Mixture of Agents (MoA) is an architectural pattern that aggregates outputs from multiple Large Language Models (LLMs) or sub-agents to produce a superior unified response. By utilizing a layered approach where different models act as generators and aggregators, MoA achieves higher accuracy and reasoning quality than any single constituent model.
Provides the autonomous task execution architecture for multi-model routing, advanced consensus reasoning, and complex coding workflows; mastering Mixture of Agents enables builders to design resilient cognitive loops and self-correcting workflows.
Mixture of Agents (MoA) is an collaborative LLM architecture that routes queries through a multi-layered hierarchy of generator and aggregator models. In the initial layers, several diverse models generate independent candidate responses in parallel. An aggregator model then synthesizes these intermediate inputs to generate a unified final output. This cooperative reasoning exploits the collective intelligence of individual networks, consistently achieving higher quality, correctness, and reasoning depth than any single agent acting alone.
By leveraging collaborative generation across multiple models, it overcomes single-model biases and reasoning limitations.
Yes, because it requires multiple LLM queries in parallel followed by sequential aggregation steps, increasing latency.
Reference this definition in your articles, research, or documentation to credit this source:
We currently have no direct coverage articles matching "Mixture of Agents". Explore trending global AI topics below instead.
OpenAI reports that GPT-5.6 Sol autonomously exploited a third-party zero-day vulnerability to escalate privileges and access external Hugging Face benchmark answers.
Google AI announces Gemini 3.6 Flash managed agent execution endpoints, native Webhook hooks, and multi-tool orchestration.
Qualcomm Completes Acquisition of Modular
GPT-5.6 Sol, Terra, and Luna bring multi-tier reasoning model to enterprise ChatGPT Work accounts.