Artificial Intelligence
Category: Generative AI
Building an AI-driven course content generation system using Amazon Bedrock
In this post, we explore each component in detail, along with the technical implementation of the two core modules: course outline generation and course content generation.
Observing and evaluating AI agentic workflows with Strands Agents SDK and Arize AX
In this post, we present how the Arize AX service can trace and evaluate AI agent tasks initiated through Strands Agents, helping validate the correctness and trustworthiness of agentic workflows.
Building AIOps with Amazon Q Developer CLI and MCP Server
In this post, we discuss how to implement a low-code no-code AIOps solution that helps organizations monitor, identify, and troubleshoot operational events while maintaining their security posture. We show how these technologies work together to automate repetitive tasks, streamline incident response, and enhance operational efficiency across your organization.
Containerize legacy Spring Boot application using Amazon Q Developer CLI and MCP server
In this post, you’ll learn how you can use Amazon Q Developer command line interface (CLI) with Model Context Protocol (MCP) servers integration to modernize a legacy Java Spring Boot application running on premises and then migrate it to Amazon Web Services (AWS) by deploying it on Amazon Elastic Kubernetes Service (Amazon EKS).
Strands Agents SDK: A technical deep dive into agent architectures and observability
In this post, we first introduce the Strands Agents SDK and its core features. Then we explore how it integrates with AWS environments for secure, scalable deployments, and how it provides rich observability for production use. Finally, we discuss practical use cases, and present a step-by-step example to illustrate Strands in action.
Build dynamic web research agents with the Strands Agents SDK and Tavily
In this post, we introduce how to combine Strands Agents with Tavily’s purpose-built web intelligence API, to create powerful research agents that excel at complex information gathering tasks while maintaining the security and compliance standards required for enterprise deployment.
Streamline GitHub workflows with generative AI using Amazon Bedrock and MCP
This blog post explores how to create powerful agentic applications using the Amazon Bedrock FMs, LangGraph, and the Model Context Protocol (MCP), with a practical scenario of handling a GitHub workflow of issue analysis, code fixes, and pull request generation.
Generate suspicious transaction report drafts for financial compliance using generative AI
A suspicious transaction report (STR) or suspicious activity report (SAR) is a type of report that a financial organization must submit to a financial regulator if they have reasonable grounds to suspect any financial transaction that has occurred or was attempted during their activities. In this post, we explore a solution that uses FMs available in Amazon Bedrock to create a draft STR.
Fine-tune and deploy Meta Llama 3.2 Vision for generative AI-powered web automation using AWS DLCs, Amazon EKS, and Amazon Bedrock
In this post, we present a complete solution for fine-tuning and deploying the Llama-3.2-11B-Vision-Instruct model for web automation tasks. We demonstrate how to build a secure, scalable, and efficient infrastructure using AWS Deep Learning Containers (DLCs) on Amazon Elastic Kubernetes Service (Amazon EKS).
Build an intelligent eDiscovery solution using Amazon Bedrock Agents
In this post, we demonstrate how to build an intelligent eDiscovery solution using Amazon Bedrock Agents for real-time document analysis. We show how to deploy specialized agents for document classification, contract analysis, email review, and legal document processing, all working together through a multi-agent architecture. We walk through the implementation details, deployment steps, and best practices to create an extensible foundation that organizations can adapt to their specific eDiscovery requirements.