Context intelligence for your data and AI agents at scale
TL;DR
AWS used its New York Summit to announce a context push for AI agents: AWS Context is meant to map existing enterprise data into a knowledge graph and give agents governed data relationships, business rules, and domain knowledge at runtime. The AWS Context service is still coming soon. Data stewards are supposed to review inferred relationships, promote them to production, and enrich them with definitions or usage rules. Glue Data Catalog, SageMaker Unified Studio, and Lake Formation are part of the integration.
Nauti's Take
This is clearly AWS PR, but the underlying problem is real. Many agents fail because the model is not given clean meaning: which table means what, which join is valid, which rule applies to the answer.
AWS is packaging that missing layer as managed context. It becomes useful only if teams actually maintain data definitions, permissions, and runbooks instead of treating the knowledge graph as a magic cleanup machine.
Briefingshow
AWS is targeting a real agent problem: in companies, data, rules, and meaning often live in separate systems, which makes reliable AI workflows fragile. The approach shifts attention from more prompting to a managed context layer with permissions, metadata, and audit trails. The catch is that the central service is not live yet, and the announcement still reads like a platform strategy with an open Iceberg coating.
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