Your AI System Doesn't Have a Cost Problem. It Has No Runtime Limits.

You built the alert. You configured the dashboard. You set the anomaly threshold at 120% of baseline spend. And your agentic pipeline still ran $40,000 over budget last quarter. Not because the tools failed. Because alerts and dashboards are not cost controls. They are cost witnesses . They record what happened. They cannot stop what is about to happen. This is the core architectural gap in most AI inference deployments in 2026: teams have invested heavily in visibility infrastructure and almost nothing in enforcement infrastructure. The result is organizations that can tell you — in impressive detail — exactly how they exceeded their budget, but had no mechanism in place to prevent it. Part 1 of this series established why AI inference cost emerges from behavior, not provisioning, and why static budget models break under agentic workloads. Part 2 is the solution layer. Execution budgets. What they are, where they live in your architecture, how to model them before production, and what