Mixture of Agents
An architecture where multiple different AI models collaborate on a task, with each model contributing its strengths. A routing or aggregation layer combines their outputs.
Why It Matters
Mixture of agents can outperform any individual model by leveraging the complementary strengths of different architectures and training approaches.
Example
Routing math questions to a math-specialized model, creative writing to a creative model, and code generation to a code model — each handles what it does best.
Think of it like...
Like a panel of experts where the tax accountant handles tax questions, the lawyer handles legal questions, and the doctor handles medical questions — better than one generalist.
Related Terms
Multi-Agent System
An architecture where multiple AI agents collaborate, each with specialized roles or capabilities, to accomplish complex tasks that no single agent could handle alone.
Mixture of Experts
An architecture where a model consists of multiple specialized sub-networks (experts) and a gating mechanism that routes each input to only the most relevant experts. Only a fraction of the total parameters are active per input.
Ensemble Learning
A strategy that combines multiple models to produce better predictions than any single model alone. Ensemble methods leverage the diversity of different models to reduce errors.
Orchestration
The coordination and management of multiple AI components, tools, and services to accomplish complex workflows. Orchestration handles routing, sequencing, error handling, and resource allocation.