arXiv:2602.06511v3 Announce Type: replace Abstract: Large language model (LLM)-based multi-agent systems (MAS) show strong promise for complex reasoning, planning, and tool-augmented tasks, but designing effective MAS architectures remains labor-intensive, brittle, and hard to generalize. Existing automatic MAS generation methods either rely on code generation, which often leads to executability and robustness failures, or impose rigid architectural templates that limit expressiveness and adaptability. We propose Evolutionary Generation of Multi-Agent Systems (EvoMAS), which formulates MAS gen
The rapid development and increasing complexity of LLM-based multi-agent systems necessitate more efficient and adaptable design methodologies.
Automating the design of multi-agent systems addresses current bottlenecks in scalability and effectiveness, paving the way for more robust and powerful AI applications.
The ability to generate effective MAS architectures evolutionarily will reduce development time, improve system adaptability, and expand the feasible scope of agentic AI systems.
- · AI developers
- · Enterprises adopting AI agents
- · Software automation sector
- · LLM providers
- · Manual MAS architects
- · Companies with rigid AI-system development pipelines
EvoMAS offers a more flexible and robust alternative to current manual or code-generation based methods for multi-agent system design.
The improved design efficiency will accelerate the deployment and sophistication of autonomous AI agents across various industries.
This could lead to a significant acceleration in the automation of complex tasks and a redefinition of white-collar workflows, driven by advanced agentic systems.
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Read at arXiv cs.LG