{
  "id": 2175071,
  "title": "AWS vector solutions: Build agentic AI where your data lives",
  "url": "https://urgent.news/2026/08/20/aws-vector-solutions-build-agentic-ai-where-your-data-lives",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-20T16:06:06.000Z",
  "source": {
    "name": "AWS Machine Learning",
    "slug": "aws-machine-learning",
    "url": "https://aws.amazon.com/blogs/machine-learning/aws-vector-solutions-build-agentic-ai-where-your-data-lives/"
  },
  "original_language": "en",
  "account": "Agentic AI is revolutionizing the way we work, and vector search is the retrieval layer that makes agents accurate and grounded in real data. Agents plan, reason, and take action across multi-step workflows, requiring fast and relevant access to organizational knowledge. AWS vector solutions bring intelligent search and retrieval to your data, where it already lives, helping agentic AI find and use the right context without moving or duplicating data.\n\nVectors are the language of AI, representing data as high-dimensional vectors to understand semantic meaning, identify relationships across various data types, and maintain context. They allow applications to compare and search across modalities, creating a shared mathematical space for text, images, audio, and video. By converting everything into vectors, AI can understand and process different types of data more effectively.\n\nThere are several ways to use vectors in agentic AI applications, such as retrieval augmented generation (RAG), semantic search, hybrid search, and graphRAG. Retrieval augmented generation combines AI models with trusted data retrieved at runtime, improving accuracy and reducing hallucinations. Semantic search retrieves information based on meaning and intent, while hybrid search combines lexical search with semantic search for comprehensive results. GraphRAG combines semantic search with knowledge graphs for accurate, context-rich, and traceable responses in enterprise scenarios requiring multi-step reasoning.\n\nAWS provides vector capabilities across various services and databases, allowing you to add vectors where your data already lives. This approach eliminates the need for data migration, reduces the learning curve for new tools, and takes advantage of existing data store capabilities. AWS offerings include Amazon OpenSearch Service, Amazon S3, Amazon Aurora PostgreSQL, Amazon DynamoDB, Amazon ElastiCache for Memcached, and Amazon Neptune, each optimized for different workload requirements.\n\nFor new workloads, Amazon OpenSearch Service is the default vector search solution, offering high throughput, low latency, and relevant results at scale. It combines lexical, vector, hybrid, and agentic search in a single system, making it an ideal choice for most agentic AI workloads. By following the data rather than the other way around, AWS vectors help you build accurate, context-aware, and personalized experiences while maximizing cost savings and performance.",
  "summary": "AWS offers a broad portfolio of vector search built directly into the databases and storage services you already use, with no standalone vector database or data migration required. This post covers six purpose-built services, a decision framework for choosing the right engine, and customer proof points for each.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}