Artificial Intelligence

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.

Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore

Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore

Multi-agent systems need deeper guarantees than fluent responses: they must select the right tools, respect constraints, and explain their decisions. Learn how to build a Strands-based multi-agent supply chain decisioning system and evaluate it with Amazon Bedrock AgentCore Evaluations using built-in, custom, and explainability evaluators.

Sweep thousands of leases for compliance using Amazon Quick and the Adjudicated Query pattern

Sweep thousands of leases for compliance using Amazon Quick and the Adjudicated Query pattern

The Adjudicated Query pattern pairs the Amazon Quick chat agent with a bounded MCP server over a deterministic rules engine to deliver provably complete, defensible compliance answers. This post walks through the reference architecture and a deployable AWS CDK sample, using lease compliance as the running example.