AWS Context automatically builds and updates a knowledge graph from existing enterprise data, aiming to eliminate manual re-curation as agents learn from usage.
Amazon Web Services on Wednesday announced a trio of products it is positioning as a context intelligence stack for AI agents, with a self-learning knowledge graph service called AWS Context at the center. [1]
The context layer — the architectural tier that sits between enterprise data stores and AI agents, giving those agents the structured knowledge they need to answer questions accurately — has become a contested product category, with offerings from Snowflake, Microsoft, Redis, and Pinecone already in the market. [1] AWS is entering that space with a different premise: the graph should update itself based on how agents actually use it, rather than requiring human re-curation. [1]
“This service automatically builds a knowledge graph from all your existing data,” said Swami Sivasubramanian, vice president of Agentic AI at AWS, during his AWS Summit NYC keynote. [1] The service maps relationships across existing data — what tables exist, what columns mean, how sources relate, and which sources are authoritative — and combines semantic search with graph-level reasoning to make that context available to agents at runtime. [1]
“The knowledge graph improves itself over time as it learns which sources produce correct results and which parts get used,” Sivasubramanian said. [1] Data stewards can review inferred relationships, promote them to production, and attach business definitions and usage rules through the AWS Management Console. [1]
On the security and governance side, every query inherits the calling user’s AWS Identity and Access Management (IAM) and Lake Formation permissions, making agent data access auditable by identity through controls enterprises already rely on. [1] All metadata is published in Apache Iceberg format to Amazon S3 Tables and is queryable via Athena, Redshift, Spark, or any Iceberg-compatible engine, with no proprietary APIs required. [1]
AWS Context is not a standalone product. It synthesizes inputs from two additional services announced alongside it. [1] Amazon S3 Annotations — now generally available — lets users attach rich business context directly to individual S3 objects at the storage layer. [1] A preview of skill assets in AWS Glue Data Catalog, the company’s metadata management service, allows domain knowledge such as runbooks, query patterns, and usage rules to be linked to data assets across an organization’s data estate. [1] AWS Context then pulls both layers together into the knowledge graph that agents query at runtime. [1]
Agents can query the graph through agentic search application programming interfaces (APIs) and Model Context Protocol (MCP) tools across Bedrock AgentCore, Amazon Elastic Kubernetes Service (EKS), or any MCP-compatible framework. [1] Third-party catalog connections are also supported, so context from systems outside AWS can be incorporated into the same graph. [1]
AWS’s pitch to enterprises already running S3, Glue, and Lake Formation is that AWS Context extends an existing identity model with no data movement required. [1] Competitors in the space include Snowflake, which announced its Horizon Context and Cortex Sense services earlier this month; Microsoft, which provides context via its Fabric IQ platform using a semantic ontology for data; Redis, which has developed a context platform optimized for retrieval; and Pinecone, whose Nexus offering compiles enterprise data into task-specific artifacts before agents query them. [1]
Holger Mueller, vice president and principal analyst at Constellation Research, told VentureBeat that “context makes agents more powerful and as the whole world is building agents, every agentic platform vendor needs a context capability.” [1] Mueller also flagged a potential limitation shared across the category: “The concern — as with all context offerings — is going to be performance, especially for transactional data,” he said. [1]
Sources
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