AWS enters context layer market with graph that learns from agents

Building a context layer between enterprise data stores and AI agents is a bespoke task with no standard service to automate or maintain the graphs over time. Amazon is making a direct play to change that. Amazon on Wednesday entered the space, announcing a series of three products it's positioning as a context intelligence stack for AI agents.

The centerpiece is AWS Context, a new knowledge graph service that gets smarter through agent usage over time. AWS also announced the general availability of Amazon S3 Annotations and a preview of skill assets in AWS Glue Data Catalog. The context layer is now a contested architectural category with no shortage of options from different vendors.

AWS is entering that market with a different architectural premise: that the graph should learn from how agents use it automatically, without human re-curation. "Your agents now get smarter without you having to rebuild anything from scratch," said Swami Sivasubramanian, vice president of Agentic AI at AWS, during his AWS Summit NYC keynote. "This service automatically builds a knowledge graph from all your existing data," he said.

"This service infers relationships across your data sets, business rules, and domain knowledge, and makes all of it available to your agents and your organization at runtime." AWS Context builds a self-learning knowledge graph from existing data. It's a problem AWS says it has seen repeatedly in customer deployments. AWS Context maps relationships across existing data automatically: what tables exist, what columns mean, how sources relate and which sources are authoritative.

It combines semantic search with graph-level reasoning and infers relationships across datasets, business rules and domain knowledge, making all of it available to agents at runtime. "The knowledge graph improves itself over time as it learns which sources produce correct results and which parts get used," Sivasubramanian said. Data stewards manage the graph through the AWS Management Console, reviewing inferred relationships, promoting them to production and attaching business definitions and usage rules.

Every query inherits the calling user's IAM and Lake Formation permissions, making agent data access auditable by identity through controls enterprises already rely on. All metadata is published in Apache Iceberg format to Amazon S3 Tables, queryable via Athena, Redshift, Spark or any Iceberg-compatible engine, with no proprietary APIs. Third-party catalog connections are supported, so context from systems outside AWS can be pulled into the same graph.

Agents query through agentic search APIs and MCP tools across Bedrock AgentCore, EKS or any MCP-compatible framework. Context is more than just a single service. Context is a complicated space and AWS is layering multiple services to help enterprises build context across the data stack.