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AI-native software engineering may be closer than developers think

CIO

Developers unimpressed by the early returns of generative AI for coding take note: Software development is headed toward a new era, when most code will be written by AI agents and reviewed by experienced developers, Gartner predicts. That’s what we call an AI software engineering agent.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

The emergence of generative AI has ushered in a new era of possibilities, enabling the creation of human-like text, images, code, and more. The solution we explore consists of two main components: a Python application for the UI and an AWS deployment architecture for hosting and serving the application securely.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

Were excited to announce the open source release of AWS MCP Servers for code assistants a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. This post is the first in a series covering AWS MCP Servers.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning - AI

With the QnABot on AWS (QnABot), integrated with Microsoft Azure Entra ID access controls, Principal launched an intelligent self-service solution rooted in generative AI. Principal needed a solution that could be rapidly deployed without extensive custom coding.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. For example, consider a text summarization AI assistant intended for academic research and literature review. Such queries could be effectively handled by a simple, lower-cost model.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

While organizations continue to discover the powerful applications of generative AI , adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle.