This is why at the AWS Summit New York City, we’re announcing a series of innovations that deliver context intelligence for your data and AI agents at scale.

In today’s keynote, we introduced AWS Context, a new service that automatically maps the relationships across your existing data into a knowledge graph and provides agentic search so AI agents in the organization can access governed data relationships, business rules, and domain knowledge at runtime. Data stewards and curators manage the graph through an intuitive console experience, reviewing inferred relationships, promoting them to production, and attaching domain-specific knowledge like business definitions and usage rules.

AWS Context extends the same knowledge graph technology that powers Amazon Quick, where hundreds of thousands of users interact daily with a production knowledge graph that catalogs datasets, dashboards, and metadata, learning from usage patterns to make every interaction smarter. That graph already processes millions of requests per day. With AWS Context, we are extending what was a personal knowledge graph into an organizational one, a shared, governed context layer that agents and applications in your organization can draw from. Existing Amazon Quick users benefit immediately. When AWS Context is enabled, Quick’s agents gain access to the broader enterprise knowledge graph, including cross-system relationships, business rules, and curated context that go beyond what any single user’s personal graph can provide. AWS Glue Data Catalog, Amazon SageMaker Unified Studio, and AWS Lake Formation integrate with the knowledge graph, so teams can govern it with business rules and permissions and add new context automatically with AI assistance or explicitly through manual curation.

Key elements of the context layer are published to Amazon S3 in the Apache Iceberg format, so that customers are free to use the Iceberg-compliant tools of their choice to consume metadata and build against AWS Context based on open standards. There is no infrastructure to provision or retrieval pipeline to build, and customers can begin to gather and curate context for their agents with just a few clicks in the AWS Management Console.

AWS Context gets smarter the more your agents use it. As agents query the graph, it observes which sources produce correct results, which join paths agents rely on, and which curated rules get applied. It ranks sources by actual usage and shares what it learns across your organization, so when one agent discovers a correct join path or resolves a schema ambiguity, other agents pick it up, without requiring a human re-curate the graph.