The Exit Strategy Problem: Why Most Autonomous Agents Aren't Real

Most current 'autonomous agent' implementations are fundamentally just while loops that call language models repeatedly without genuine exit conditions, driving unnecessary token costs and wasted API spending. The article argues that true agents require crisp, externally-verifiable success criteria—not LLM self-assessment—and accountability that goes beyond arbitrary iteration caps. As AI systems become increasingly central to business operations, understanding this distinction becomes critical for organizations trying to build cost-effective and genuinely intelligent autonomous systems.