Category: Intermediate (200)

In this post, we address several key risks that surface when designing an agentic payment system, and how to address them with the capabilities of AgentCore payments.

In this post, we walk through a practical implementation using KDB-X MCP server integration with Amazon Quick, demonstrating how traders and analysts can ask questions using conversational language and receive actionable insights from datasets. You can apply this same integration pattern across various domains, from financial market analysis to IoT sensor monitoring to DevOps performance dashboards, where you need to simplify access to time series insights.

This post combines learnings from LangChain’s work on evaluating deep agents and Anthropic’s guide to demystifying evals for AI agents into a practical guide. In this post, you will learn how to: 1) apply five evaluation patterns for deep agents, 2) build offline evaluations using pytest and LangSmith, and 3) configure online monitoring for production. The walkthrough uses a text-to-SQL deep agent with Amazon Bedrock for the full development to production lifecycle.

In this post, we share how the AWS Generative AI Innovation Center (GenAIIC) collaborated with Works Human Intelligence (WHI) to build two AI agents using Amazon Bedrock AgentCore. We discuss the challenges encountered and the solutions that reduced costs by up to 97% while improving operational efficiency.

This solution combines the power of Amazon Bedrock AgentCore, Strands Agents, and Amazon Quick transforms to deliver a secure, scalable, and intelligent system for building and operating AI agents while transforming data into actionable business insights.

Today, Amazon SageMaker AI introduces OpenAI-compatible API support for real-time inference endpoints. If you use the OpenAI SDK, LangChain, or Strands Agents, you can now invoke models on SageMaker AI by changing only your endpoint URL. You don’t need a custom client, a SigV4 wrapper, or code rewrites. Overview With this launch, SageMaker AI endpoints […]

Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance and Iceberg table properties in the SageMaker Python SDK repository.

In this post, you learn how to prompt Amazon Nova 2 Lite for content moderation using structured and free-form approaches, grounded in the MLCommons AILuminate Assessment Standard. The prompting techniques use the AILuminate taxonomy as an example, but they work equally well with your own custom moderation policy. You can swap in your own category definitions and the prompt structure stays the same. We also benchmark the content moderation capabilities of Amazon Nova 2 Lite against several foundation models (FMs) on three public datasets.

In this post, we walk through how to configure document-level ACLs for your S3 knowledge base in Amazon Quick. You will learn how to set up and verify an ACL configuration that enforces document-level permissions across chat and automated workflows.

Building end-to-end live streaming applications with real-time voice interaction presents several challenges. This post introduces a solution based on Amazon Nova 2 Sonic (Nova Sonic) and Amazon Kinesis Video Streams WebRTC (WebRTC) that addresses these challenges. In this post, we’ll walk through the solution architecture, implementation patterns, and two real-world scenario examples.