Every regulated AI system eventually meets a decision it shouldn't take alone. The engineering discipline isn't about whether to escalate — it's about designing an escalation layer that a regulator will still recognise as human oversight three years from now, when the auditor asks who actually decided.

Before we begin: This is article 9 in a series on the engineering economics of regulated AI. Prior pieces have covered provenance graphs as core infrastructure, multimodal validation for climate risk, runtime enforcement in place of pre-deployment approval, the reproducibility crisis coming for enterprise LLMs, attribution stability over attribution accuracy, deterministic orchestration via state machines, and the cost model for auditable AI. The next piece will address audit replay — the mechanism by which persisted state becomes retrospective evidence of specific past decisions.

Six months into a production ESG validation pipeline, we caught something the design documents hadn't anticipated.…