Agentic AI Development with Strands Framework

Complete Guide to Amazon Q - Agentic AI Assistant

https://aws.amazon.com/q/

Developing Custom AI-ML models with Amazon Sagemaker

Modernizing Legacy Enterprise Apps with AWS Serverless

Listen this one hour podcast to get an foundational understanding of Networking Services provided by AWS that helps with Modernizing legacy apps and for new application development.

With the Oracle Database @ AWS officially announced recently, AWS provides the optimal platform for deploWith the Oracle Database @ AWS officially announced recently, AWS provides the optimal platform for deploying Oracle 23ai with full AI Vector Search capabilities. The architecture leverages Oracle’s Exadata infrastructure deployed directly within AWS data centers, providing dedicated hardware optimized for database and AI workloads, while maintaining seamless integration with AWS Services.

https://builder.aws.com/content/31MnN0Gw4qSWEZy9KVapw6kfoSS/oracle-ai-vector-search-architecture-on-aws

I’ve been attending Oracle Yatra in July 2025 in Bengaluru, and AI and Multicloud are core concepts discussed, along with Oracle Database concepts. It’s interesting to see how Oracle is encouraging a multicloud strategy and AI capabilities within the Oracle ecosystem. In this article, we will discuss an overview of the Oracle Multicloud Strategy through Oracle @ AWS.

https://builder.aws.com/content/31MWAULLazXegTJlsNVw8q36FLM/oracle-multicloud-a-new-era-with-oracle-databaseaws

If you've been to any tech conferences lately, especially around generative AI and cloud, you've likely heard this buzzword. Today, we're breaking down what MCP servers are, why they're crucial for advanced AI applications, and how you can confidently deploy and even build them, particularly within an enterprise AWS ecosystem.

This episode assumes you have a foundational understanding of LLMs and cloud architecture, and while our examples lean into AWS and .NET, the concepts are broadly applicable. So, let's jump right in!

1. Introduction to MCP Servers: We'll start by setting the stage, explaining what MCP servers are and why they've become so relevant in the world of AI, especially as of mid-2025.

2. The Core Idea of MCP Protocol: We'll break down the fundamental concept behind the MCP protocol, which standardizes how Large Language Models (LLMs) interact with external tools and data, freeing LLMs to focus on intelligence.

3. Anatomy of an MCP Application: We'll look at the higher-level components of an application using an MCP server, including the application itself, the MCP client, and the server's key elements: resources, actions, and prompts.

4. Tackling Enterprise Integration Challenges: A critical discussion on how authentication has evolved for MCP servers, from basic keys to robust OAuth 2.1 and Resource Servers, enabling secure enterprise integration with identity providers like Amazon Cognito.

5. AWS's Blueprint for Enterprise MCP Deployments: We'll walk through AWS's recommended architecture for securely deploying MCP servers, covering everything from CloudFront and AWS WAF to ALBs, authentication services, and serverless compute options like Fargate and Lambda.

6. The Next Evolution: MCP with Amazon Bedrock AgentCore: Discover how AWS Bedrock AgentCore further streamlines agent development and MCP server management, offering specialized runtimes, gateways, and built-in identity and observability.

7. Real-World MCP Server Examples: We'll highlight the growing popularity of MCP servers by examining a practical use case: the AWS Documentation MCP Server, and how it empowers AI assistants with real-time, accurate knowledge.

8. Building Your Own MCP Server in .NET (High-Level): For our developer listeners, we'll provide a concise, step-by-step guide on how to get started building your own MCP server using .NET, from setting up your web app to defining your AI's capabilities.