This essay outlines a framework to enhance decision-making and service visibility in AI systems, indicating a shift towards agent-driven recommendations.
This essay introduces Agentic Recommendation Optimization (ARO), a framework for optimizing professional service visibility for AI systems that act as autonomous decision-makers. It defines the shift from human-driven search to agent-driven recommendation, proposes a five-layer verification stack, and documents infrastructure convergence in early 2026 supporting the framework's thesis.
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George Chip Holmes (2026) studied this question.
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