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Articles by Jagadeesh Chitikesi
Modernize ASP.NET Apps with Bedrock AgentCore Gateway and Strands Agents
Organizations running ASP.NET applications face a common dilemma: adding AI capabilities typically means significant refactoring or building entirely new systems. Your existing REST APIs contain years of valuable business logic, but there’s no straightforward path to make them AI-accessible without a rewrite. What if you could keep your code and add intelligence on top?
Reimagining Windows Forms Applications with Kiro and Blazor
In this post, we will show how we used Kiro, an AI-powered IDE (Integrated Development Environment) from AWS, to simplify the process of transforming a Windows Forms application into a modern ASP.NET Core Blazor web application. Windows Forms has been a cornerstone of .NET desktop development since 2002, making it easy to build graphically rich Windows applications using the features in Visual Studio.
Integrating C# Model Context Protocol servers with Amazon Q Developer CLI | Amazon Web Services
This post shows how to create your own Model Context Protocol (MCP) server with C# and .NET. You will use Amazon Q Developer Command-Line Interface (Amazon Q CLI) as an MCP client. The Model Context Protocol (MCP) is an open standard that lets Artificial Intelligence (AI) models safely interact with external tools and resources.
Announcing the general availability of AWS .NET OpenTelemetry libraries | Amazon Web Services
Observability has become a crucial aspect of modern software development, enabling developers to gain insights into the behavior and performance of their applications. The OpenTelemetry project, a Cloud Native Computing Foundation (CNCF) hosted project, has emerged as a powerful solution for generating, collecting, and exporting telemetry data, providing a vendor-neutral and standardized approach to observability.
Add Retrieval Augmented Generation (RAG) to your .NET applications with Amazon Bedrock Knowledge Bases | Amazon Web Services
.NET on AWS Blog Introduction When interacting with a large language model (LLM), its knowledge comes from data used during its training, which is often mostly from public sources. As a result, the model’s knowledge may not fully reflect the most current information. This leads to three main issues: outdated information, no access to internal company data, and potential inaccuracies. However, an approach called Retrieval Augmented Generation (RAG) addresses these issues.
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