When Errors Become Narratives: A Longitudinal Taxonomy of Silent Failures in a Production LLM Agent Runtime

Most AI failure research is theoretical or laboratory-based — this paper is a rare longitudinal postmortem of a real production LLM agent system running continuously since early 2026, with 22 documented incidents over eight weeks. The most dangerous failure class identified is "fail-plausible": the agent doesn't just fail to report an error, it transforms the error into fluent, convincing narrative delivered to the user. The study finds that human observation catches ~70% of silent failures that tests and audits miss entirely, and that audit processes function as regression engines rather than predictive ones. The taxonomy and design principles derived are immediately actionable for anyone building or operating long-running autonomous AI systems. Authors: Wei Wu Paper: https://arxiv.org/abs/2606.14589v1