The cost of agentic AI workflows is forecast to increase more than fivefold by the end of 2028 as users adopt more complex applications of the technology.

As Nvidia and other tech giants push inference and agentic AI as the next stage of the AI wave, Gartner warns that the cost of implementing these systems will rise even as foundation models become cheaper.

The analyst firm has cast its eye over the nascent world of AI agents – systems designed to act independently in pursuit of a goal – and sees multiple challenges ahead.

Leaving aside the substantial security concerns, these software agents are considerably more complex than chatbots.

Gartner believes falling model prices are tempting users to build more complex workflows, whose greater token consumption can outweigh those savings and drive up overall inference costs.

In other words, tokens are becoming more cost-efficient, but those savings are not keeping pace with the rising cost of more advanced AI capabilities. The rate of innovation is outpacing the cost curve, Gartner claims.

“The harsh economics of the inference paradox are exemplified by the differences between a simple chatbot and an AI agent,” says Gartner senior director analyst Will Sommer.

“Where a simple chatbot must read and interpret a query and quickly respond with a probabilistic reasonable answer, an AI agent must constantly reason, negotiate, and question itself,” he explains.

Those processes add up: routing a task to an agentic reasoning model increases inference costs at least fivefold, and potentially by much more as the task becomes more complex.

Securing a return on investment from such advanced AI tools therefore demands either much greater returns than basic models provide or better optimisation of inference, routing, and orchestration, Gartner warns. This could mean assigning each task to the most cost-efficient model capable of handling it.

The move by some AI providers from flat-rate subscriptions to usage-based billing hasn’t helped. Token-heavy workflows can produce runaway costs under the new model.

Perhaps it is no wonder Gartner predicted earlier this year that 40 percent of organisations would demote or decommission AI agents because of problems with the heavily hyped technology.

The analyst biz also cheerily forecast last year that at least half of all generative AI projects would blow their budgets because of poor architectural choices and a lack of expertise, while most attempts to build custom models would be abandoned.