AWS Unveils 'Context Intelligence' to Help AI Agents Reason Over Business Data

At the AWS Summit in New York City, Amazon Web Services unveiled a set of new "context intelligence" capabilities aimed at helping AI agents find and reason over a company's data. The announcements were detailed in a blog post by Mai-Lan Tomsen Bukovec, Technology Vice President at AWS, dated 17 June 2026, with the keynote delivered by Swami Sivasubramanian, AWS VP of Agentic AI.

The headline launch is AWS Context, a new service that automatically builds a knowledge graph from a company's existing data. It infers relationships between data assets, business rules and domain knowledge, then makes that map available to every AI agent across the organisation. AWS describes the service as "coming soon."

The reporting has been independently corroborated by outlets including TechTarget, The Register and GeekWire, alongside Amazon's own newsroom — confirming the announcements are current, genuine news rather than recycled material.

The idea behind AWS Context is straightforward: instead of every team building its own pipeline to feed data into AI tools, the whole organisation draws from one shared, governed map of its information. Agents can then understand how different datasets, dashboards and rules connect to one another.

According to the announcement, AWS Context extends the same knowledge graph technology that powers Amazon Quick, an agentic AI assistant for business employees. TechTarget independently confirmed this, noting that Quick's knowledge graph catalogs datasets, dashboards and metadata, and learns from usage patterns. The original post says Quick serves hundreds of thousands of users and processes millions of requests per day.

The key difference is scope: where Quick is personal, AWS Context is organisational. It publishes its metadata into Amazon S3 tables in Apache Iceberg format — an open, widely supported design — making the underlying data accessible without locking it into a single proprietary structure.

A central theme of the announcement is governance. AWS Context includes built-in governance that restricts the data agentic AI tools can access, and shows what agents accessed and under whose authority.

Crucially, all queries are identity-aware, meaning an agent can only see information it is authorised to access. For organisations wary of letting autonomous tools loose on sensitive records — think client files in a professional services firm, patient information in an allied health practice, or financial records in any business — this is a significant design choice.

It reflects a broader industry challenge: making AI agents genuinely useful while keeping a clear, auditable line around what they can and can't see.

AWS Context arrived alongside several related launches. Amazon S3 Annotations became generally available, letting businesses attach up to 1 GB of rich, mutable and queryable context directly to their stored objects — purpose-built for AI agents and autonomous workflows.

The Amazon Bedrock AgentCore harness is now generally available, allowing teams to build and run production-grade AI agents without coding orchestration loops themselves. AWS also previewed business context and semantic search for the AWS Glue Data Catalog, plus "skill assets."

Analyst reaction was cautiously positive. Jake Dolezal of McKnight Consulting Group told TechTarget it represents "a meaningful shift from each team building its own [retrieval-augmented generation] pipeline to one governed context layer the whole organization draws from." Others sounded a note of caution: Donald Farmer of TreeHive Strategy flagged that a graph which learns from agent interactions could replicate and distribute errors without human intervention if agents follow incorrect join paths or draw on incorrect data sources.

Strip away the product names and the message is consistent: AI agents are only as good as the data they can reach — and the relationships they can understand. A shared, governed "context layer" is fast becoming the foundation that makes AI useful and safe at scale, rather than a patchwork of one-off integrations per team.

For Australian mid-market businesses, the practical lesson lands close to home. Whether your data lives in a CRM, an accounting platform, a practice management tool or a job-scheduling system, fragmented data is the bottleneck — both for analytics today and for AI agents tomorrow. The analyst caution about replicating errors is also a reminder that a single, well-governed source of truth beats many disconnected ones.

This is exactly the problem Intellova is built to solve: unifying business data from many sources into one clean, governed database — the foundation that makes analytics, AI and automation possible. The big cloud players are validating the direction. The starting point, for any business, is getting your data in order first.