value quickly. But as enterprises move from experimentation to transforming core functions, technical skill alone falls short: the deciding factor is context, which is usually what's missing.

RAG and Long Context solve the same problem - getting enterprise data in front of an LLM through fundamentally different mechanisms, each with distinct tradeoffs around scale, cost, reasoning quality, and failure modes. Choosing between them, or combining both in a hybrid architecture, is one of the most consequential infrastructure decisions in enterprise AI deployment.