The article separates throughput, intelligence, autonomy, and business value rather than treating an AI deployment as a single improvement score.
It cites a study of 260 agent-system configurations across six benchmarks, where multi-agent performance reportedly ranged from an 80.8% improvement on decomposable financial reasoning to a 70% decline on sequential planning. It also points to Anthropic’s reported CI-job growth as an illustration that faster agentic output can move, rather than remove, a constraint.
The operational prescription is to trace completed work through waiting, review, repair, exceptions, and acceptance—and to define scenario indicators that would justify changing the plan.