Production AI agents fail on infra, not models

A developer logging every failure across multiple production AI agent deployments has published a breakdown showing that infrastructure problems — not model hallucinations or capability gaps — account for the majority of real-world agent errors. The failure taxonomy is specific: API schema drift causes tool calls to silently misfire as upstream services update without notice; context loss accumulates across long sessions until agents lose coherent task state; sandboxing gaps let agents take unintended side effects that are difficult to reverse; and brittle prompt engineering collapses under input distributions the demo never saw. The author's core argument is that most successful demos survive by constraining the workspace to a narrow, controlled environment rather than by solving the underlying coordination problem. Essentially: (anonymous practitioner, r/agi community) are surfacing that the AI agent deployment gap is an ops and tooling problem more than a frontier-model problem. - Tool-call failures from API schema drift rank as the single most frequent failure mode in the dataset. - Context management across multi-step sessions degrades reliably without explicit windowing or summarization strategies. - Absent sandboxing, agents operating on live systems produce hard-to-reverse errors that compound across retries. The thread is drawing substantial practitioner engagement, suggesting the pattern is widely recognized but underreported in benchmarks and product announcements.