MaAS: Multi-agent Architecture Search (Agentic Supernet)
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Building multi-agent systems of LLMs (where multiple agents collaborate, each with specific roles or tools) is powerful but usually requires hand-designing a single complex pipeline. MaAS (Multi-agent Architecture Search) instead learns a universal “agentic supernet” from which it can spawn an optimal agent team on the fly for each query. It automates designing the agent workflow per task:
Agentic supernet – The authors define a continuous space of possible agent architectures (chains of LLM calls, tool uses, etc.). Rather than picking one static architecture, they train a supernet that encompasses many configurations. Each query can trigger a different sub-network of agents tailored to that query’s domain and difficulty.
Dynamic resource allocation – Because the system adapts per query, it can allocate resources efficiently. Easy questions might use a simple, fast agent chain; hard problems invoke a more elaborate reasoning team. This avoids the one-size-fits-all cost of a monolithic agent system.
Huge cost savings – On six benchmarks, MaAS used only 6–45% of the inference cost of existing multi-agent pipelines, yet still outperformed them by ~0.5–11.8% in accuracy. It finds cheaper ways to reach equal or better performance by tuning the agent configuration to the task.
Robust and transferable – The agentic supernet approach showed strong generalization: architectures found effective on one task transferred well to new domains and even with different LLM backbones, outperforming static designs. This suggests the method learns general principles of how to orchestrate LLM agents optimally.
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