AI agents are transforming e-commerce by unlocking new ways to engage customers, streamline operations, and automate decisions without human intervention. These autonomous systems combine autonomy, reasoning, and tool use to provide meaningful action, unlike GenAI which focuses on content creation. AI agents are already being used in retail today to automate workflows such as developer productivity, self-serve data analysis, and conversational product discovery. To support these applications, vector databases like Zilliz Cloud provide fast, accurate access to knowledge by enabling interpretive queries, accessing unstructured knowledge, and working across modalities. Vector databases are essential for effective agents as they need to retrieve information based on meaning, intent, context, past interactions, or descriptions that don't use the same wording as the data. Zilliz Cloud powers AI agents with features such as single-agent memory, multi-agent collaboration, autonomous RAG, chain-of-thought reasoning, and tenant-aware memory isolation. A purpose-built vector database like Zilliz Cloud is optimized for production-grade AI applications, providing sub-10ms vector search, hybrid search, multimodal support, and serverless or dedicated options. As agents reshape workflows and system architectures, developers will need to make tooling decisions that determine whether their system can support agent-driven experiences.