A wave of recent research reveals a fundamental limitation of single large language models: they struggle with consistency and reliability in complex, high-stakes scenarios. Clinical prediction systems exhibit dramatic variance when processing complicated cases, educational AI tools drift from their intended objectives, and tool-integrated agents fail unpredictably when invoking external resources. These failures suggest that relying on monolithic LLMs for sophisticated tasks represents an architectural dead-end, prompting researchers to explore whether distributed, multi-agent systems can overcome these bottlenecks through specialization and orchestration.