Artificial Intelligence

Category: Learning Levels

Improve contract search accuracy with auto-generated filters in Amazon Bedrock

In this post, we describe how AIDA works at a high level and how it helps address these challenges — grounding users in the right contracts, under the right legal context, and within the right access boundaries. Specifically, we explore how AIDA uses implicit and explicit filtering, along with metadata-enriched chunking in Amazon Bedrock Knowledge Bases, to dramatically improve contract search accuracy.

NVIDIA Nemotron 3.5 Lightning now available in Amazon SageMaker JumpStart

NVIDIA Nemotron 3.5 Lightning now available in Amazon SageMaker JumpStart

NVIDIA Nemotron 3.5 Lightning, an open model built for high-volume agentic workloads, is now available in Amazon SageMaker JumpStart. This post shows how to deploy the 30B Mixture-of-Experts model (3B active), which delivers up to 4x higher throughput and up to 30% faster task completion for always-on agents.

Build OpenClaw agents that transact with Amazon Bedrock AgentCore payments

Build OpenClaw agents that transact with Amazon Bedrock AgentCore payments

Give an autonomous agent a wallet and spending guardrails so it can pay for paywalled APIs, MCP servers, and web content. This post connects OpenClaw to Amazon Bedrock AgentCore payments and the x402 protocol, using the aws-agents-pay plugin to make bounded, human-approved testnet payments.

Custom reward functions for multi-turn reinforcement learning with Amazon Nova Forge

In multi-turn reinforcement learning, your custom reward function decides what the model actually learns. This post shows how to design a composite multi-turn reward for Amazon Nova Forge, execute model-generated code safely inside it, and instrument each component to catch the pitfalls that quietly collapse a reward.

Building agentic workflows with SageMaker AI and Bedrock AgentCore

Building agentic workflows with SageMaker AI and Bedrock AgentCore

Learn how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime to build a multi-agent workflow where each specialized agent uses the model best suited to its job. This post also shows how to get token-level observability from SageMaker endpoints that Strands Agents does not instrument by default.

Monitor on-premises and multi-cloud AI agents with AgentCore Observability

Monitor on-premises and multi-cloud AI agents with AgentCore Observability

Set up Amazon Bedrock AgentCore Observability for AI agents running outside AWS: on-premises, on GCP, on Azure, or on developer machines. This walkthrough uses the AWS Distro for OpenTelemetry (ADOT) and IAM credentials to route session traces, span metrics, and token usage to the same AgentCore Observability dashboard.

Automate legacy web applications with Amazon Bedrock AgentCore Browser Tool

Automate legacy web applications with Amazon Bedrock AgentCore Browser Tool

Learn how to automate legacy web applications that need human-like interaction using Amazon Bedrock AgentCore Browser Tool and Strands Agents. This walkthrough covers a reference architecture for an AI-powered digital worker that drives legacy interfaces through secure, isolated browser sessions while preserving human oversight and full audit trails.

Amazon Quick for Microsoft 365: Agentic AI where you work

Amazon Quick for Microsoft 365: Agentic AI where you work

Amazon Quick is now available directly inside Microsoft Word, Excel, PowerPoint, and Outlook. These extensions bring connected data access and agentic document editing into the Microsoft 365 apps your teams already use, so you can analyze data, draft content, and reach enterprise knowledge without switching applications.