A wave of recent research papers highlights a fundamental limitation of single large language models: they struggle with consistency and reliability across diverse, complex tasks. Studies show that while LLMs produce reliable outputs for straightforward cases, their performance degrades dramatically on more nuanced problems. Researchers have discovered that this inconsistency stems from two key issues: single-agent systems lack the specialized expertise needed for complex decision-making, and they fail to maintain safety standards across different domains. These findings suggest that the field's approach to deploying LLMs needs a significant shift from monolithic models toward collaborative multi-agent architectures.