What Is Mixture-of-Agents (MoA) and How Does It Boost Performance?

MoA (Mixture-of-Agents) , also called the mixed-expert mode, is a quality-first multi-agent collaboration approach. Unlike ordinary parallel agents that prioritize efficiency, MoA emphasizes result quality.

The basic workflow starts with multiple large models (the Proposers ) simultaneously tackling the same task. Because each model has different strengths—some excel at logical reasoning, others at expression or detail accuracy—they produce answers with varied styles and focuses.

An Aggregator then evaluates all outputs, identifies each answer’s strengths and weaknesses, and fuses the strengths into a more complete and reliable final result.

Advantages include leveraging model diversity to improve robustness; even if one model performs poorly, the combined output remains high-quality, often surpassing any single model’s answer.

Disadvantages are higher cost and greater latency, essentially trading resources for quality, making MoA suitable for critical tasks rather than everyday scenarios.

Suitable scenarios are high-value, open-ended, creative tasks such as core copywriting, brand storytelling, or complex analysis, where no single correct answer exists and completeness and stability matter.

Extensions can add multiple aggregation layers: after an initial aggregation, a next-level model refines the result, and further layers can be added for even higher quality at the expense of additional cost.