What AI gets wrong and what failure teaches us

In a Microsoft Research Podcast conversation with Chad Atalla, Microsoft partner research manager Jennifer Neville discusses how her team uses practical evaluations—multiturn conversations, collaborative environments and long-horizon agentic tasks—to find where current AI systems break down beyond traditional benchmarks. She describes research showing that model performance degrades significantly over multiturn interactions and that errors can accumulate in long workflows, and offers practical guidance such as restarting a chat with a fully specified single turn and checking answers rather than fully delegating work.