Multi-Agent System Reliability

LLMs are unreliable out of the box, but multi-agent systems can improve by dividing work among specialized agents. Building robust systems involves leveraging human system patterns like hierarchy, consensus, adversarial debate, and knock-out in a multi-agent architecture to ensure correctness and reliability. To combat LLMs' stochastic nature, utilize multiple models in parallel to cancel out noise and improve accuracy. It's crucial to treat LLMs as unreliable components in a distributed system, emphasizing constraint, verification, pruning, and challenges over anthropomorphizing them.