A wave of new research papers reveals a fundamental limitation in current large language models: single-agent systems struggle with consistency and reliability when facing complex, heterogeneous tasks. Studies show that LLMs produce divergent outputs on identical queries when deployed alone, particularly in high-stakes domains like clinical prediction and behavioral health communication. This inconsistency stems partly from the models' susceptibility to minor variations in prompting and their inability to maintain specialized expertise across diverse problem types. The findings suggest that relying on monolithic AI systems for critical applications introduces unacceptable failure modes that restrict real-world deployment.