At the AWS Summit in New York, Amazon's cloud division unveiled several services designed to make AI agents production-ready. These include a security service for code vulnerabilities and a knowledge graph that gives agents the business context they need. The announcements centered on two new services, AWS Continuum and AWS Context, both aimed at addressing typical bottlenecks when deploying AI agents in production. Agents lack business context, and security risks can't keep up with the pace of AI-generated code.
AWS Continuum tackles security vulnerabilities in code by covering the full lifecycle of code vulnerabilities, from detection and prioritization to validation and recommended fixes. The service is initially available only to select pilot customers. AWS points to specialized security models like Anthropic's Claude Mythos as the driving force, writing in its security blog that such models can spot vulnerabilities and map out attack paths faster than defenders can respond. Traditional approaches built around data collection, storage, and dashboards weren't designed for that kind of speed, and the backlog of unresolved issues keeps piling up. AWS Continuum automates the security cycle. Risks are identified, ranked by business impact, validated for exploitability, and addressed with specific remediation steps.
AWS Context automatically builds a knowledge graph from existing enterprise data and makes it available to every agent across an organization. A knowledge graph links individual data points into a network of relationships. That lets an agent figure out which table belongs to which customer or which source is authoritative for a specific piece of information. The service derives these relationships from databases, documents, emails, and chat messages, then layers in business rules and domain knowledge. Without this layer, agents would too often give confident but wrong recommendations, AWS argues.
