Agent Architecture

What Is the Mixture-of-Agents Pattern?

Mixture of Agents (MoA) is a pattern where several agents independently produce candidate answers to the same task, and an aggregator agent synthesises them into a single, stronger response. By combining diverse attempts — often from different models or prompts — MoA improves quality and robustness over any one agent acting alone.

Dishant Sethi ·Updated Jun 22, 2026

How does mixture of agents work?

Mixture of Agents runs the same task through several agents in parallel, then aggregates their outputs. Each agent may use a different model, prompt, or strategy, so they make different mistakes. An aggregator — itself an agent — reads all the candidates and produces a final answer that takes the strongest parts of each.

The intuition is the same as an ensemble in classical machine learning: diverse, independent attempts cancel out individual errors. One agent might hallucinate a fact another gets right; one might structure the answer well while another reasons more carefully. The aggregator's job is to reconcile them.

There are two common shapes. In a single-layer MoA, several proposers run once and an aggregator combines them. In a layered MoA, the aggregated output is fed back to another round of proposers, refining over multiple passes at the cost of more compute and latency.

Mixture of agents vs other multi-agent patterns

MoA solves a different problem than coordination-focused patterns.

PatternCore ideaBest for
Mixture of AgentsMany attempts, one synthesisMaximising answer quality
Orchestrator-SpecialistOne planner, many narrow workersComplex multi-capability tasks
Critic-RefinerOne drafts, one critiques, repeatIterative quality improvement

MoA is about breadth — exploring the solution space in parallel. Orchestrator-specialist is about division of labour. Critic-refiner is about depth through iteration. They compose: an orchestrator can delegate a hard sub-task to a mixture of specialists.

What are the trade-offs?

The cost of MoA is literal: running N agents plus an aggregator multiplies token spend and adds latency, especially in layered configurations. It is worth it when answer quality matters more than cost — high-stakes reasoning, content where errors are expensive — and wasteful for simple, high-volume tasks where a single cheaper model suffices. As with any quality technique, the right call comes from measuring whether the lift justifies the spend.

Frequently Asked Questions

A single model produces one answer from one reasoning path. Mixture of Agents produces several independent answers — often from different models or prompts — and synthesises them. Because the attempts make different mistakes, combining them cancels out individual errors, much like an ensemble in classical machine learning, yielding a more robust result than any one agent alone.

Yes. Running multiple proposer agents plus an aggregator multiplies token usage and latency, and layered MoA multiplies it further with each round. It's justified when answer quality is worth the spend — high-stakes or error-sensitive tasks — and overkill for simple, high-volume work where a single model is good enough.

Yes, and it often helps. Using different underlying models as proposers increases diversity, since each model has different strengths and failure modes. The aggregator can then draw the best from each. Mixing models is a common way to get more robust outputs than repeating the same model with different prompts.

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