Tag: SysAdmin

AWS Transform is expanding its capability from the .NET modernization agent to now include the full-stack Windows modernization agent that handles both .NET applications and their associated databases. The new agent automates the transformation of .NET applications and Microsoft SQL Server databases to Amazon Aurora PostgreSQL and deploys them to containers on Amazon ECS or Amazon EC2 Linux. AWS Transform accelerates full-stack Windows modernization by 5x across application and database layers, while reducing operating costs by up to 70%. With AWS Transform, customers can accelerate their full-stack modernization journey through automated discovery, transformation, and deployment. The full-stack Windows modernization agent scans Microsoft SQL Server databases in Amazon EC2 or Amazon RDS instances, and it scans .NET application code from source repositories (GitHub, GitLab, Bitbucket, or Azure Repos) to create customized, editable modernization plans. It automatically transf

AWS Transform for mainframe delivers new data and activity analysis capabilities to extract comprehensive insights to drive the reimagining of mainframe applications. These insights can be combined with business logic extraction to inform decomposition of legacy applications into logical business domains. Together, these form the basis of a comprehensive specification for coding agents like Kiro to reimagine applications into cloud-native architectures. The new capabilities empower organizations to reimagine legacy workloads, providing a comprehensive reverse engineering workflow that includes automated code and data structure analysis, activity analysis, technical documentation generation, business logic extraction, and intelligent code decomposition. Through in-depth data and activity analysis, AWS Transform helps identify application components with high utilization or business value, allowing teams to optimize their modernization efforts and make data-informed architect

Today, AWS announces the general availability of expanded .NET transformation capabilities and an enhanced developer experience in AWS Transform. Customers can now modernize .NET Framework and .NET code to .NET 10 or .NET Standard. New transformation capabilities include UI porting of ASP.NET Web Forms to Blazor on ASP.NET Core and porting Entity Framework ORM code. The new developer experience, available with the AWS Toolkit for Visual Studio 2026 or 2022, is customizable, interactive, and iterative. It includes an editable transformation plan, estimated transformation time, real-time updates during transformation, the ability to repeat transformations with a revised plan, and next steps markdown for easy handoff to AI code companions. With these enhancements, AWS Transform provides a path to modern .NET for more project types, supports the latest releases of .NET and Visual Studio, and gives developers over

AWS Transform for mainframe now offers test planning and automation features to accelerate mainframe modernization projects. New capabilities include automated test plan generation, test data collection scripts, and test case automation scripts, alongside functional test environment tools for continuous delivery and regression testing, helping accelerate and de-risk testing and validation during mainframe modernization projects. The new capabilities address key testing challenges across the modernization lifecycle, reducing the time and effort required for mainframe modernization testing, which typically consumes over 50% of project duration. Automated test plan generation helps teams reduce upfront planning efforts and align on critical functional tests needed to mitigate risk and ensure modernization success, while test data collection scripts accelerate the error-prone, complex process of capturing mainframe data. Test automation scripts then enable scalable execution of

AWS Transform custom is now generally available, accelerating organization-specific code and application modernization at scale using agentic AI. AWS Transform is the first agentic AI service to accelerate the transformation of Windows, mainframe, VMware, and more—reducing technical debt and making your tech stack AI-ready. Technical debt accumulates when organizations maintain legacy systems and outdated code, requiring them to allocate 20-30% of their software development resources to repeatable, cross-codebase transformation tasks that must be performed manually. AWS Transform can automate repeatable transformations of version upgrades, runtime migrations, framework transitions, and language translations at scale, reducing execution time by over 80% in many cases while eliminating the need for specialized automation expertise. The custom transformation agent in AWS Transform provides both pre-built and custom solutions. It includes out-of-the-box transformations for com

AWS Transform adds powerful new agentic AI capabilities to automate VMware migrations to AWS. The migration agent collaborates with migration teams to understand business priorities and intelligently plan and migrate hundreds of applications spanning thousands of servers, significantly reducing manual effort, time, and complexity. The agent can now discover your on-premises environment and prioritize applications for migration using the AWS Transform discovery tool, inventory data from various third-party discovery tools, and unstructured data such as documents, notes, and business rules. It analyzes infrastructure, database, and application details, maps dependencies, and generates migration plans grouped by business and technical priorities such as ownership, department, function, subnet, and operating systems. It generates networks

Amazon Connect now provides businesses with the ability to automatically evaluate the quality of self-service interactions and get aggregated insights to improve customer experience. Managers can define custom criteria to assess the quality of self-service interactions, that can be filled manually or automatically using insights from conversational analytics, and other Connect data. For example, you can automatically assess if the AI agent repeatedly fails to understand the customer, resulting in poor customer sentiment and transfer to a human agent. Managers can review these insights in aggregate and on individual contacts, alongside self-service interaction recordings and transcripts, to identify opportunities to improve AI agent performance. Manually filled evaluations of self-service interactions are available in all regions where Amazon Connect is offer

Amazon Connect is introducing agentic self-service capabilities that enable AI agents to understand, reason, and take action across voice and messaging channels to automate routine and complex customer service tasks. Connect enables you to blend deterministic and agentic experiences, allowing you to deploy these AI agents at scale, reliably and safely. With integration with advanced speech models from Amazon Nova Sonic, voice self-service experiences now deliver more natural and adaptive interactions. Connect's self-service voice AI agents understand not only what customers say but how they say it, adapting voice responses to match customer tone and sentiment while maintaining natural conversational pace across multiple languages and accents. For example, when a customer calls about an order issue, your AI agent can greet them by name, ask clarifying questions, look up their order status, and process a refund, with voice interactions that adapt to the customer's tone and respond exp

Amazon Connect now supports creation of custom metrics, enabling contact center supervisors to analyze tailored performance measurements without requiring technical skills. This feature provides a simple, no-code interface for performing mathematical operations (e.g., addition, subtraction, sum, average) on existing Connect data to build metrics that align with your organization's specific business requirements. Custom metrics are available to use in the dashboards and APIs. With custom metrics, you can track performance in ways that matter most to your business. For example, create average handle time metrics for premium versus standard customer segments, calculate total agent time on outbound calls by product line, or measure queue performance filtered by contact type such as callbacks versus incoming calls. This new feature is available in all AWS regions where Amazon Conn

Amazon Connect now allows you to bring your own Amazon Bedrock Knowledge Bases and supports multiple knowledge bases per AI agent, giving you greater flexibility in how you organize and access knowledge content for your AI agents. You can now connect your existing Bedrock Knowledge Bases directly to Amazon Connect AI agents in just a few clicks, with no additional setup or data duplication required. This allows you to leverage your current data sources and the Amazon Bedrock Knowledge Base connectors, including Adobe Experience Manager, Confluence, SharePoint, and OneDrive, giving you flexibility to use existing content repositories. With support for multiple knowledge bases per AI agent, you can configure AI agents to query multiple sources in parallel for more comprehensive responses. For example, a financial services company can easily connect separate knowledge bases for compliance documentation, product information, and internal policies, enabling AI agents to provide