Engineering economics
Cost per verified AI delivery measures the total execution, infrastructure, retry and review cost required to produce an accepted, integrable software change. It is more informative than token cost or lines generated because it includes failed attempts and quality gates that determine whether agent output becomes useful product state.
A delivery should represent a bounded change that passed its configured tests, independent review and integration decision. Counting raw agent tasks rewards fragmentation and can hide repeated remediation.
Teams should define comparable slice size and evidence requirements before using the metric.
A cheaper model is not cheaper if it creates more failed reviews. A stronger model is not automatically economical if routine work waits behind it.
Routing experiments should compare representative task classes and retain failure reasons.
Nexus records work state, evidence and review outcomes that can support delivery-level economics. Pricing is based on active-project capacity rather than per-prompt token resale.
Predictability still depends on workload, verification depth and model route; no single benchmark describes every codebase.
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