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AWS Context helps agents navigate a company's data to be more effective at scale

AWS Context is a new service that automatically builds a knowledge graph from your existing data that can be used by your agents. It infers the relationships between your data assets, business rules, and domain knowledge, and makes all of it available to every agent in your organization to help them get to the right answer.

Context is what makes your agent's tenth decision better than its first. With the right context, your agent can see the latest interactions you've had with a customer in your CRM and recommend the best follow up. Without context, the agent is more likely to confidently give you recommendations that are wrong. Businesses have access to all kinds of data, from information stored in databases to Slack messages, documents, and emails. For an agent to make that information useful, it needs to understand how to navigate the information. This means understanding what tables exist, what's stored in different columns, which sources are the most authoritative, and how they relate to each other. When agents have unified context across all this customer data, regardless of where it lives or what form it takes, they can be exponentially more effective in how they do their jobs.

Built on the same knowledge graph technology that powers Amazon Quick, AWS Context is an agentic search layer for an organization's data that you can connect to all your agents. It has built-in governance to ensure agents can only access the information they're supposed to access. With all the metadata from your data sources stored in Iceberg format in S3 Tables, you can build against AWS Context with the tools you already use. No infrastructure to provision. No retrieval pipeline to build.

As agents interact with AWS Context, it learns which sources produce correct results, which paths get used, and which business rules matter, improving over time. Every agent can then improve based on the findings of a single query. For example, a customer support agent triaging an issue with a customer order may need to pull up a customer's purchase history, shipping status, and return eligibility across multiple different sources. The next time a customer support agent faces a similar issue, it will know exactly where to go, reducing time spent searching to get to a resolution faster.