Mixture of Agentsdefinition and how it works in 2026
- Mixture of Agents
- An architecture where multiple agents (often using different models) generate candidate responses, then an aggregator agent synthesizes them. Higher quality at higher cost.
Mixture of Agents (MoA), introduced by Together AI in 2024, runs several "proposer" agents in parallel on the same task — typically using different underlying models — then has an "aggregator" agent synthesize the best response from the candidates. The aggregator can be the same architecture as the proposers or a stronger model.
The technique consistently beats single-agent baselines on benchmarks like AlpacaEval and MT-Bench, often by 5–15 points. The cost is real: 3–6× the inference of single-agent runs. For high-stakes work where quality matters more than latency or cost, MoA is one of the most-defensible quality lifts available in 2026.
Production deployments use MoA selectively — on the hardest 5–15% of queries that single-agent runs flag as low-confidence — rather than on every request. That hybrid pattern captures most of the quality lift at a small fraction of the cost.
Frequently asked
When does Mixture of Agents pay off?+
When output quality matters more than latency or cost — research agents, complex coding tasks, regulated-domain Q&A. Skip MoA when the workflow is high-volume + low-stakes (e.g. tier-1 support); the cost premium doesn't pencil.
How is MoA different from chain-of-agents?+
MoA runs agents in parallel and aggregates. Chain-of-agents runs them sequentially, each refining the previous output. MoA is better for divergent thinking; chain is better for iterative depth.