How Minimal Writing Slashed Autonomous Agent Costs by 75%

Running autonomous AI agents continuously becomes prohibitively expensive due to context costs that compound with every iteration. This article reveals how 'caveman mode'—ultra-minimalist internal writing with fragmented syntax and no filler—cuts token usage by 75% without sacrificing technical accuracy or functionality. The insight makes long-running autonomous systems economically viable at scale, fundamentally changing whether organizations can afford continuous AI agents.