This talk focuses on Alibaba Cloud‘s latest research progress in retrieval-augmented system in large language model agents, exploring core technical pathways for knowledge storage, comprehension, reasoning, and planning, while proposing external information enhancement strategies to expand cognitive boundaries. For complex multi-modal search scenarios, it systematically elaborates an agent-based task planning framework and its autonomous decision-making capabilities. Furthermore, by analyzing practical implementations of knowledge enhancement technologies in AI Search applications such as Q&A and cross-modal retrieval, the study provides technical frameworks and actionable insights for building self-evolving agent systems that transcend traditional paradigms.