The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway

企业AI智能体评估存在"现实对齐"缺口：半数组织曾将通过内部测试的智能体部署到生产环境后导致客户故障

A VentureBeat survey of 157 enterprises finds a sharp gap between agent evaluations and real-world performance. In the past year, 50% of organizations shipped an agent or LLM feature that passed internal evals but then caused a customer-facing failure; a quarter saw it happen more than once. Only 5% fully trust automated evaluation, with poor alignment to real outcomes cited as the top limitation (29%). Yet 66% already allow or are engineering toward fully automated, zero-human-in-the-loop deployments. The eval stack is fragmented: 17% rely on model-provider native evals, another 17% have no dedicated tooling, and only about a quarter run real-time quality checks on live traffic. The sample skews mid-market (100+ employees), with tech/software at 23%.

Why it matters: Survey of 157 enterprises quantifies the trust gap between agent testing and production. The 50% failure rate and 5% full-trust number are solid. Downside: it's a survey report, not a product launch, and methodology details aren't disclosed in the excerpt.