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AWS Summit New York 2026: AWS Focuses on Shared Knowledge, Trusted Agents, and the Future of Enterprise Work
Enhancements across AWS Context, Amazon Quick, AgentCore, Continuum, Kiro, and AWS Transform aim to make knowledge more accessible, enable trusted agentic workflows, and reduce the friction of working across enterprise systems.
06/24/2026
Key Highlights
- AWS introduced AWS Context, a service built around a self-learning knowledge graph designed to connect structured data, unstructured information, skills, and knowledge into a shared contextual layer for people and agents.
- Amazon Quick expands beyond search and assistant capabilities with autonomous agents and deeper integration with AWS Context, providing a unified experience for discovering information, understanding relationships, and completing work.
- AWS announced multiple enhancements to AgentCore focused on governance, policy enforcement, observability, optimization, security, and trusted agent deployment.
- New capabilities across Continuum, Kiro, DevOps Agent, and AWS Transform extend AI-assisted security, software development, operations, and modernization workflows.
The News
At AWS Summit New York 2026, AWS focused on knowledge management, agent deployment, software development, security, and modernization. Central to the strategy are AWS Context, a knowledge graph-based service for connecting enterprise information, and Amazon Quick, which uses that contextual understanding to help users discover information, coordinate work, and interact with AI systems. AWS also expanded AgentCore governance, security, optimization, and connectivity while extending AI-assisted workflows across software development, security, and modernization initiatives. For more information, refer to the What’s New with AWS page.
Analyst Take
Organizations typically do not suffer from a lack of information or tools. They struggle to connect them in ways that create value. During the Summit keynote, AWS described the challenge succinctly: "The problem is not a single tool. It's the space between them."
Information is distributed across applications, repositories, business processes, and people. Finding information is only part of the challenge. Understanding how information relates to a customer, project, decision, or business objective is often more difficult than retrieving the information itself. The problem becomes even more pronounced when AI systems are expected to work across those same environments.
AWS Context provides a self-learning knowledge graph designed to connect structured data, unstructured information, skills, and business knowledge. Knowledge graphs are becoming a common component of AI architectures. Knowledge graphs are becoming a common component of AI architectures. AWS's opportunity lies in the intelligence surrounding the graph, the breadth of systems it can connect, and its ability to improve through usage. AWS Context creates a shared representation of enterprise knowledge that can be used consistently across applications, users, and agents.
Amazon Quick provides the clearest example of how AWS intends to apply that foundation. Enterprise workers have historically adapted to software. They learn where information resides, which applications support specific tasks, and how work moves between systems. The burden of connecting those systems has largely fallen on the user. Quick employs the knowledge graph to surface information, relationships, and relevant context through a unified experience. Quick reduces the effort required to locate information, connect systems, and assemble context around a task or decision.
The implications extend beyond individual productivity. Shared knowledge becomes easier to discover. Expertise becomes more accessible across teams, and institutional knowledge becomes less dependent on specific individuals. Applications continue to serve as systems of record and execution while the user experience becomes increasingly organized and personalized around work and outcomes.
Users ultimately want AI systems that can take action. That requirement appears throughout the AgentCore announcements. New releases focusing on governance, observability, and policy control address the requirements of deploying agents in production. AgentCore Harness, Gateway, Policy, Web Search, and optimizations help agents interact with systems safely, consistently, and under organizational control.
The same theme appears across the broader AWS portfolio. Continuum applies AI to threat modeling, vulnerability analysis, and remediation workflows. Kiro extends specification-driven software development. DevOps Agent adds operational and release management capabilities. AWS Transform extends modernization efforts into an ongoing discipline focused on reducing technical debt, updating legacy applications, and improving efficiency over time. Each enhancement applies AI to a specific domain while remaining connected to the larger goal of making enterprise knowledge actionable.
The announcements address concerns that extend well beyond AI. Users struggle to convert information into action when knowledge is fragmented across systems, teams, and processes. AWS is investing in the systems required to connect knowledge, coordinate work, and govern AI-driven actions. Those capabilities become increasingly important as AI systems move beyond isolated tasks and participate in broader business processes.
What Was Announced
AWS introduced AWS Context, a new service built around a self-learning knowledge graph that connects structured data, unstructured information, skills, and organizational knowledge. The service is designed to help users and agents discover relationships and create a shared contextual layer that can be used across applications, workflows, and business processes.
Amazon Quick expands its role as a work assistant through autonomous agents and deeper integration with AWS Context. The enhancements allow Quick to tap into institutional knowledge, maintain awareness of relationships across information sources, and support goal-oriented activities that extend beyond traditional search and chat interactions.
AWS also expanded Amazon Bedrock AgentCore with features focused on policy enforcement, governance, connectivity, optimization, observability, and agent security. AgentCore Harness introduces reusable integration components that simplify interactions between agents and external systems, while Policy, Gateway, Web Search, and optimization capabilities provide governance, security, retrieval, and operational controls. The updates are intended to help deploy agents in production environments while maintaining visibility, control, and consistency across agent interactions with systems and data sources
Additional announcements extended AI-assisted capabilities across security, software development, operations, and modernization. AWS Continuum adds threat modeling, vulnerability analysis, remediation, and validation. Kiro introduces new functionality for specification-driven software development, including support for mobile workflows. AWS DevOps Agent expands release management and production automation by assisting with application deployment, monitoring, troubleshooting, and incident remediation. AWS Transform extends modernization efforts through continuous assessment and AI-assisted transformation of legacy applications, infrastructure, and runtime environments, helping organizations address technical debt while accelerating ongoing modernization initiatives.
Looking Ahead
Product roadmaps are measured in months while enterprise planning cycles are often measured in years. New models, agents, frameworks, and services appear almost weekly, while many organizations are still working through governance, data readiness, security, and skills gaps. The underlying technology continues to evolve even as businesses work to integrate AI into existing processes and operating models.
This environment favors companies that can learn from real-world deployment. Amazon runs one of the largest collections of interconnected businesses in the world and routinely uses its own infrastructure, development platforms, and tools at scale. That experience provides AWS with a continuous stream of feedback that influences product direction, identifies limitations, and exposes issues long before many customers encounter them.
Customers face a different reality. Most teams are trying to improve efficiency, strengthen security, and deliver measurable business results. The HyperFRAME Research Lens (1H 2026) found that only 14% of organizations consider their data architecture ready for AI, while 62% cite complexity as a significant barrier to infrastructure deployment and expansion. The primary constraint is the ability to integrate new capabilities into existing environments while maintaining visibility into cost, governance, and business outcomes.
Partners remain important to enterprise AI adoption. Most organizations must integrate advancements into existing environments shaped by years of technology decisions, data sources, and business processes. Technical expertise, industry knowledge, and production experience remain important components of successful deployments.
The next phase of AI adoption will reward discipline. Organizations have no shortage of technology options. The harder task is deciding where AI creates value, where it introduces complexity, and how those tradeoffs should be managed over time.
Don Gentile | Analyst-in-Residence -- Storage & Data Resiliency
Don Gentile brings three decades of experience turning complex enterprise technologies into clear, differentiated narratives that drive competitive relevance and market leadership. He has helped shape iconic infrastructure platforms including IBM z16 and z17 mainframes, HPE ProLiant servers, and HPE GreenLake — guiding strategies that connect technology innovation with customer needs and fast-moving market dynamics.
His current focus spans flash storage, storage area networking, hyperconverged infrastructure (HCI), software-defined storage (SDS), hybrid cloud storage, Ceph/open source, cyber resiliency, and emerging models for integrating AI workloads across storage and compute. By applying deep knowledge of infrastructure technologies with proven skills in positioning, content strategy, and thought leadership, Don helps vendors sharpen their story, differentiate their offerings, and achieve stronger competitive standing across business, media, and technical audiences.