For-profit model-makers, many of which are spending billions on AI infrastructure, will be chasing revenue from just ten percent of steps involved in agentic AI workflows, according to analyst firm Gartner.
“Nine in 10 agent workflow steps are routine, and open-weight models handle them at parity for a quarter of the price of the cheapest closed model,” the firm’s senior director Mark Petty and group vice president Raymond Paquet wrote last week in new document titled “Open-Weight Models Will Run 90% of Enterprise Agentic Workflow Steps.”
“Typical operational workflows involve hundreds of routine executions, such as classification, tool calling, passing, validating and reconciling data,” the pair observe, before suggesting open-weight models “can handle hundreds of these routine steps for every one that needs frontier reasoning.”
“Against the cheapest closed model at the same score, the open model costs a quarter as much. Against a frontier model run on every step, it costs as little as a twentieth,” the pair add.
Those are the best-case numbers, because the analysts admit “Current open models can be more verbose and retry more often, so the saving per task against a frontier model is closer to five or six times.”
That’s still a lovely saving, but the analysts rate it as the welcome by-product of moving to open models rather than an end in itself.
“Years of workflow history are a large part of what an acquirer pays for in a SaaS company and every routine step sent to a frontier API moves a slice of it outside your perimeter,” the pair wrote. “Open-weights preserve your control. The cost saving is a bonus.”
The two analysts therefore offer the following strategic planning assumption: “By 2030, 90% of enterprise operational agent workflow steps will execute on open-weight models.”
They argue that enterprises will mostly adopt domain-specific open-weight models, because they do well “in scenarios where a small number of highly stable steps run millions of times a month.”
Most models, however, are already commodities. Harnesses, the tools that run agents and manage agents, are the source of value in AI products, because one of their roles is to route work to the most appropriate model for a job, without users noticing any impact. A good harness saves money, imposes strong guardrails, and manages agent fleets. Less powerful harnesses leave users with more to do.
The paper suggests frontier models will continue to have a role as the engine of research and development and are effectively capital expense. Open-weight models become an operational expense after frontier models do the heavy lifting. ®
