arXiv:2605.22177v1 Announce Type: new Abstract: The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on monolithic LLMs and fixed logic to interface with these skills. This gives rise to a critical bottleneck: different LLMs offer distinct advantages across diverse domains, yet current frameworks fail to exploit the complementary strengths of models and skills, thereby limiting their performance on downstream tasks. In this paper, we present Maestro (Multimodal Agent for Ex
The proliferation of various LLMs and specialized skills creates a clear need for advanced orchestration to maximize their complementary strengths, which existing monolithic frameworks fail to address.
This development allows for more efficient and powerful AI agents by dynamically leveraging the distinct advantages of different models and skills, overcoming current performance limitations.
AI agent architectures will evolve from monolithic LLM reliance to sophisticated, hierarchical orchestration of diverse models and skills, leading to more adaptable and capable autonomous systems.
- · AI platform developers
- · Enterprises adopting AI agents
- · Specialized AI model developers
- · Cloud providers
- · Developers of monolithic AI solutions
- · Fixed-logic automation frameworks
- · Companies relying on single LLM strategies
Improved performance and broader applicability of AI agents across complex tasks.
Accelerated development of highly specialized and interconnected AI services and applications.
Enhanced competition among AI model developers as orchestration capabilities highlight specific model strengths and weaknesses, fostering innovation.
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Read at arXiv cs.LG