The Agent Layer Is Where Mid-Market AI Finally Pays
Pilots stalled because models were treated as products. The teams pulling ahead are wiring agents into billing, support and supply workflows — and measuring cost per resolved task, not per token.
Most mid-market AI programmes did not fail because the models were weak. They failed because a model was treated as a product, dropped next to a workflow rather than inside it, and measured on novelty instead of throughput.
The teams pulling ahead have moved one layer up. They build agents that hold context, call internal systems, and complete a unit of work end to end — a disputed invoice reconciled, a support ticket resolved, a supplier delay re-planned. The model is a component, not the deliverable.
That shift changes the economics. Cost per token is a vendor metric; cost per resolved task is a business metric. Once a team can quote the second number, the conversation with the CFO stops being about experimentation budget and starts being about unit cost.
Our guidance is blunt: pick three workflows with a measurable cost per transaction, instrument them before you automate anything, and give each agent a narrow mandate with a human escalation path. Breadth comes later — and only after the first number moves.