Transfer Learning in Agentic Systems: Improving Cross-Task Knowledge Application in AI Agents

This paper presents a novel framework for enhancing transfer learning capabilities in artificial intelligence agents, focusing on the crucial challenge of knowledge generalization across diverse tasks. We introduce the Adaptive Knowledge Transfer Network (AKTN), a hierarchical architecture that enables AI agents to decompose learned behaviors into fundamental cognitive primitives and recombine them for novel task execution. Our research demonstrates significant improvements in cross-domain knowledge application, reducing the learning curve for new tasks by up to 73\% compared to traditional approaches.