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The rapid ascent of agentic AI—autonomous systems capable of planning, tool use, and multi-step reasoning—has been framed as the final frontier of workforce automation. However, a significant discrepancy exists between the marketing narrative of 'total autonomy' and the operational reality in production environments. Recent empirical studies reveal that the majority of successfully deployed AI agents are not truly autonomous; instead, they operate within strictly bounded 'guardrails' where human intervention is not a fallback for failure, but a foundational requirement for reliability. Currently, 68% of production-ready agents require human intervention within just ten steps of execution, highlighting a 'scaling wall' where complexity outstrips the model's ability to maintain logical consistency.This research dive exposes the 'dirty secret' of the agentic era: the massive, often invisible human infrastructure required to sustain these systems. From the 'ghost work' economy of millions of low-wage data annotators and RLHF workers to the emerging 'Expert-in-the-Loop' (XITL) frameworks used in high-stakes industries like finance and law, human judgment remains the primary safeguard against cascading errors and hallucinations. As organizations move from experimental pilots to enterprise-grade deployments, the strategic focus is shifting from achieving 100% autonomy to mastering 'managed autonomy'—a paradigm where human orchestration is the key to unlocking measurable ROI in an increasingly non-deterministic landscape.