Agentic AI is changing how you work, and vector search powers the retrieval layer that makes agents accurate, contextual, and grounded in real data. Agents plan, reason, and take action across multi-step workflows, making fast, relevant access to your organization’s knowledge essential.

That knowledge already has a home across databases, object stores, search engines, and unstructured sources such as PDFs, recorded video calls, and the systems your teams use every day. AWS vector solutions bring intelligent search and retrieval to your data where it already lives, helping agents find and use the right context without requiring you to move or duplicate your data.

For new workloads where no existing data store applies, we offer a clear decision model across six purpose-built solutions so you can choose the right vector solution for your agentic AI and analytics workloads.

Why vectors matter and top use cases for agentic AI

Vectors are the language of AI. They bridge frontier models and the scattered organizational knowledge accumulated over decades. By representing data as high-dimensional vectors, applications can understand semantic meaning, identify relationships across text, images, audio, and video, and maintain context across sessions. Whether you’re working with product descriptions, security logs, or media libraries, vectors convert everything into a shared mathematical space so you can compare and search across modalities.