Patterns for Agent Diversity in Multi-Agent LLM Systems
When you run multiple LLM agents together, the most likely outcome is collapse: every agent converges on the same answer, echoes the others, and you end up paying for expensive consensus that a single call could have produced. This happens because the default pressure in most multi-agent setups is agreement. Agents read each other’s outputs and optimize for coherence. Without an opposing force, there’s no reason for any agent to disagree. ...