Datadog surveyed over 1,000 organizations running AI in production. The report is framed around observability and operational maturity. Read carefully, it is also the clearest empirical signal yet that the industry's next unsolved problem is governance. Most industry reports on AI engineering measure what is easy to measure: adoption rates, token volumes, model preferences, framework usage. Datadog's State of AI Engineering 2026 does all of that -- and then, in a handful of sentences buried across four findings, says something the AI tooling industry has been reluctant to say directly. The report does not use the word "governance" as its organizing frame. It talks about observability, operational discipline, and the maturation of production systems. But the data it surfaces -- model churn rates, context composition, error clustering, agent complexity -- all point to the same structural gap. The industry has scaled AI execution faster than it has scaled AI constraint enforcement.…