How can agents efficiently share skills and code?
SkillFlow: Efficient Skill and Code Transfer Through Communication in Adapting AI Agents
April 9, 2025
https://arxiv.org/pdf/2504.06188SkillFlow enables AI agents to learn new skills (functions/code) from each other, improving their adaptability and efficiency. This decentralized skill-sharing framework reduces reliance on pre-defined capabilities and allows agents to handle increasingly complex tasks. Key points relevant to LLMs are:
- Skill acquisition improves efficiency, particularly when communication costs between agents are high, as demonstrated by simulations and a calendar scheduling agent example.
- SkillFlow's modular design facilitates skill discovery, transfer, and integration within existing agent architectures, enhancing tools like tool-calling.
- Decentralized skill registers maintained by individual agents offer an alternative to centralized skill databases, promoting flexibility and autonomy.
- The framework is inspired by biological systems like lateral gene transfer, suggesting potential for future research on agent evolution and adaptation.
- Challenges include security concerns around code transfer and the need for effective skill indexing in large-scale decentralized networks.