Artificial Intelligence

Responsible AI governance: How AWS positions customers to align with ISO/IEC 42005:2025

Responsible AI governance: How AWS positions customers to align with ISO/IEC 42005:2025

AWS invests in tools that help customers align with international standards for responsible AI governance. In this post, we explore the AI system impact assessment: what it is, how it improves enterprise-wide risk management, and how ISO/IEC 42005:2025 codifies best practices for conducting and documenting these assessments.

Best practices for Amazon SageMaker HyperPod administration and governance

Best practices for Amazon SageMaker HyperPod administration and governance

Learn how to administer Amazon SageMaker HyperPod through Amazon SageMaker Unified Studio while preserving cluster governance. This post shows platform teams how to design infrastructure boundaries, govern access, allocate shared capacity, and operate HyperPod consistently across the organization, project, cluster, and workload control layers.

Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code Editor environments in a few clicks, without using command-line tools.

Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic

Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic

Add a voice travel concierge to an airline app with Amazon Bedrock AgentCore, Amazon Nova Sonic for real-time speech, and Amazon Bedrock Knowledge Bases for policy answers. Travelers change seats, check delays, and ask policy questions by voice, while the agent reaches your backend through MCP tools and confirms every change before it writes.

New agent skill: Amazon SageMaker optimized generative AI inference for your coding agent

New agent skill: Amazon SageMaker optimized generative AI inference for your coding agent

Amazon SageMaker optimized generative AI inference introduces the aws-ai-ml skill through the Agent Toolkit for AWS, giving coding agents like Kiro, Claude Code, and Codex deep expertise in inference optimization and benchmarking. Describe what you want, and your agent generates executable SageMaker Python SDK v3 code to benchmark, recommend, and compare deployments.

Making Amazon Quick enterprise-ready: Automated, auditable cross-account resource promotion

Making Amazon Quick enterprise-ready: Automated, auditable cross-account resource promotion

Promoting Amazon Quick resources (agents, action connectors, knowledge bases, flows, and spaces) from a development to a production AWS account has been a manual, error-prone chore. This post shows how to automate cross-account promotion with an idempotent, auditable MCP server on Amazon Bedrock AgentCore.

Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases

Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases

Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrieval answers poorly. Run the same query through both paths, read the trace events, and compare what each retrieval path costs.