arXiv:2602.03695v2 Announce Type: replace-cross Abstract: While existing multi-agent systems (MAS) can handle complex problems by enabling collaboration among multiple agents, they are often highly task-specific, relying on manually crafted agent roles and interaction prompts, which leads to increased architectural complexity and limited reusability across tasks. Moreover, most MAS communicate primarily through natural language, making them vulnerable to error accumulation and instability in long-context, multi-stage interactions within internal agent histories. In this work, we propose \textb
The increasing complexity and task-specificity of current multi-agent systems necessitate a more efficient and reusable approach to agent design.
This development proposes a method to create more robust, scalable, and less 'brittle' AI agents, which are crucial for broader deployment and impact.
AI agents could become more modular and adaptable across diverse tasks, moving away from purely task-specific designs prone to instability.
- · AI software developers
- · Enterprises adopting AI agents
- · Research institutions
- · Companies with highly specialized, non-reusable agent architectures
Increased efficiency in developing and deploying complex multi-agent systems.
Accelerated proliferation of autonomous AI agents across various industry verticals.
The abstraction of agent 'primitives' could lead to new programming paradigms for AI systems.
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Read at arXiv cs.CL