AI & ML on AWS
13 articles from official documentation
Empower Your AI: Amazon WorkSpaces Gives Agents Their Own Desktop
Unlock the potential of AI in your workflows with Amazon WorkSpaces. This new feature allows AI agents to securely access desktop applications, maintaining your security posture while enhancing productivity. Discover how the Model Context Protocol (MCP) enables seamless integration with any agent framework.
- →Utilize AWS IAM for secure authentication of AI agents.
- →Leverage the Model Context Protocol (MCP) for compatibility with various agent frameworks.
Unlocking Productivity with Amazon Quick and OpenAI's Latest Innovations
AWS is pushing the boundaries of productivity with Amazon Quick and its integration with OpenAI models. Discover how Quick can generate polished documents and presentations directly from a chat interface, streamlining your workflow.
- →Leverage Amazon Quick to generate documents and presentations directly from chat.
- →Integrate Amazon Connect's four AI solutions to enhance existing workflows.
Unlocking AI Potential: Key AWS Announcements from 2026
AWS just dropped some game-changing announcements that could redefine how you integrate AI into your workflows. With Amazon Bedrock Managed Agents, you can now deploy OpenAI models like Codex seamlessly. This is a must-read for engineers looking to leverage cutting-edge AI technology.
- →Leverage Amazon Quick to automate repetitive tasks and improve productivity.
- →Integrate Amazon Connect's four AI solutions to enhance customer interactions.
Unlocking the Power of Claude Opus 4.7 and AWS Interconnect
AWS just rolled out Claude Opus 4.7 in Amazon Bedrock, enhancing your AI capabilities with improved performance for coding and professional tasks. Plus, AWS Interconnect is now generally available, offering robust private connectivity options that can transform your cloud architecture.
- →Leverage Claude Opus 4.7 for improved performance in coding and professional tasks.
- →Utilize dynamic capacity allocation in Claude Opus 4.7 to optimize resource usage.
Unlocking the Power of Claude Opus 4.7 in Amazon Bedrock
Claude Opus 4.7 is a game-changer for coding and knowledge work, leveraging Amazon Bedrock's advanced infrastructure. With a 1M token context window, it excels in long-running tasks and complex reasoning. Dive in to see how it can elevate your production workflows.
- →Leverage Claude Opus 4.7 for complex coding tasks with its enhanced agentic coding capabilities.
- →Utilize the 1M token context window for effective long-running tasks and multi-step workflows.
AI-Driven Troubleshooting in AWS Elastic Beanstalk: A Game Changer
Struggling with environment health issues in AWS Elastic Beanstalk? AI Analysis leverages Amazon Bedrock to streamline troubleshooting. Discover how it collects and analyzes data to reduce your mean time to resolution (MTTR).
- →Leverage AI Analysis to automate troubleshooting in Elastic Beanstalk.
- →Collect environment events, health data, and logs for deeper insights.
Unlocking AI Potential: Claude Mythos and AWS Agent Registry
AWS is pushing the boundaries of AI with the preview of Claude Mythos on Amazon Bedrock. This sophisticated model is part of a broader strategy that includes the AWS Agent Registry, a powerful tool for managing AI resources.
- →Explore Claude Mythos for advanced AI capabilities in your applications.
- →Utilize AWS Agent Registry to manage AI agents and resources efficiently.
Unlocking AWS Innovations: NVIDIA Nemotron 3 Super and More
AWS continues to push the envelope with new features that can transform your workflows. The NVIDIA Nemotron 3 Super model is now available on Amazon Bedrock, optimized for complex tasks. Dive into the latest updates that can enhance your cloud experience.
- →Utilize the NVIDIA Nemotron 3 Super for advanced text generation and reasoning tasks.
- →Leverage Amazon Redshift's 7x performance boost for faster ETL workloads.
AWS Weekly Roundup: Key Updates You Can't Ignore
Stay ahead of the curve with the latest AWS updates that can transform your cloud strategy. Amazon Connect Health now offers five AI agents tailored for healthcare, enhancing patient interactions significantly. Dive in to discover how these changes can impact your production environment.
- →Leverage Amazon Connect Health's AI agents for improved patient interactions.
- →Utilize centralized controls in Amazon Bedrock AgentCore for enhanced security.
Deploying OpenClaw on Amazon Lightsail: Your Private AI Agent Awaits
OpenClaw offers a self-hosted solution for running autonomous AI agents right on your infrastructure. With a simple setup on Amazon Lightsail, you can create a powerful personal assistant while maintaining control over your data.
- →Create an OpenClaw instance on Amazon Lightsail by selecting the 4 GB memory plan for optimal performance.
- →Pair your browser with OpenClaw using the SSH terminal to establish a secure connection.
Unlocking Scalable Code Modernization with AWS Transform Custom
Struggling with technical debt? AWS Transform custom leverages agentic AI to modernize your codebase at scale. With AWS Batch, you can run transformations on thousands of repositories in parallel, streamlining your modernization efforts.
- →Utilize AWS Batch to run transformations on thousands of repositories in parallel.
- →Configure up to 128 concurrent jobs with 2 vCPUs per job for optimal performance.
Revolutionize Your Java Codebase with AWS Transform Custom
Tired of tech debt slowing your Java projects? AWS Transform Custom leverages AI to automate code modernization, handling everything from API migrations to framework updates. Discover how it can streamline your development process and improve code quality.
- →Leverage agentic AI to automate large-scale code modernization.
- →Utilize AWS-managed transformations for common use cases without setup.
From Prototype to Production: Building the AWS DevOps Agent
Transforming an AI agent prototype into a reliable product is no small feat. The AWS DevOps Agent employs a multi-agent architecture, where a lead agent orchestrates tasks among specialized sub-agents. This article dives into the lessons learned during its development.
- →Implement evaluations (evals) to measure agent performance against success criteria.
- →Utilize fast feedback loops to quickly iterate on failing scenarios.
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