Autonomous AI agents face usability challenges where excessive token spending correlates with diminished returns, entering 'denial loops' where accuracy plateaus or degrades despite rising costs.

More tokens do not equate to more intelligence. In fact, research indicates a dangerous pattern: as token consumption increases beyond a certain point, an agent's accuracy stops improving and may even decline. This occurs when agents fall into what experts call a 'denial loop'—a state where they become stuck reprocessing the same information or making repetitive, unproductive corrections. Instead of converging on a solution, they spin their wheels, consuming ever more computational resources without gaining meaningful progress. This makes high-cost agent runs not just expensive, but potentially counterproductive, highlighting that the economic viability of agents depends on finding the sweet spot where autonomy delivers value without triggering wasteful, self-defeating cycles of over-analysis.