What is Mixture of Agents?

Mixture of Agents (MoA) extends ensemble methods to the era of large language models. Instead of using a single LLM for a task, MoA systems deploy multiple models or agents that each bring different strengths—some may excel at reasoning, others at creativity, coding, or factual accuracy. An aggregation mechanism combines their outputs.

In its simplest form, MoA generates responses from multiple LLMs and uses another LLM to synthesize the best elements into a final answer. More sophisticated implementations route different subtasks to specialized agents, create debate-style interactions where agents critique each other's outputs, or use hierarchical structures where a coordinator agent manages specialist agents.

The approach is motivated by the observation that different models have complementary strengths and weaknesses. A response that combines the factual accuracy of one model with the reasoning clarity of another can outperform either individually. This is analogous to how ensemble methods in traditional ML combine weak learners into a strong learner.

MoA patterns are increasingly used in production for high-stakes applications where reliability is paramount. Code review systems might use multiple models to check different aspects. Content generation systems might use one model for drafting and another for editing. The multi-agent paradigm is also central to frameworks like CrewAI and AutoGen.